davanstrien/dataset_cards_with_metadata · Datasets at Hugging Face

datasetId large_stringlengths 6 107	author large_stringlengths 3 34	last_modified large_stringdate 2021-05-20 00:57:22 2025-05-05 16:11:44	downloads int64 0 4.28M	likes int64 0 7.74k	tags large listlengths 1 2.03k	task_categories large listlengths 0 16	createdAt large_stringdate 2022-03-02 23:29:22 2025-05-05 14:04:15	trending_score float64 1 39 ⌀	card large_stringlengths 31 1M
SylvanL/Traditional-Chinese-Medicine-Dataset-SFT	SylvanL	2024-10-26T10:47:40Z	478	70	[ "task_categories:table-question-answering", "language:zh", "license:apache-2.0", "size_categories:1M<n<10M", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "region:us", "medical" ]	[ "table-question-answering" ]	2024-10-02T08:04:26Z	2	--- license: apache-2.0 task_categories: - table-question-answering language: - zh tags: - medical size_categories: - 1B<n<10B --- # 启古纳今，厚德精术 --- ## 数据介绍 ## 非网络来源的高质量中医数据集-指令微调 ## High-Quality Traditional Chinese Medicine Dataset from Non-Internet Sources - SFT/IFT 该数据集经过大量人力和资源的投入精心构建，以共建LLM高质量中文社区为己任。包含约1GB的中医各个领域临床案例、名家典籍、医学百科，名词解释等优质问答内容，涵盖全面，配比均衡。数据集主要由非网络来源的内部数据构成，并99%为简体中文内容，内容质量优异，信息密度可观。该数据集的数据源与SylvanL/Traditional-Chinese-Medicine-Dataset-Pretrain中的内容存在一定关联，但不高度重叠。在二者的构建过程中，存在着一定的循序渐进与互为补充的逻辑. 该数据集可以独立使用，但建议先使用配套的预训练数据集对模型进行继续预训练后，再使用该数据集进行进一步的指令微调。该数据集仅为单轮对话，且只涉及中医知识与通用医疗知识，因此强烈建议使用者在训练时混入一定比例的【中文常识、中文多轮对话数据】来避免灾难性遗忘。（未包含于该数据集内，可选择"BAAI/COIG"与"m-a-p/COIG-CQIA"）并且因为中医领域的特殊性，使用者也可以按照自己的业务需求酌情混入一定比例的【古文/文言文<->现代文翻译数据】以进一步加强模型表现。(已经在下方一并提供) 所有数据集的可是均可被LLamaFactory按照"dataset_info.json"内的信息（需要与数据集文件在同一路径下）直接读取，list:dict->[{"instruction":"...", "input":"...", "output":"..."},...] 任何问题请联系：[email protected] \| 文件名 \| 数据细节 \| 备注 \| 是否有曾公开过 \|----------------------------------\|----------------------------------\|----------------------------------\|----------------------------------\| \| SFT_medicalKnowledge_source1_548404 \| 原始数据包含来自"中国中医药信息查询平台"数据库的17,921条结构化信息，涵盖疾病、症状、医疗美容、药品、中药材、保健品、方剂、药膳食疗、针灸穴位、术语等的百科词条。原始内容全部经过人工校对，质量极高。由"Qwen2.5-14B"进行本地处理QA化成548,404条数据，基于下文【附录四】内的提示词工程。 \| 未进行minHash，且认为没有进行minHash的必要。 \| 网络来源数据，内部处理校对。 \| \| SFT_medicalKnowledge_source2_99334 \| 原始数据包含来自，但不限于ICD-10术语和中医国标所囊括的病症与术语的12,889条相关解释，同时涵盖常见方剂、中药材、针灸配穴等各种中医术语和名词的详细解释。原始内容全部经由人工构建，质量极高。由"deepseek-chat"API进行本地处理QA化成99,334条数据，基于"附录四"内的提示词工程。\| 已进行一次minHash(threshold=0.91596, num_perm=128)，且认为没有进行二次minHash的必要。 \| 内部数据，未曾面世。 \| \| SFT_medicalKnowledge_source3_556540 \| 原始数据包含来自688本中医领域常用教材、著作、典籍的146244条书本内容。从pdf、word、图片、html以及chm等原始文件格式整理出。具体书录详见【SylvanL/Traditional-Chinese-Medicine-Dataset-Pretrain 附录一】。由"DeepSeek-V2-Lite-Chat"进行本地处理QA化成556,540条数据，基于"附录四"内的提示词工程。\| 未进行minHash，且认为没有进行minHash的必要。 \| 网络来源数据，内部处理校对。 \| \| SFT_nlpDiseaseDiagnosed_61486 \| 原始数据包含61,486条来自高水平执业中医医生的临床问诊记录，原内容为自然语言优质医案，包含患者问诊引导、病症、主诉、诊断、方剂、中药等处方笺必备内容。内容完全由人工编辑，质量极高。由规则直接生成，映射关系为：{'instruction': '基于输入的患者医案记录，直接给出你的疾病诊断，无需给出原因。','input': 自然语言的患者病历记录, 'output': 诊断的疾病，具体清单详见【附录表二】} \| 未进行minHash，且认为没有进行minHash的必要。 \| 内部数据，未曾面世。 \| \| SFT_nlpSyndromeDiagnosed_48665 \| 原始数据包含48,665条来自某知名教授的临床问诊记录，原内容为自然语言优质医案，包含患者问诊引导、病症、主诉、诊断、证型、方剂、中药等处方笺必备内容。内容完全由人工编辑，质量极高。由规则直接生成，映射关系为：{'instruction': '基于输入的患者医案记录，直接给出你的证型诊断，无需给出原因。','input': 自然语言的患者病历记录, 'output': 诊断的证型，具体清单详见【附录表一】} \| 未进行minHash，且认为没有进行minHash的必要。 \| 内部数据，未曾面世。 \| \| SFT_structGeneral_310860 \| 原始数据包含从某互联网医院数据库中获取的部分结构化医案（1,592,410条医案），去重处理后剩余310,860条。内容源头由医生线上问诊时由本人/学生点选或键入。内容由规则联表拼接，质量中规中矩。由规则直接生成，映射关系为：{'instruction': '基于输入的患者医案记录，直接给出你认为的【治疗方案】：中药、方剂，和【诊断】：证型。','input': 半结构化的患者病历记录, 'output': 半结构化的通用诊断，具体构成详见【附录表三】} \| 已进行一次minHash(threshold=0.618, num_perm=128)，可酌情进行二次minHash。 \| 内部数据，未曾面世。 \| \| SFT_structPrescription_92896 \|原始数据包含92,896条从某中医院EMR系统中获得的部分归档医案，内容源头由医生线下问诊时由本人/学生点选或键入。内容由规则联表拼接，质量中规中矩。由规则直接生成，映射关系为：{'instruction': '基于输入的患者医案记录，直接给出你认为的方剂中药组成。','input': 半结构化的患者病历记录, 'output': 半结构化的最终开具处方}\| 未进行minHash，且认为没有进行minHash的必要。 \| 内部数据，未曾面世。 \| \| 外部数据集 __SFT_traditionalTrans_1959542_ \| 古文/文言文<->现代文翻译数据，由规则直接生成，映射关系为：{"instruction": "将输入的古文翻译成现代文。", "input": "古文：", "output": "现代文："}、{"instruction": "请为输入的现代文找到其对应的古文原文与出处。", "input": "现代文：", "output": "内容出自：" + "古文原文:"}\| https://github.com/NiuTrans/Classical-Modern \| 网络来源数据，内部处理校对。 \| --- # 附录表一：SFT_nlpSyndromeDiagnosed_48665 证型诊断样本分布情况 \| 证型 \| 样本数量 \| \| --- \| --- \| \| 气虚血瘀证 \| 8303 \| \| 湿热下注证 \| 7843 \| \| 气滞血瘀证 \| 4791 \| \| 风痰瘀阻证 \| 3358 \| \| 肝胃郁热证 \| 2009 \| \| 正虚瘀结证 \| 1993 \| \| 痰湿蕴肺证 \| 1875 \| \| 肝肾亏虚证 \| 1457 \| \| 热毒蕴结证 \| 1292 \| \| 脾肾两虚证 \| 1022 \| \| 气阴两虚证 \| 860 \| \| 湿热瘀阻证 \| 622 \| \| 肾虚证 \| 593 \| \| 肝胃不和证 \| 546 \| \| 风寒袭肺证 \| 526 \| \| 脾胃虚寒证 \| 522 \| \| 湿热蕴结证 \| 520 \| \| 阳虚水泛证 \| 504 \| \| 痰瘀痹阻证 \| 477 \| \| 心脉痹阻证 \| 446 \| \| 风痰上扰证 \| 414 \| \| 风寒外袭证 \| 396 \| \| 风痰入络证 \| 344 \| \| 瘀血阻络证 \| 315 \| \| 痰瘀互结证 \| 309 \| \| 阴虚阳亢证 \| 299 \| \| 痰浊蒙窍证 \| 282 \| \| 气血亏虚证 \| 218 \| \| 肝阳上亢证 \| 209 \| \| 痰湿蒙窍证 \| 208 \| \| 肝肾阴虚证 \| 194 \| \| 痰湿中阻证 \| 185 \| \| 阴虚火旺证 \| 180 \| \| 血瘀痰凝证 \| 175 \| \| 脾胃虚弱证 \| 174 \| \| 肝经湿热证 \| 169 \| \| 正虚毒瘀证 \| 160 \| \| 血热证 \| 156 \| \| 脾胃不和证 \| 144 \| \| 痰热壅肺证 \| 138 \| \| 阴虚血瘀证 \| 130 \| \| 湿热阻络证 \| 125 \| \| 肝郁气滞证 \| 123 \| \| 湿热内蕴证 \| 112 \| \| 心血虚证 \| 110 \| \| 寒凝血瘀证 \| 99 \| \| 脾肾阳虚证 \| 94 \| \| 阳虚血瘀证 \| 93 \| \| 风寒湿痹证 \| 92 \| \| 瘀阻心脉证 \| 91 \| \| 肝郁血瘀证 \| 90 \| \| 肝阴不足证 \| 89 \| \| 痰瘀滞络证 \| 88 \| \| 气虚不摄证 \| 80 \| \| 正虚毒结证 \| 78 \| \| 气滞证 \| 75 \| \| 肝气犯胃证 \| 73 \| \| 心肾不交证 \| 72 \| \| 瘀血阻滞证 \| 71 \| \| 饮食积滞证 \| 70 \| \| 气血不足证 \| 69 \| \| 脾虚湿盛证 \| 68 \| \| 脾胃积热证 \| 68 \| \| 风热犯肺证 \| 66 \| \| 肾虚水泛证 \| 66 \| \| 心肺气虚证 \| 65 \| \| 肺肾气虚证 \| 63 \| \| 痰浊内蕴证 \| 62 \| \| 气虚痰结证 \| 60 \| \| 风寒阻络证 \| 60 \| \| 气血两虚证 \| 54 \| \| 痰湿内阻证 \| 54 \| \| 痰火扰心证 \| 54 \| \| 津亏热结证 \| 53 \| \| 风寒袭表证 \| 52 \| \| 肺卫不固证 \| 48 \| \| 寒湿阻络证 \| 46 \| \| 肝郁脾虚证 \| 45 \| \| 血虚风燥证 \| 43 \| \| 肾气虚证 \| 40 \| \| 阴虚津亏证 \| 37 \| \| 大肠湿热证 \| 37 \| \| 肝胆湿热证 \| 36 \| \| 脾虚湿蕴证 \| 36 \| \| 热毒炽盛证 \| 36 \| \| 气血瘀滞证 \| 34 \| \| 阴虚血热证 \| 33 \| \| 肾虚肝亢证 \| 33 \| \| 脾气虚证 \| 33 \| \| 阴虚风动证 \| 33 \| \| 痰浊中阻证 \| 32 \| \| 风寒湿阻证 \| 30 \| \| 中气下陷证 \| 29 \| \| 风热毒蕴证 \| 28 \| \| 肾虚寒凝证 \| 28 \| \| 风热袭表证 \| 28 \| \| 湿热蕴毒证 \| 27 \| \| 脾胃气虚证 \| 27 \| \| 风湿蕴肤证 \| 27 \| \| 风寒痹阻证 \| 27 \| \| 气虚证 \| 27 \| \| 阳虚寒凝证 \| 26 \| \| 痰湿证 \| 26 \| \| 中气不足证 \| 26 \| \| 气滞痰凝证 \| 25 \| \| 心虚胆怯证 \| 25 \| \| 气郁痰阻证 \| 24 \| \| 痰蒙清窍证 \| 24 \| \| 风热伤络证 \| 23 \| \| 痰蒙神窍证 \| 22 \| \| 风热血热证 \| 22 \| \| 肝胃气滞证 \| 22 \| \| 血虚证 \| 22 \| \| 痰浊上蒙证 \| 21 \| \| 髓海不足证 \| 20 \| \| 脾胃阳虚证 \| 20 \| \| 风湿热证 \| 20 \| \| 热毒蕴肤证 \| 18 \| \| 痰湿阻滞证 \| 18 \| \| 肝肾亏损证 \| 18 \| \| 外伤损络证 \| 18 \| \| 痰热内扰证 \| 18 \| \| 气不摄血证 \| 18 \| \| 瘀滞胞宫证 \| 17 \| \| 肝肾两虚证 \| 17 \| \| 心阳不振证 \| 17 \| \| 肠燥津伤证 \| 16 \| \| 脾肾气虚证 \| 16 \| \| 风热外袭证 \| 16 \| \| 气虚血溢证 \| 14 \| \| 肾不纳气证 \| 14 \| \| 气阴亏虚证 \| 13 \| \| 肺阴虚证 \| 13 \| \| 心脾两虚证 \| 13 \| \| 湿毒蕴结证 \| 12 \| \| 风痰闭窍证 \| 12 \| \| 湿热瘀滞证 \| 12 \| \| 阴虚湿热证 \| 11 \| \| 寒痰阻肺证 \| 11 \| \| 热毒壅结证 \| 11 \| \| 痰气交阻证 \| 11 \| \| 毒邪流窜证 \| 10 \| \| 风痰闭阻证 \| 10 \| \| 脾胃湿热证 \| 10 \| \| 肾阳虚证 \| 9 \| \| 瘀血内阻证 \| 9 \| \| 阳虚痰凝证 \| 9 \| \| 气血失调证 \| 9 \| # 附录表二： SFT_nlpDiseaseDiagnosed_61486 疾病诊断样本分布情况 \| 疾病 \| 样本数量 \| \| --- \| --- \| \| 咳嗽 \| 14468 \| \| 便秘 \| 5395 \| \| 失眠 \| 4036 \| \| 胃炎 \| 3477 \| \| 痛经 \| 3252 \| \| 高血压 \| 2085 \| \| 肝炎 \| 1688 \| \| 冠心病 \| 1418 \| \| 糖尿病 \| 1143 \| \| 肾炎 \| 1072 \| \| 紫癜 \| 886 \| \| 不孕症 \| 814 \| \| 痤疮 \| 704 \| \| 湿疹 \| 612 \| \| 胆囊炎 \| 579 \| \| 肝硬化 \| 561 \| \| 口腔溃疡 \| 555 \| \| 溃疡性结肠炎 \| 535 \| \| 呼吸道感染 \| 492 \| \| 心律失常 \| 488 \| \| 荨麻疹 \| 465 \| \| 鼻炎 \| 450 \| \| 前列腺炎 \| 405 \| \| 类风湿关节炎 \| 398 \| \| 盆腔炎 \| 388 \| \| 银屑病 \| 385 \| \| 子宫肌瘤 \| 379 \| \| 颈椎病 \| 379 \| \| 贫血 \| 374 \| \| 不育 \| 373 \| \| 脂肪肝 \| 329 \| \| 多囊卵巢综合征 \| 323 \| \| 类风湿性关节炎 \| 320 \| \| 更年期综合征 \| 314 \| \| 肾病综合征 \| 311 \| \| 乳腺增生 \| 296 \| \| 乳腺癌 \| 293 \| \| 咽炎 \| 285 \| \| 偏头痛 \| 281 \| \| 肠癌 \| 276 \| \| 糖尿病肾病 \| 276 \| \| 带状疱疹 \| 262 \| \| 抑郁症 \| 260 \| \| 胃癌 \| 253 \| \| 强直性脊柱炎 \| 243 \| \| 红斑狼疮 \| 239 \| \| 卵巢囊肿 \| 201 \| \| 腰椎间盘突出症 \| 191 \| \| 肝癌 \| 191 \| \| 肠易激综合征 \| 189 \| \| 小儿咳嗽 \| 185 \| \| 黄褐斑 \| 185 \| \| 肺炎 \| 184 \| \| IgA肾病 \| 174 \| \| 尿路感染 \| 165 \| \| 胃溃疡 \| 165 \| \| 月经失调 \| 159 \| \| 十二指肠溃疡 \| 149 \| \| 子宫内膜异位症 \| 148 \| \| 痔疮 \| 144 \| \| 肾功能衰竭 \| 143 \| \| 反流性食管炎 \| 142 \| \| 肺结核 \| 138 \| \| 支气管哮喘 \| 128 \| \| 痛风性关节炎 \| 128 \| \| 肺癌 \| 123 \| \| 功能失调性子宫出血 \| 120 \| \| 扁桃体炎 \| 115 \| \| 风湿性关节炎 \| 115 \| \| 骨质疏松 \| 115 \| \| 肾结石 \| 109 \| \| 脑梗塞 \| 108 \| \| 先兆流产 \| 106 \| \| 三叉神经痛 \| 102 \| \| 脑出血 \| 101 \| \| 乳腺炎 \| 99 \| \| 高脂血症 \| 92 \| \| 慢性肾衰竭 \| 92 \| \| 胆结石 \| 91 \| \| 帕金森病 \| 91 \| \| 皮肤瘙痒症 \| 89 \| \| 肺气肿 \| 86 \| \| 功能性消化不良 \| 85 \| \| 阴道炎 \| 83 \| \| 白癜风 \| 81 \| \| 胰腺炎 \| 80 \| \| 创伤 \| 80 \| \| 心绞痛 \| 80 \| \| 坐骨神经痛 \| 78 \| \| 风湿性心脏病 \| 77 \| \| 食管癌 \| 77 \| \| 泌尿系感染 \| 75 \| \| 精神分裂症 \| 75 \| \| 鼻咽癌 \| 74 \| \| 小儿腹泻 \| 71 \| \| 早产 \| 70 \| \| 胆囊结石 \| 70 \| \| 慢性肾功能不全 \| 69 \| \| 鼻窦炎 \| 66 \| \| 卵巢癌 \| 66 \| \| 小儿肺炎 \| 66 \| \| 慢性支气管炎 \| 64 \| \| 支气管扩张 \| 60 \| \| 梅尼埃病 \| 59 \| \| 急性肾小球肾炎 \| 59 \| \| 高尿酸血症 \| 58 \| \| 上消化道出血 \| 58 \| \| 小儿厌食症 \| 58 \| \| 焦虑症 \| 57 \| \| 低血压 \| 57 \| \| 小儿感冒 \| 55 \| \| 面神经炎 \| 55 \| \| 小儿哮喘 \| 54 \| \| 百日咳 \| 54 \| \| 胰腺癌 \| 54 \| \| 夜惊 \| 53 \| \| 肝囊肿 \| 52 \| \| 急性阑尾炎 \| 50 \| \| 白塞病 \| 50 \| \| 输尿管结石 \| 47 \| \| 心肌梗死 \| 45 \| \| 小儿癫痫 \| 44 \| \| 性早熟 \| 44 \| \| 视神经萎缩 \| 43 \| \| 消化性溃疡 \| 43 \| \| 尿毒症 \| 41 \| \| 支气管炎 \| 41 \| \| 附件炎 \| 40 \| \| 胸腔积液 \| 40 \| \| 先天性心脏病 \| 38 \| \| 多发性骨髓瘤 \| 38 \| \| 异位妊娠 \| 37 \| \| 腰肌劳损 \| 34 \| \| 心房颤动 \| 34 \| \| 脂溢性皮炎 \| 32 \| \| 细菌性痢疾 \| 32 \| \| 心功能不全 \| 30 \| \| 婴儿湿疹 \| 29 \| \| 急性白血病 \| 29 \| \| 宫颈炎 \| 27 \| \| 白内障 \| 26 \| \| 流行性腮腺炎 \| 26 \| \| 单纯性甲状腺肿 \| 26 \| \| 经前期综合征 \| 25 \| \| 慢性阻塞性肺疾病 \| 24 \| \| 垂体瘤 \| 24 \| \| 真性红细胞增多症 \| 24 \| \| 早产儿 \| 24 \| \| 排卵期出血 \| 23 \| \| 多发性神经炎 \| 22 \| \| 手足口病 \| 21 \| \| 尿道炎 \| 20 \| \| 慢性心力衰竭 \| 17 \| \| 冠状动脉粥样硬化性心脏病 \| 15 \| \| 肝脓肿 \| 15 \| \| 尿路结石 \| 13 \| \| 牙周炎 \| 13 \| \| 视神经炎 \| 13 \| \| 婴幼儿腹泻 \| 12 \| \| 慢性阻塞性肺病 \| 12 \| \| 急性咽炎 \| 12 \| \| 小儿多动症 \| 11 \| \| 阿尔茨海默病 \| 11 \| \| 呼吸衰竭 \| 10 \| \| 肝性脑病 \| 10 \| \| 二尖瓣狭窄 \| 9 \| \| 代谢综合征 \| 9 \| \| 雷诺综合征 \| 9 \| \| 新生儿黄疸 \| 9 \| \| 骨肿瘤 \| 8 \| \| 慢性肺源性心脏病 \| 8 \| \| 肺性脑病 \| 8 \| \| 小儿疝气 \| 7 \| \| 急性化脓性中耳炎 \| 6 \| \| 新生儿肺炎 \| 6 \| \| 自闭症 \| 6 \| \| 慢性喉炎 \| 6 \| \| 血管性水肿 \| 6 \| \| 血友病 \| 6 \| \| 慢性粒细胞白血病 \| 5 \| \| 唇癌 \| 4 \| \| 缺血性结肠炎 \| 3 \| \| 脊髓灰质炎 \| 3 \| \| 急性呼吸窘迫综合征 \| 3 \| \| 儿童孤独症 \| 3 \| \| 细菌性阴道病 \| 2 \| \| 小儿惊厥 \| 2 \| \| 唇腭裂 \| 2 \| \| 弥散性血管内凝血 \| 2 \| \| 小儿支气管炎 \| 2 \| \| 肥厚型心肌病 \| 2 \| \| 淹溺 \| 1 \| \| 肺大疱 \| 1 \| \| 急性呼吸衰竭 \| 1 \| # 附录表三：SFT_structGeneral_310849 指令跟随模板分布情况 \| 指令跟随模板 \| 样本数量 \| \| --- \| --- \| \| 【治疗方案】：中药、方剂，和【诊断】：证型 \| 105788 \| \| 【治疗方案】：中药、方剂 \| 68781 \| \| 【治疗方案】：中药 \| 45828 \| \| 【治疗方案】：中药，和【诊断】：证型 \| 38434 \| \| 【治疗方案】：中药、方剂，和【诊断】：证型、治法 \| 23229 \| \| 【治疗方案】：中药、方剂，和【诊断】：西医诊断 \| 12187 \| \| 【治疗方案】：中药、方剂，和【诊断】：西医诊断、中医诊断 \| 3715 \| \| 【治疗方案】：中药，和【诊断】：西医诊断 \| 2782 \| \| 【治疗方案】：中药，和【诊断】：证型、治法 \| 1909 \| \| 【诊断】：证型 \| 1839 \| \| 【治疗方案】：中药、方剂，和【诊断】：证型、治法、中医诊断 \| 1467 \| \| 【治疗方案】：中药、方剂，和【诊断】：中医诊断 \| 1376 \| \| 【治疗方案】：中药，和【诊断】：西医诊断、中医诊断 \| 1250 \| \| 【治疗方案】：中药，和【诊断】：中医诊断 \| 508 \| \| 【诊断】：西医诊断 \| 467 \| \| 【治疗方案】：中药，和【诊断】：证型、治法、中医诊断 \| 378 \| \| 【治疗方案】：中药、方剂，和【诊断】：证型、中医诊断 \| 359 \| \| 【治疗方案】：中药，和【诊断】：证型、中医诊断 \| 337 \| \| 【诊断】：证型、治法 \| 47 \| \| 【诊断】：中医诊断 \| 39 \| \| 【诊断】：西医诊断、中医诊断 \| 38 \| \| 【治疗方案】：中药、成药 \| 34 \| \| 【治疗方案】：中药、成药、方剂 \| 13 \| \| 【诊断】：证型、治法、中医诊断 \| 12 \| \| 【治疗方案】：中药、成药，和【诊断】：证型、治法 \| 10 \| \| 【治疗方案】：中药、方剂，和【诊断】：治法 \| 8 \| \| 【治疗方案】：中药、成药，和【诊断】：西医诊断 \| 6 \| \| 【治疗方案】：中药、成药、方剂，和【诊断】：证型、治法 \| 4 \| \| 【治疗方案】：中药、方剂，和【诊断】：治法、中医诊断 \| 3 \| \| 【治疗方案】：中药、成药，和【诊断】：西医诊断、中医诊断 \| 3 \| \| 【治疗方案】：中药、成药、方剂，和【诊断】：西医诊断、中医诊断 \| 2 \| \| 【治疗方案】：中药、成药，和【诊断】：中医诊断 \| 2 \| \| 【诊断】：证型、中医诊断 \| 1 \| \| 【诊断】：治法 \| 1 \| \| 【治疗方案】：中药、成药、方剂，和【诊断】：中医诊断 \| 1 \| \| 【治疗方案】：中药、成药，和【诊断】：证型、治法、中医诊断 \| 1 \| \| 【治疗方案】：中药，和【诊断】：治法 \| 1 \| # 附录四：prompt-template ``` 模块目标：通过改写和优化现有的医疗问答训练数据，以面向下游任务构建指令跟随微调训练集。输入：单个问答对，格式为单个dict，JSON格式形如：{问题: "...", 答案: "..."} 输出：多个问答对，数量为N个，格式为由N个dict的list，JSON格式形如：[{"问题": "...", "答案": "..."}, {"问题": "...", "答案": "..."}, {"问题": "...", "答案": "..."}, ...] 模块功能： { 1. 简单输入处理：对于简单的答案，重点在于优化答案的质量，确保自然语言流畅性。并围绕相似的答案生成不同的问题变体。输出数量：N=2。 2. 复杂输入处理：对于复杂的答案，重点在于合理拆解答案内容，将长答案分解为多个部分。并针对每个不同的部分生成与其配套的问题。输出数量：N=4。 3. 格式要求：确保输出为合法的JSON格式，所有key和value均需用双引号包裹。字典用花括号 {}，数组用方括号 []，并确保所有括号正确闭合。 4. 评估标准：一切改写和优化应以提升下游模型在医疗垂直领域的任务上的表现为唯一评估标准。 } ```
bigcode/MultiPL-E-completions	bigcode	2024-10-24T08:10:53Z	20,206	9	[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "code" ]	[]	2023-04-22T14:01:20Z	null	--- pretty_name: MultiPL-E generated programs and execution results dataset_info: features: - name: experiment dtype: string - name: problem dtype: string - name: language dtype: string - name: top_p dtype: float64 - name: max_tokens dtype: int64 - name: prompt dtype: string - name: tests dtype: string - name: stop_tokens sequence: string - name: completions sequence: string - name: programs sequence: string - name: stdouts sequence: string - name: stderrs sequence: string - name: exit_codes sequence: int64 - name: statuses sequence: string - name: timestamps sequence: int64 splits: - name: humaneval.py.bigcode_15b_800m.0.2.reworded num_bytes: 50941974 num_examples: 161 - name: humaneval.py.bigcode_15b_200m.0.2.reworded num_bytes: 57850786 num_examples: 161 - 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split: humaneval.go.starcoderbase_7b.0.2.reworded path: data/humaneval.go.starcoderbase_7b.0.2.reworded-* - split: humaneval.java.codellama_13b_base.0.2.reworded path: data/humaneval.java.codellama_13b_base.0.2.reworded-* - split: humaneval.java.codellama_7b_base.0.2.reworded path: data/humaneval.java.codellama_7b_base.0.2.reworded-* - split: humaneval.java.deepseekcoder_1.3b_base.0.2.reworded path: data/humaneval.java.deepseekcoder_1.3b_base.0.2.reworded-* - split: humaneval.java.deepseekcoder1.5_7b_base.0.2.reworded path: data/humaneval.java.deepseekcoder1.5_7b_base.0.2.reworded-* - split: humaneval.java.stablecode3b.0.2.reworded path: data/humaneval.java.stablecode3b.0.2.reworded-* - split: humaneval.java.StarCoder2_15b_16k.0.2.reworded path: data/humaneval.java.StarCoder2_15b_16k.0.2.reworded-* - split: humaneval.java.starcoder2_3b_long.0.2.reworded path: data/humaneval.java.starcoder2_3b_long.0.2.reworded-* - split: humaneval.java.StarCoder2_7b_16k.0.2.reworded path: data/humaneval.java.StarCoder2_7b_16k.0.2.reworded-* - split: humaneval.jl.codellama_13b_base.0.2.reworded path: data/humaneval.jl.codellama_13b_base.0.2.reworded-* - split: humaneval.jl.codellama_7b_base.0.2.reworded path: data/humaneval.jl.codellama_7b_base.0.2.reworded-* - split: humaneval.jl.deepseekcoder_1.3b_base.0.2.reworded path: data/humaneval.jl.deepseekcoder_1.3b_base.0.2.reworded-* - split: humaneval.jl.deepseekcoder1.5_7b_base.0.2.reworded path: data/humaneval.jl.deepseekcoder1.5_7b_base.0.2.reworded-* - split: humaneval.jl.stablecode3b.0.2.reworded path: data/humaneval.jl.stablecode3b.0.2.reworded-* - split: humaneval.jl.StarCoder2_15b_16k.0.2.reworded path: data/humaneval.jl.StarCoder2_15b_16k.0.2.reworded-* - split: humaneval.jl.starcoder2_3b_long.0.2.reworded path: data/humaneval.jl.starcoder2_3b_long.0.2.reworded-* - split: humaneval.jl.StarCoder2_7b_16k.0.2.reworded path: data/humaneval.jl.StarCoder2_7b_16k.0.2.reworded-* - split: humaneval.js.codellama_13b_base.0.2.reworded path: data/humaneval.js.codellama_13b_base.0.2.reworded-* - split: humaneval.js.codellama_7b_base.0.2.reworded path: data/humaneval.js.codellama_7b_base.0.2.reworded-* - split: humaneval.js.deepseekcoder_1.3b_base.0.2.reworded path: data/humaneval.js.deepseekcoder_1.3b_base.0.2.reworded-* - split: humaneval.js.deepseekcoder1.5_7b_base.0.2.reworded path: data/humaneval.js.deepseekcoder1.5_7b_base.0.2.reworded-* - split: humaneval.js.stablecode3b.0.2.reworded path: data/humaneval.js.stablecode3b.0.2.reworded-* - split: humaneval.js.StarCoder2_15b_16k.0.2.reworded path: data/humaneval.js.StarCoder2_15b_16k.0.2.reworded-* - split: humaneval.js.starcoder2_3b_long.0.2.reworded path: data/humaneval.js.starcoder2_3b_long.0.2.reworded-* - split: humaneval.js.StarCoder2_7b_16k.0.2.reworded path: data/humaneval.js.StarCoder2_7b_16k.0.2.reworded-* - split: humaneval.lua.codellama_13b_base.0.2.reworded path: data/humaneval.lua.codellama_13b_base.0.2.reworded-* - split: humaneval.lua.codellama_7b_base.0.2.reworded path: data/humaneval.lua.codellama_7b_base.0.2.reworded-* - split: humaneval.lua.deepseekcoder_1.3b_base.0.2.reworded path: data/humaneval.lua.deepseekcoder_1.3b_base.0.2.reworded-* - split: humaneval.lua.deepseekcoder1.5_7b_base.0.2.reworded path: data/humaneval.lua.deepseekcoder1.5_7b_base.0.2.reworded-* - split: humaneval.lua.stablecode3b.0.2.reworded path: data/humaneval.lua.stablecode3b.0.2.reworded-* - split: humaneval.lua.StarCoder2_15b_16k.0.2.reworded path: data/humaneval.lua.StarCoder2_15b_16k.0.2.reworded-* - split: humaneval.lua.starcoder2_3b_long.0.2.reworded path: data/humaneval.lua.starcoder2_3b_long.0.2.reworded-* - split: humaneval.lua.StarCoder2_7b_16k.0.2.reworded path: data/humaneval.lua.StarCoder2_7b_16k.0.2.reworded-* - split: humaneval.php.codellama_13b_base.0.2.reworded path: data/humaneval.php.codellama_13b_base.0.2.reworded-* - split: humaneval.php.codellama_7b_base.0.2.reworded path: data/humaneval.php.codellama_7b_base.0.2.reworded-* - split: humaneval.php.deepseekcoder_1.3b_base.0.2.reworded path: data/humaneval.php.deepseekcoder_1.3b_base.0.2.reworded-* - split: humaneval.php.deepseekcoder1.5_7b_base.0.2.reworded path: data/humaneval.php.deepseekcoder1.5_7b_base.0.2.reworded-* - split: humaneval.php.stablecode3b.0.2.reworded path: data/humaneval.php.stablecode3b.0.2.reworded-* - split: humaneval.php.StarCoder2_15b_16k.0.2.reworded path: data/humaneval.php.StarCoder2_15b_16k.0.2.reworded-* - split: humaneval.php.starcoder2_3b_long.0.2.reworded path: data/humaneval.php.starcoder2_3b_long.0.2.reworded-* - split: humaneval.php.StarCoder2_7b_16k.0.2.reworded path: data/humaneval.php.StarCoder2_7b_16k.0.2.reworded-* - split: humaneval.pl.codellama_13b_base.0.2.reworded path: data/humaneval.pl.codellama_13b_base.0.2.reworded-* - split: humaneval.pl.CodeLlama_34b_base.0.2.reworded path: data/humaneval.pl.CodeLlama_34b_base.0.2.reworded-* - split: humaneval.pl.codellama_7b_base.0.2.reworded path: data/humaneval.pl.codellama_7b_base.0.2.reworded-* - split: humaneval.pl.deepseekcoder_1.3b_base.0.2.reworded path: data/humaneval.pl.deepseekcoder_1.3b_base.0.2.reworded-* - split: humaneval.pl.deepseekcoder1.5_7b_base.0.2.reworded path: data/humaneval.pl.deepseekcoder1.5_7b_base.0.2.reworded-* - split: humaneval.pl.DeepSeekCoder_34b_base.0.2.reworded path: data/humaneval.pl.DeepSeekCoder_34b_base.0.2.reworded-* - split: humaneval.pl.stablecode3b.0.2.reworded path: data/humaneval.pl.stablecode3b.0.2.reworded-* - split: humaneval.pl.StarCoder2_15b_16k.0.2.reworded path: data/humaneval.pl.StarCoder2_15b_16k.0.2.reworded-* - split: humaneval.pl.starcoder2_3b_long.0.2.reworded path: data/humaneval.pl.starcoder2_3b_long.0.2.reworded-* - split: humaneval.pl.StarCoder2_7b_16k.0.2.reworded path: data/humaneval.pl.StarCoder2_7b_16k.0.2.reworded-* - split: humaneval.pl.starcoderbase_3b.0.2.reworded path: data/humaneval.pl.starcoderbase_3b.0.2.reworded-* - split: humaneval.pl.starcoderbase_7b.0.2.reworded path: data/humaneval.pl.starcoderbase_7b.0.2.reworded-* - split: humaneval.rb.codellama_13b_base.0.2.reworded path: data/humaneval.rb.codellama_13b_base.0.2.reworded-* - split: humaneval.rb.CodeLlama_34b_base.0.2.reworded path: data/humaneval.rb.CodeLlama_34b_base.0.2.reworded-* - split: humaneval.rb.codellama_7b_base.0.2.reworded path: data/humaneval.rb.codellama_7b_base.0.2.reworded-* - split: humaneval.rb.deepseekcoder_1.3b_base.0.2.reworded path: data/humaneval.rb.deepseekcoder_1.3b_base.0.2.reworded-* - split: humaneval.rb.deepseekcoder1.5_7b_base.0.2.reworded path: data/humaneval.rb.deepseekcoder1.5_7b_base.0.2.reworded-* - split: humaneval.rb.DeepSeekCoder_34b_base.0.2.reworded path: data/humaneval.rb.DeepSeekCoder_34b_base.0.2.reworded-* - split: humaneval.rb.stablecode3b.0.2.reworded path: data/humaneval.rb.stablecode3b.0.2.reworded-* - split: humaneval.rb.StarCoder2_15b_16k.0.2.reworded path: data/humaneval.rb.StarCoder2_15b_16k.0.2.reworded-* - split: humaneval.rb.starcoder2_3b_long.0.2.reworded path: data/humaneval.rb.starcoder2_3b_long.0.2.reworded-* - split: humaneval.rb.StarCoder2_7b_16k.0.2.reworded path: data/humaneval.rb.StarCoder2_7b_16k.0.2.reworded-* - split: humaneval.rb.starcoderbase_3b.0.2.reworded path: data/humaneval.rb.starcoderbase_3b.0.2.reworded-* - split: humaneval.rb.starcoderbase_7b.0.2.reworded path: data/humaneval.rb.starcoderbase_7b.0.2.reworded-* - split: humaneval.r.codellama_13b_base.0.2.reworded path: data/humaneval.r.codellama_13b_base.0.2.reworded-* - split: humaneval.r.codellama_7b_base.0.2.reworded path: data/humaneval.r.codellama_7b_base.0.2.reworded-* - split: humaneval.r.deepseekcoder_1.3b_base.0.2.reworded path: data/humaneval.r.deepseekcoder_1.3b_base.0.2.reworded-* - split: humaneval.r.deepseekcoder1.5_7b_base.0.2.reworded path: data/humaneval.r.deepseekcoder1.5_7b_base.0.2.reworded-* - split: humaneval.r.DeepSeekCoder_34b_base.0.2.reworded path: data/humaneval.r.DeepSeekCoder_34b_base.0.2.reworded-* - split: humaneval.rkt.codellama_13b_base.0.2.reworded path: data/humaneval.rkt.codellama_13b_base.0.2.reworded-* - split: humaneval.rkt.codellama_7b_base.0.2.reworded path: data/humaneval.rkt.codellama_7b_base.0.2.reworded-* - split: humaneval.rkt.deepseekcoder_1.3b_base.0.2.reworded path: data/humaneval.rkt.deepseekcoder_1.3b_base.0.2.reworded-* - split: humaneval.rkt.deepseekcoder1.5_7b_base.0.2.reworded path: data/humaneval.rkt.deepseekcoder1.5_7b_base.0.2.reworded-* - split: humaneval.rkt.stablecode3b.0.2.reworded path: data/humaneval.rkt.stablecode3b.0.2.reworded-* - split: humaneval.rkt.StarCoder2_15b_16k.0.2.reworded path: data/humaneval.rkt.StarCoder2_15b_16k.0.2.reworded-* - split: humaneval.rkt.starcoder2_3b_long.0.2.reworded path: data/humaneval.rkt.starcoder2_3b_long.0.2.reworded-* - split: humaneval.rkt.StarCoder2_7b_16k.0.2.reworded path: data/humaneval.rkt.StarCoder2_7b_16k.0.2.reworded-* - split: humaneval.rs.codellama_13b_base.0.2.reworded path: data/humaneval.rs.codellama_13b_base.0.2.reworded-* - split: humaneval.rs.codellama_7b_base.0.2.reworded path: data/humaneval.rs.codellama_7b_base.0.2.reworded-* - split: humaneval.rs.deepseekcoder_1.3b_base.0.2.reworded path: data/humaneval.rs.deepseekcoder_1.3b_base.0.2.reworded-* - split: humaneval.rs.deepseekcoder1.5_7b_base.0.2.reworded path: data/humaneval.rs.deepseekcoder1.5_7b_base.0.2.reworded-* - split: humaneval.rs.stablecode3b.0.2.reworded path: data/humaneval.rs.stablecode3b.0.2.reworded-* - split: humaneval.rs.StarCoder2_15b_16k.0.2.reworded path: data/humaneval.rs.StarCoder2_15b_16k.0.2.reworded-* - split: humaneval.rs.starcoder2_3b_long.0.2.reworded path: data/humaneval.rs.starcoder2_3b_long.0.2.reworded-* - split: humaneval.rs.StarCoder2_7b_16k.0.2.reworded path: data/humaneval.rs.StarCoder2_7b_16k.0.2.reworded-* - split: humaneval.r.stablecode3b.0.2.reworded path: data/humaneval.r.stablecode3b.0.2.reworded-* - split: humaneval.r.StarCoder2_15b_16k.0.2.reworded path: data/humaneval.r.StarCoder2_15b_16k.0.2.reworded-* - split: humaneval.r.starcoder2_3b_long.0.2.reworded path: data/humaneval.r.starcoder2_3b_long.0.2.reworded-* --- # Raw Data from MultiPL-E This repository is frozen. See https://huggingface.co/datasets/nuprl/MultiPL-E-completions for a more complete version of this repository. Uploads are a work in progress. If you are interested in a split that is not yet available, please contact [email protected]. This repository contains the raw data -- both completions and executions -- from MultiPL-E that was used to generate several experimental results from the MultiPL-E, SantaCoder, and StarCoder papers. The original MultiPL-E completions and executions are stored in JOSN files. We use [the following script](https://github.com/nuprl/MultiPL-E/blob/main/upload_completions.py-) to turn each experiment directory into a dataset split and upload to this repository. Every split is named `base_dataset`.`language`.`model`.`temperature`.`variation` - `base_dataset` is either `humaneval` or `mbpp`. - `language` is the file extension of the programming language. E.g., `py` for Python or `sh` for Bash. - `model` is the name of the model. Some model names used by MultiPL-E: - `bigcode_15b_1000m`: StarCoderBase - `bigcode_15b_200m`, `bigcode_15b_400m`, `bigcode_15b_600m`, `bigcode_15b_800m`: the 20%, 40%, 60%, 80% checkpoints for StarCoderBase - `pystarcoder2`: StarCoder - there are others, just have a look :) - `temperature`: this is either 0.2 or 0.8 - `variation`: the variation of the MultiPL-E prompt to use, which should be `reworded`. MultiPL-E has several prompt ablations and the `reworded` prompts are the canonical variation.
lmms-lab/LLaVA-OneVision-Data	lmms-lab	2024-10-22T06:47:46Z	18,720	184	[ "language:en", "language:zh", "license:apache-2.0", "size_categories:1M<n<10M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2408.03326", "arxiv:2310.05126", "region:us" ]	[]	2024-07-25T15:25:28Z	null	--- language: - en - zh license: apache-2.0 pretty_name: llava-onevision-data dataset_info: - config_name: CLEVR-Math(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 791346970 num_examples: 5280 download_size: 441208499 dataset_size: 791346970 - config_name: FigureQA(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 463326576.625 num_examples: 17587 download_size: 258197193 dataset_size: 463326576.625 - config_name: GEOS(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1503641 num_examples: 498 download_size: 684471 dataset_size: 1503641 - config_name: GeoQA+(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 53579705.75 num_examples: 17162 download_size: 33480538 dataset_size: 53579705.75 - config_name: Geometry3K(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 218085473.5 num_examples: 9724 download_size: 125914780 dataset_size: 218085473.5 - config_name: IconQA(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 208430568.375 num_examples: 22589 download_size: 117222488 dataset_size: 208430568.375 - config_name: MapQA(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 384120915.875 num_examples: 5225 download_size: 215768443 dataset_size: 384120915.875 - config_name: PMC-VQA(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 571444866.5 num_examples: 35948 download_size: 326541003 dataset_size: 571444866.5 - config_name: Super-CLEVR(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2795082410.75 num_examples: 8642 download_size: 1580301917 dataset_size: 2795082410.75 - config_name: TabMWP(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 307726997.5 num_examples: 22452 download_size: 173938487 dataset_size: 307726997.5 - config_name: UniGeo(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 38296693.375 num_examples: 11949 download_size: 24170743 dataset_size: 38296693.375 - config_name: VisualWebInstruct(filtered) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 36317112275.0 num_examples: 263584 download_size: 36239916454 dataset_size: 36317112275.0 - config_name: VizWiz(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1170333936.5 num_examples: 6604 download_size: 660752297 dataset_size: 1170333936.5 - config_name: ai2d(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 438572782.375 num_examples: 2429 download_size: 437348514 dataset_size: 438572782.375 - config_name: ai2d(gpt4v) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 866076731 num_examples: 4864 download_size: 860306578 dataset_size: 866076731 - config_name: ai2d(internvl) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1832787249.625 num_examples: 12403 download_size: 527493895 dataset_size: 1832787249.625 - config_name: allava_instruct_laion4v features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 5981767621.25 num_examples: 49990 download_size: 5873046236 dataset_size: 5981767621.25 - config_name: allava_instruct_vflan4v features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2680974558.25 num_examples: 19990 download_size: 2670088751 dataset_size: 2680974558.25 - config_name: aokvqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 6896420844.25 num_examples: 16534 download_size: 6894236970 dataset_size: 6896420844.25 - config_name: chart2text(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1145458729.5 num_examples: 26956 download_size: 1123681047 dataset_size: 1145458729.5 - config_name: chartqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 815335215.5 num_examples: 18260 download_size: 803084541 dataset_size: 815335215.5 - config_name: chrome_writting features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 44422597.875 num_examples: 8825 download_size: 39611257 dataset_size: 44422597.875 - config_name: clevr(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 10528974543.625 num_examples: 69995 download_size: 10460536445 dataset_size: 10528974543.625 - config_name: diagram_image_to_text(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 18858266 num_examples: 295 download_size: 18659115 dataset_size: 18858266 - config_name: dvqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 4487270615.625 num_examples: 199995 download_size: 4277056467 dataset_size: 4487270615.625 - config_name: figureqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2351194509.625 num_examples: 99995 download_size: 2222640639 dataset_size: 2351194509.625 - config_name: geo170k(align) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 204236256.75 num_examples: 60242 download_size: 58185410 dataset_size: 204236256.75 - config_name: geo170k(qa) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 266040519.125 num_examples: 67823 download_size: 160022430 dataset_size: 266040519.125 - config_name: geo3k features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 42634333.625 num_examples: 2091 download_size: 41097851 dataset_size: 42634333.625 - config_name: geomverse(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2263893609.75 num_examples: 9298 download_size: 2211726352 dataset_size: 2263893609.75 - config_name: hateful_memes(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 3057252325.125 num_examples: 8495 download_size: 3055839880 dataset_size: 3057252325.125 - config_name: hitab(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 161706881.125 num_examples: 2495 download_size: 157871287 dataset_size: 161706881.125 - config_name: hme100k features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 273229915.5 num_examples: 74492 download_size: 241005430 dataset_size: 273229915.5 - config_name: iam(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1131633206.75 num_examples: 5658 download_size: 1128371221 dataset_size: 1131633206.75 - config_name: iconqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 331284932.25 num_examples: 27302 download_size: 327005220 dataset_size: 331284932.25 - config_name: iiit5k features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 21821437.25 num_examples: 1990 download_size: 21623116 dataset_size: 21821437.25 - config_name: image_textualization(filtered) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 5218283253.375 num_examples: 99573 download_size: 5164176816 dataset_size: 5218283253.375 - config_name: infographic(gpt4v) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 713657496.25 num_examples: 1982 download_size: 656276080 dataset_size: 713657496.25 - config_name: infographic_vqa features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1528953078.75 num_examples: 4394 download_size: 1419340319 dataset_size: 1528953078.75 - config_name: infographic_vqa_llava_format features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1765315696.875 num_examples: 2113 download_size: 1764548536 dataset_size: 1765315696.875 - config_name: intergps(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 24973395.625 num_examples: 1275 download_size: 24736545 dataset_size: 24973395.625 - config_name: k12_printing features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1205153118.5 num_examples: 256636 download_size: 1108572712 dataset_size: 1205153118.5 - config_name: llavar_gpt4_20k features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 633833350.25 num_examples: 19790 download_size: 625365542 dataset_size: 633833350.25 - config_name: lrv_chart features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 99338686 num_examples: 1776 download_size: 97979446 dataset_size: 99338686 - config_name: lrv_normal(filtered) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 422589381.75 num_examples: 10490 download_size: 406958773 dataset_size: 422589381.75 - config_name: magpie_pro(l3_80b_mt) features: - name: id dtype: string - name: image dtype: 'null' - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1657129141 num_examples: 299988 download_size: 885893066 dataset_size: 1657129141 - config_name: magpie_pro(l3_80b_st) features: - name: id dtype: string - name: image dtype: 'null' - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1033666690 num_examples: 299990 download_size: 562771564 dataset_size: 1033666690 - config_name: magpie_pro(qwen2_72b_st) features: - name: id dtype: string - name: image dtype: 'null' - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 703489344 num_examples: 299982 download_size: 361433408 dataset_size: 703489344 - config_name: mapqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 3355751195.5 num_examples: 37412 download_size: 3305639218 dataset_size: 3355751195.5 - config_name: mathqa features: - name: id dtype: string - name: image dtype: 'null' - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 18318538 num_examples: 29827 download_size: 7857130 dataset_size: 18318538 - config_name: mavis_math_metagen features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2304025372.5 num_examples: 87348 download_size: 322776224 dataset_size: 2304025372.5 - config_name: mavis_math_rule_geo features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 14313211512.25 num_examples: 99990 download_size: 5841283073 dataset_size: 14313211512.25 - config_name: multihiertt(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 300319803.25 num_examples: 7614 download_size: 295638314 dataset_size: 300319803.25 - config_name: orand_car_a features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 23602442.125 num_examples: 1999 download_size: 23333412 dataset_size: 23602442.125 - config_name: raven(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1706160514.625 num_examples: 41995 download_size: 1693150088 dataset_size: 1706160514.625 - config_name: rendered_text(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 11082594894.625 num_examples: 9995 download_size: 11081962044 dataset_size: 11082594894.625 - config_name: robut_sqa(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 685580779.375 num_examples: 8509 download_size: 678666263 dataset_size: 685580779.375 - config_name: robut_wikisql(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 6200499653 num_examples: 74984 download_size: 6168399217 dataset_size: 6200499653 - config_name: robut_wtq(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 4091776188.875 num_examples: 38241 download_size: 4062777449 dataset_size: 4091776188.875 - config_name: scienceqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 286843125.625 num_examples: 4971 download_size: 282896809 dataset_size: 286843125.625 - config_name: scienceqa(nona_context) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2111029055 num_examples: 19208 download_size: 2053942726 dataset_size: 2111029055 - config_name: screen2words(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 7977502095.375 num_examples: 15725 download_size: 7962327904 dataset_size: 7977502095.375 - config_name: sharegpt4o features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 6968025789.5 num_examples: 57284 download_size: 6772195470 dataset_size: 6968025789.5 - config_name: sharegpt4v(coco) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2620153362.875 num_examples: 50017 download_size: 2595583499 dataset_size: 2620153362.875 - config_name: sharegpt4v(knowledge) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 372100773.5 num_examples: 1988 download_size: 369799318 dataset_size: 372100773.5 - config_name: sharegpt4v(llava) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 781795487.25 num_examples: 29990 download_size: 400344187 dataset_size: 781795487.25 - config_name: sharegpt4v(sam) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 4437405218.25 num_examples: 8990 download_size: 4428597081 dataset_size: 4437405218.25 - config_name: sroie features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 117810195 num_examples: 33616 download_size: 103647636 dataset_size: 117810195 - config_name: st_vqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 5771194098.75 num_examples: 17242 download_size: 5768888141 dataset_size: 5771194098.75 - config_name: tabmwp(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 311192518.375 num_examples: 22717 download_size: 306092255 dataset_size: 311192518.375 - config_name: tallyqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 35998988065.625 num_examples: 98675 download_size: 35982430394 dataset_size: 35998988065.625 - config_name: textcaps features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2222268476.25 num_examples: 21942 download_size: 2217838132 dataset_size: 2222268476.25 - config_name: textocr(gpt4v) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2581655353 num_examples: 25104 download_size: 2574418106 dataset_size: 2581655353 - config_name: tqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 331203026.25 num_examples: 27302 download_size: 326999466 dataset_size: 331203026.25 - config_name: ureader_cap features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 9269857109.75 num_examples: 91434 download_size: 2292099971 dataset_size: 9269857109.75 - config_name: ureader_ie features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 11871457209.75 num_examples: 17322 download_size: 1999083115 dataset_size: 11871457209.75 - config_name: vision_flan(filtered) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 24847242604.5 num_examples: 186060 download_size: 24750561877 dataset_size: 24847242604.5 - config_name: vistext(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 550187184.5 num_examples: 9964 download_size: 452795103 dataset_size: 550187184.5 - config_name: visual7w(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 4451436523.875 num_examples: 14361 download_size: 4441971985 dataset_size: 4451436523.875 - config_name: visualmrc(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2938154124.25 num_examples: 3022 download_size: 2909296079 dataset_size: 2938154124.25 - config_name: vqarad(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 95533417 num_examples: 308 download_size: 95410398 dataset_size: 95533417 - config_name: vsr(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 891981646 num_examples: 2152 download_size: 891572866 dataset_size: 891981646 - config_name: websight(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 11209715828.625 num_examples: 9995 download_size: 11144460985 dataset_size: 11209715828.625 configs: - config_name: CLEVR-Math(MathV360K) data_files: - split: train path: CLEVR-Math(MathV360K)/train-* - config_name: FigureQA(MathV360K) data_files: - split: train path: FigureQA(MathV360K)/train-* - config_name: GEOS(MathV360K) data_files: - split: train path: GEOS(MathV360K)/train-* - config_name: GeoQA+(MathV360K) data_files: - split: train path: GeoQA+(MathV360K)/train-* - config_name: Geometry3K(MathV360K) data_files: - split: train path: Geometry3K(MathV360K)/train-* - config_name: IconQA(MathV360K) data_files: - split: train path: IconQA(MathV360K)/train-* - config_name: MapQA(MathV360K) data_files: - split: train path: MapQA(MathV360K)/train-* - config_name: PMC-VQA(MathV360K) data_files: - split: train path: PMC-VQA(MathV360K)/train-* - config_name: Super-CLEVR(MathV360K) data_files: - split: train path: Super-CLEVR(MathV360K)/train-* - config_name: TabMWP(MathV360K) data_files: - split: train path: TabMWP(MathV360K)/train-* - config_name: UniGeo(MathV360K) data_files: - split: train path: UniGeo(MathV360K)/train-* - config_name: VisualWebInstruct(filtered) data_files: - split: train path: VisualWebInstruct(filtered)/train-* - config_name: VizWiz(MathV360K) data_files: - split: train path: VizWiz(MathV360K)/train-* - config_name: ai2d(cauldron,llava_format) data_files: - split: train path: ai2d(cauldron,llava_format)/train-* - config_name: ai2d(gpt4v) data_files: - split: train path: ai2d(gpt4v)/train-* - config_name: ai2d(internvl) data_files: - split: train path: ai2d(internvl)/train-* - config_name: allava_instruct_laion4v data_files: - split: train path: allava_instruct_laion4v/train-* - config_name: allava_instruct_vflan4v data_files: - split: train path: allava_instruct_vflan4v/train-* - config_name: aokvqa(cauldron,llava_format) data_files: - split: train path: aokvqa(cauldron,llava_format)/train-* - config_name: chart2text(cauldron) data_files: - split: train path: chart2text(cauldron)/train-* - config_name: chartqa(cauldron,llava_format) data_files: - split: train path: chartqa(cauldron,llava_format)/train-* - config_name: chrome_writting data_files: - split: train path: chrome_writting/train-* - config_name: clevr(cauldron,llava_format) data_files: - split: train path: clevr(cauldron,llava_format)/train-* - config_name: diagram_image_to_text(cauldron) data_files: - split: train path: diagram_image_to_text(cauldron)/train-* - config_name: dvqa(cauldron,llava_format) data_files: - split: train path: dvqa(cauldron,llava_format)/train-* - config_name: figureqa(cauldron,llava_format) data_files: - split: train path: figureqa(cauldron,llava_format)/train-* - config_name: geo170k(align) data_files: - split: train path: geo170k(align)/train-* - config_name: geo170k(qa) data_files: - split: train path: geo170k(qa)/train-* - config_name: geo3k data_files: - split: train path: geo3k/train-* - config_name: geomverse(cauldron) data_files: - split: train path: geomverse(cauldron)/train-* - config_name: hateful_memes(cauldron,llava_format) data_files: - split: train path: hateful_memes(cauldron,llava_format)/train-* - config_name: hitab(cauldron,llava_format) data_files: - split: train path: hitab(cauldron,llava_format)/train-* - config_name: hme100k data_files: - split: train path: hme100k/train-* - config_name: iam(cauldron) data_files: - split: train path: iam(cauldron)/train-* - config_name: iconqa(cauldron,llava_format) data_files: - split: train path: iconqa(cauldron,llava_format)/train-* - config_name: iiit5k data_files: - split: train path: iiit5k/train-* - config_name: image_textualization(filtered) data_files: - split: train path: image_textualization(filtered)/train-* - config_name: infographic(gpt4v) data_files: - split: train path: infographic(gpt4v)/train-* - config_name: infographic_vqa data_files: - split: train path: infographic_vqa/train-* - config_name: infographic_vqa_llava_format data_files: - split: train path: infographic_vqa_llava_format/train-* - config_name: intergps(cauldron,llava_format) data_files: - split: train path: intergps(cauldron,llava_format)/train-* - config_name: k12_printing data_files: - split: train path: k12_printing/train-* - config_name: llavar_gpt4_20k data_files: - split: train path: llavar_gpt4_20k/train-* - config_name: lrv_chart data_files: - split: train path: lrv_chart/train-* - config_name: lrv_normal(filtered) data_files: - split: train path: lrv_normal(filtered)/train-* - config_name: magpie_pro(l3_80b_mt) data_files: - split: train path: magpie_pro(l3_80b_mt)/train-* - config_name: magpie_pro(l3_80b_st) data_files: - split: train path: magpie_pro(l3_80b_st)/train-* - config_name: magpie_pro(qwen2_72b_st) data_files: - split: train path: magpie_pro(qwen2_72b_st)/train-* - config_name: mapqa(cauldron,llava_format) data_files: - split: train path: mapqa(cauldron,llava_format)/train-* - config_name: mathqa data_files: - split: train path: mathqa/train-* - config_name: mavis_math_metagen data_files: - split: train path: mavis_math_metagen/train-* - config_name: mavis_math_rule_geo data_files: - split: train path: mavis_math_rule_geo/train-* - config_name: multihiertt(cauldron) data_files: - split: train path: multihiertt(cauldron)/train-* - config_name: orand_car_a data_files: - split: train path: orand_car_a/train-* - config_name: raven(cauldron) data_files: - split: train path: raven(cauldron)/train-* - config_name: rendered_text(cauldron) data_files: - split: train path: rendered_text(cauldron)/train-* - config_name: robut_sqa(cauldron) data_files: - split: train path: robut_sqa(cauldron)/train-* - config_name: robut_wikisql(cauldron) data_files: - split: train path: robut_wikisql(cauldron)/train-* - config_name: robut_wtq(cauldron,llava_format) data_files: - split: train path: robut_wtq(cauldron,llava_format)/train-* - config_name: scienceqa(cauldron,llava_format) data_files: - split: train path: scienceqa(cauldron,llava_format)/train-* - config_name: scienceqa(nona_context) data_files: - split: train path: scienceqa(nona_context)/train-* - config_name: screen2words(cauldron) data_files: - split: train path: screen2words(cauldron)/train-* - config_name: sharegpt4o data_files: - split: train path: sharegpt4o/train-* - config_name: sharegpt4v(coco) data_files: - split: train path: sharegpt4v(coco)/train-* - config_name: sharegpt4v(knowledge) data_files: - split: train path: sharegpt4v(knowledge)/train-* - config_name: sharegpt4v(llava) data_files: - split: train path: sharegpt4v(llava)/train-* - config_name: sharegpt4v(sam) data_files: - split: train path: sharegpt4v(sam)/train-* - config_name: sroie data_files: - split: train path: sroie/train-* - config_name: st_vqa(cauldron,llava_format) data_files: - split: train path: st_vqa(cauldron,llava_format)/train-* - config_name: tabmwp(cauldron) data_files: - split: train path: tabmwp(cauldron)/train-* - config_name: tallyqa(cauldron,llava_format) data_files: - split: train path: tallyqa(cauldron,llava_format)/train-* - config_name: textcaps data_files: - split: train path: textcaps/train-* - config_name: textocr(gpt4v) data_files: - split: train path: textocr(gpt4v)/train-* - config_name: tqa(cauldron,llava_format) data_files: - split: train path: tqa(cauldron,llava_format)/train-* - config_name: ureader_cap data_files: - split: train path: ureader_cap/train-* - config_name: ureader_ie data_files: - split: train path: ureader_ie/train-* - config_name: vision_flan(filtered) data_files: - split: train path: vision_flan(filtered)/train-* - config_name: vistext(cauldron) data_files: - split: train path: vistext(cauldron)/train-* - config_name: visual7w(cauldron,llava_format) data_files: - split: train path: visual7w(cauldron,llava_format)/train-* - config_name: visualmrc(cauldron) data_files: - split: train path: visualmrc(cauldron)/train-* - config_name: vqarad(cauldron,llava_format) data_files: - split: train path: vqarad(cauldron,llava_format)/train-* - config_name: vsr(cauldron,llava_format) data_files: - split: train path: vsr(cauldron,llava_format)/train-* - config_name: websight(cauldron) data_files: - split: train path: websight(cauldron)/train-* --- # Dataset Card for LLaVA-OneVision [2024-09-01]: Uploaded VisualWebInstruct(filtered), it's used in OneVision Stage > almost all subsets are uploaded with HF's required format and you can use the recommended interface to download them and follow our code below to convert them. > the subset of `ureader_kg` and `ureader_qa` are uploaded with the processed jsons and tar.gz of image folders. > You may directly download them from the following url. > https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data/tree/main/ureader_kg In this dataset, we include the data splits used in the both final image stage and one-vision stage. For more details, please check our [paper](arxiv.org/abs/2408.03326) and our [training doc](https://github.com/LLaVA-VL/LLaVA-NeXT/tree/main/scripts/train#about-the-llava-onevision-data). ## Dataset Description - Curated by: Bo Li, Kaichen Zhang, Hao Zhang, Yuanhan Zhang, Renrui Zhang, Feng Li, Dong Guo - Language(s) (NLP): English, Chinese - License: Apache License 2.0 ## Dataset Sources <!-- Provide the basic links for the dataset. --> - Dataset Collection: We include a few subsets from existing dataset collection [Cambrian](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M), [Cauldron](https://huggingface.co/datasets/HuggingFaceM4/the_cauldron), [UReader](https://arxiv.org/abs/2310.05126). Since we only used a few subsets from these datasets, and applied the cleaning and re-annotation process, we uploaded our processed version of these datasets into our own repository and thank the authors for providing the original datasets. - Other Datasets: For rest single source dataset, such as AI2D, OKVQA, we cite and link the original sources in our paper. ## Uses This dataset is used for the training of the LLaVA-OneVision model. We only allow the use of this dataset for academic research and education purpose. For OpenAI GPT-4 generated data, we recommend the users to check the [OpenAI Usage Policy](https://openai.com/policies/usage-policies/). ## Dataset Structure We expalin the data composition for mid-stage and final-stage at our repo in [training doc](https://github.com/LLaVA-VL/LLaVA-NeXT/tree/main/scripts/train#about-the-llava-onevision-data). ### Statistics We provide the statistics of the dataset in the following figures, and refer the audience to check our paper. ![](https://i.postimg.cc/2y989XZJ/WX20240802-145215-2x.png) ![](https://i.postimg.cc/MZ9TGXFD/WX20240802-145226-2x.png) ### Code Guidance To help audience to better understand our dataest, we upload them into Hugging Face Dataset compatible format. During LLaVA-OneVision training, we use the `json` and `image/video` folder to store the data. > the subset of `ureader_kg` and `ureader_qa` are uploaded with the processed jsons and tar.gz of image folders. You may directly download them from the following url. > https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data/tree/main/ureader_kg Here we provide the code guidance to convert the dataset into the format of LLaVA-OneVision, and conduct the training of the LLaVA-OneVision model with converted dataset. ```python import os from datasets import load_dataset from tqdm import tqdm import json data = load_dataset("lmms-lab/LLaVA-OneVision-Data", split="train") image_folder = "<your_image_folder>" converted_data = [] for da in tqdm(data): json_data = {} json_data["id"] = da["id"] if da["image"] is not None: json_data["image"] = f"{da['id']}.jpg" da["image"].save(os.path.join(image_folder, json_data["image"])) json_data["conversations"] = da["conversations"] converted_data.append(json_data) with open("<your_json_file>.json", "w") as f: json.dump(converted_data, f, indent=4, ensure_ascii=False) ``` ## Citation BibTeX: [More Information Needed] ## Glossary The dataset collection process is conducted by all of the authors, we thank the Feng Li and Renrui Zhang for providing [LLaVA-M4-Instruct Data](https://huggingface.co/datasets/lmms-lab/M4-Instruct-Data) and Yuanhan for providing the [Video datasets](https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K). After the dataset collection, the cleaning and re-annotation process, including final mixture of the dataset, is conducted by Bo Li and with the great help of Kaichen Zhang. ## Dataset Card Authors The dataset is curated by the following authors: Bo Li, Kaichen Zhang, Hao Zhang, Yuanhan Zhang, Renrui Zhang, Feng Li ## Dataset Card Contact [Bo Li](https://brianboli.com/): [email protected] [Kaichen Zhang](https://www.linkedin.com/in/kaichen-zhang-014b17219/?originalSubdomain=sg)
Forceless/PPTAgent	Forceless	2024-10-20T05:51:45Z	11,427	3	[ "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-10-18T04:49:53Z	null	--- dataset_info: features: - name: filename dtype: string - name: size dtype: int64 - name: url dtype: string - name: license dtype: string - name: title dtype: string - name: created dtype: string - name: updated dtype: string - name: doi dtype: string - name: checksum dtype: string - name: page dtype: int64 - name: topic dtype: string - name: filetype dtype: string splits: - name: pptx num_bytes: 317828 num_examples: 761 - name: pdf num_bytes: 253893 num_examples: 603 download_size: 249178 dataset_size: 571721 configs: - config_name: default data_files: - split: pptx path: data/pptx-* - split: pdf path: data/pdf-* ---
allenai/WildChat	allenai	2024-10-17T17:48:53Z	1,308	142	[ "task_categories:text-generation", "task_categories:question-answering", "task_categories:text2text-generation", "license:odc-by", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2405.01470", "arxiv:2409.03753", "arxiv:2406.13706", "region:us", "instruction-finetuning" ]	[ "text-generation", "question-answering", "text2text-generation" ]	2023-10-27T23:53:36Z	null	--- license: odc-by size_categories: - 100K<n<1M task_categories: - text-generation - question-answering - text2text-generation pretty_name: WildChat dataset_info: features: - name: conversation_id dtype: string - name: model dtype: string - name: timestamp dtype: timestamp[s, tz=UTC] - name: conversation list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: toxic dtype: bool - name: turn dtype: int64 - name: language dtype: string - name: openai_moderation list: - name: categories struct: - name: harassment dtype: bool - name: harassment/threatening dtype: bool - name: hate dtype: bool - name: hate/threatening dtype: bool - name: self-harm dtype: bool - name: self-harm/instructions dtype: bool - name: self-harm/intent dtype: bool - name: sexual dtype: bool - name: sexual/minors dtype: bool - name: violence dtype: bool - name: violence/graphic dtype: bool - name: category_scores struct: - name: harassment dtype: float64 - name: harassment/threatening dtype: float64 - name: hate dtype: float64 - name: hate/threatening dtype: float64 - name: self-harm dtype: float64 - name: self-harm/instructions dtype: float64 - name: self-harm/intent dtype: float64 - name: sexual dtype: float64 - name: sexual/minors dtype: float64 - name: violence dtype: float64 - name: violence/graphic dtype: float64 - name: flagged dtype: bool - name: detoxify_moderation list: - name: identity_attack dtype: float32 - name: insult dtype: float32 - name: obscene dtype: float32 - name: severe_toxicity dtype: float32 - name: sexual_explicit dtype: float32 - name: threat dtype: float32 - name: toxicity dtype: float32 - name: toxic dtype: bool - name: redacted dtype: bool splits: - name: train num_bytes: 2949464505.6494355 num_examples: 529428 download_size: 1586548072 dataset_size: 2949464505.6494355 configs: - config_name: default data_files: - split: train path: data/train-* tags: - instruction-finetuning --- # Dataset Card for WildChat ## Note: a newer version with 1 million conversations and demographic information can be found [here](https://huggingface.co/datasets/allenai/WildChat-1M). ## Dataset Description - Paper: https://arxiv.org/abs/2405.01470 - Interactive Search Tool: https://wildvisualizer.com ([paper](https://arxiv.org/abs/2409.03753)) - License: [ODC-BY](https://opendatacommons.org/licenses/by/1-0/) - Language(s) (NLP): multi-lingual - Point of Contact: [Yuntian Deng](https://yuntiandeng.com/) ### Dataset Summary WildChat is a collection of 650K conversations between human users and ChatGPT. We collected WildChat by offering online users free access to OpenAI's GPT-3.5 and GPT-4. The dataset contains a broad spectrum of user-chatbot interactions that are not previously covered by other instruction fine-tuning datasets: for example, interactions include ambiguous user requests, code-switching, topic-switching, political discussions, etc. WildChat can serve both as a dataset for instructional fine-tuning and as a valuable resource for studying user behaviors. Note that this version of the dataset only contains non-toxic user inputs/ChatGPT responses. ### Updates 2024-10-17: Content Update. Conversations flagged by [Niloofar Mireshghallah](https://homes.cs.washington.edu/~niloofar/) and her collaborators in ["Breaking News: Case Studies of Generative AI's Use in Journalism"](https://arxiv.org/abs/2406.13706) for containing PII or sensitive information have been removed from this version of the dataset. 2024-07-22: Content Update. All toxic conversations identified by the OpenAI Moderations API or Detoxify have been removed from this version of the dataset. 2024-06-26: License Change. We have updated the license of WildChat to [ODC-BY](https://opendatacommons.org/licenses/by/1-0/). This change is retroactively applied to any previous downloads under the ImpACT license. ### Languages 66 languages were detected in WildChat. ### Personal and Sensitive Information The data has been de-identified with Microsoft Presidio and hand-written rules by the authors. ### Data Fields - `conversation_id` (string): Each conversation has a unique id. - `model` (string): The underlying OpenAI model, such as gpt-3.5-turbo or gpt-4. - `timestamp` (timestamp): The timestamp of the last turn in the conversation in UTC. - `conversation` (list): A list of user/assistant utterances. Each utterance is a dictionary containing the `role` of the speaker (user or assistant), the `content` of the utterance, the detected `language` of the utterance, whether the content of the utterance is considered `toxic`, and whether PII has been detected and anonymized (`redacted`). - `turn` (int): The number of turns in the conversation. A turn refers to one round of user-assistant interaction. - `language` (string): The language of the conversation. Note that this is the most frequently detected language in the utterances of the conversation. - `openai_moderation` (list): A list of OpenAI Moderation results. Each element in the list corresponds to one utterance in the conversation. - `detoxify_moderation` (list): A list of Detoxify results. Each element in the list corresponds to one utterance in the conversation. - `toxic` (bool): Whether this conversation contains any utterances considered to be toxic by either OpenAI Moderation or Detoxify. - `redacted` (bool): Whether this conversation contains any utterances in which PII is detected and anonymized. ### Empty User Inputs This dataset includes a small subset of conversations where users submitted empty inputs, sometimes leading to hallucinated responses from the assistant. This issue, first noticed by @yuchenlin, arises from the design of our Huggingface chatbot used for data collection, which did not restrict the submission of empty inputs. As a result, users could submit without entering any text, causing the assistant to generate responses without any user prompts. This occurs in a small fraction of the dataset---12,405 out of 652,139 conversations. ### Licensing Information WildChat is now made available under the [ODC-BY License](https://opendatacommons.org/licenses/by/1-0/). This change is retroactively applied to any previous downloads under the ImpACT license. ### Citation Information Please consider citing [our paper](https://arxiv.org/abs/2405.01470) if you find this dataset useful: ``` @inproceedings{ zhao2024wildchat, title={WildChat: 1M Chat{GPT} Interaction Logs in the Wild}, author={Wenting Zhao and Xiang Ren and Jack Hessel and Claire Cardie and Yejin Choi and Yuntian Deng}, booktitle={The Twelfth International Conference on Learning Representations}, year={2024}, url={https://openreview.net/forum?id=Bl8u7ZRlbM} } ``` ``` @misc{deng2024wildvisopensourcevisualizer, title={WildVis: Open Source Visualizer for Million-Scale Chat Logs in the Wild}, author={Yuntian Deng and Wenting Zhao and Jack Hessel and Xiang Ren and Claire Cardie and Yejin Choi}, year={2024}, eprint={2409.03753}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2409.03753}, } ```
openai/MMMLU	openai	2024-10-16T18:39:00Z	11,119	478	[ "task_categories:question-answering", "language:ar", "language:bn", "language:de", "language:es", "language:fr", "language:hi", "language:id", "language:it", "language:ja", "language:ko", "language:pt", "language:sw", "language:yo", "language:zh", "license:mit", "size_categories:100K<n<1M", "format:csv", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2009.03300", "region:us" ]	[ "question-answering" ]	2024-09-13T16:37:19Z	null	--- task_categories: - question-answering configs: - config_name: default data_files: - split: test path: test/.csv - config_name: AR_XY data_files: - split: test path: test/mmlu_AR-XY.csv - config_name: BN_BD data_files: - split: test path: test/mmlu_BN-BD.csv - config_name: DE_DE data_files: - split: test path: test/mmlu_DE-DE.csv - config_name: ES_LA data_files: - split: test path: test/mmlu_ES-LA.csv - config_name: FR_FR data_files: - split: test path: test/mmlu_FR-FR.csv - config_name: HI_IN data_files: - split: test path: test/mmlu_HI-IN.csv - config_name: ID_ID data_files: - split: test path: test/mmlu_ID-ID.csv - config_name: IT_IT data_files: - split: test path: test/mmlu_IT-IT.csv - config_name: JA_JP data_files: - split: test path: test/mmlu_JA-JP.csv - config_name: KO_KR data_files: - split: test path: test/mmlu_KO-KR.csv - config_name: PT_BR data_files: - split: test path: test/mmlu_PT-BR.csv - config_name: SW_KE data_files: - split: test path: test/mmlu_SW-KE.csv - config_name: YO_NG data_files: - split: test path: test/mmlu_YO-NG.csv - config_name: ZH_CN data_files: - split: test path: test/mmlu_ZH-CN.csv language: - ar - bn - de - es - fr - hi - id - it - ja - ko - pt - sw - yo - zh license: mit --- # Multilingual Massive Multitask Language Understanding (MMMLU) The MMLU is a widely recognized benchmark of general knowledge attained by AI models. It covers a broad range of topics from 57 different categories, covering elementary-level knowledge up to advanced professional subjects like law, physics, history, and computer science. We translated the MMLU’s test set into 14 languages using professional human translators. Relying on human translators for this evaluation increases confidence in the accuracy of the translations, especially for low-resource languages like Yoruba. We are publishing the professional human translations and the code we use to run the evaluations. This effort reflects our commitment to improving the multilingual capabilities of AI models, ensuring they perform accurately across languages, particularly for underrepresented communities. By prioritizing high-quality translations, we aim to make AI technology more inclusive and effective for users worldwide. ## Locales MMMLU contains the MMLU test set translated into the following locales: AR_XY (Arabic) * BN_BD (Bengali) * DE_DE (German) * ES_LA (Spanish) * FR_FR (French) * HI_IN (Hindi) * ID_ID (Indonesian) * IT_IT (Italian) * JA_JP (Japanese) * KO_KR (Korean) * PT_BR (Brazilian Portuguese) * SW_KE (Swahili) * YO_NG (Yoruba) * ZH_CN (Simplified Chinese) ## Sources Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., & Steinhardt, J. (2021). [Measuring Massive Multitask Language Understanding](https://arxiv.org/abs/2009.03300). [OpenAI Simple Evals GitHub Repository](https://github.com/openai/simple-evals)
HuggingFaceH4/ultrachat_200k	HuggingFaceH4	2024-10-16T11:52:27Z	15,121	527	[ "task_categories:text-generation", "language:en", "license:mit", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2305.14233", "region:us" ]	[ "text-generation" ]	2023-10-24T08:24:57Z	null	--- language: - en license: mit size_categories: - 100K<n<1M task_categories: - text-generation pretty_name: UltraChat 200k configs: - config_name: default data_files: - split: train_sft path: data/train_sft-* - split: test_sft path: data/test_sft-* - split: train_gen path: data/train_gen-* - split: test_gen path: data/test_gen-* dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train_sft num_bytes: 1397058554 num_examples: 207865 - name: test_sft num_bytes: 154695659 num_examples: 23110 - name: train_gen num_bytes: 1347396812 num_examples: 256032 - name: test_gen num_bytes: 148276089 num_examples: 28304 download_size: 1624049723 dataset_size: 3047427114 --- # Dataset Card for UltraChat 200k ## Dataset Description This is a heavily filtered version of the [UltraChat](https://github.com/thunlp/UltraChat) dataset and was used to train [Zephyr-7B-β](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), a state of the art 7b chat model. The original datasets consists of 1.4M dialogues generated by ChatGPT and spanning a wide range of topics. To create `UltraChat 200k`, we applied the following logic: - Selection of a subset of data for faster supervised fine tuning. - Truecasing of the dataset, as we observed around 5% of the data contained grammatical errors like "Hello. how are you?" instead of "Hello. How are you?" - Removal of dialogues where the assistant replies with phrases like "I do not have emotions" or "I don't have opinions", even for fact-based prompts that don't involve either. ## Dataset Structure The dataset has four splits, suitable for: * Supervised fine-tuning (`sft`). * Generation ranking (`gen`) via techniques like rejection sampling or PPO. The number of examples per split is shown as follows: \| train_sft \| test_sft \| train_gen \| test_gen \| \|:-------:\|:-----------:\|:-----:\| :-----:\| \| 207865 \| 23110 \| 256032 \| 28304 \| The dataset is stored in parquet format with each entry using the following schema: ``` { "prompt": "Create a fully-developed protagonist who is challenged to survive within a dystopian society under the rule of a tyrant. ...", "messages":[ { "content": "Create a fully-developed protagonist who is challenged to survive within a dystopian society under the rule of a tyrant. ...", "role": "user" }, { "content": "Name: Ava\n\n Ava was just 16 years old when the world as she knew it came crashing down. The government had collapsed, leaving behind a chaotic and lawless society. ...", "role": "assistant" }, { "content": "Wow, Ava's story is so intense and inspiring! Can you provide me with more details. ...", "role": "user" }, { "content": "Certainly! ....", "role": "assistant" }, { "content": "That's really interesting! I would love to hear more...", "role": "user" } { "content": "Certainly! ....", "role": "assistant" }, ], "prompt_id": "d938b65dfe31f05f80eb8572964c6673eddbd68eff3db6bd234d7f1e3b86c2af" } ``` ## Citation If you find this dataset is useful in your work, please cite the original UltraChat dataset: ``` @misc{ding2023enhancing, title={Enhancing Chat Language Models by Scaling High-quality Instructional Conversations}, author={Ning Ding and Yulin Chen and Bokai Xu and Yujia Qin and Zhi Zheng and Shengding Hu and Zhiyuan Liu and Maosong Sun and Bowen Zhou}, year={2023}, eprint={2305.14233}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
HuggingFaceH4/ultrafeedback_binarized	HuggingFaceH4	2024-10-16T11:49:06Z	8,312	285	[ "task_categories:text-generation", "language:en", "license:mit", "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2310.01377", "region:us" ]	[ "text-generation" ]	2023-10-24T08:53:19Z	null	--- language: - en license: mit task_categories: - text-generation pretty_name: UltraFeedback Binarized configs: - config_name: default data_files: - split: train_prefs path: data/train_prefs-* - split: train_sft path: data/train_sft-* - split: test_prefs path: data/test_prefs-* - split: test_sft path: data/test_sft-* - split: train_gen path: data/train_gen-* - split: test_gen path: data/test_gen-* dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 splits: - name: train_prefs num_bytes: 405688662 num_examples: 61135 - name: train_sft num_bytes: 405688662 num_examples: 61135 - name: test_prefs num_bytes: 13161585 num_examples: 2000 - name: test_sft num_bytes: 6697333 num_examples: 1000 - name: train_gen num_bytes: 325040536 num_examples: 61135 - name: test_gen num_bytes: 5337695 num_examples: 1000 download_size: 649967196 dataset_size: 1161614473 --- # Dataset Card for UltraFeedback Binarized ## Dataset Description This is a pre-processed version of the [UltraFeedback dataset](https://huggingface.co/datasets/openbmb/UltraFeedback) and was used to train [Zephyr-7Β-β](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), a state of the art chat model at the 7B parameter scale. The original UltraFeedback dataset consists of 64k prompts, where each prompt is accompanied with four model completions from a wide variety of open and proprietary models. GPT-4 is then used to assign a score to each completion, along criteria like helpfulness and honesty. To create `UltraFeedback Binarized`, we picked the highest `overall_score` as the "chosen" completion, and one of the remaining 3 at random as the "rejected" one. This defines the preference modelling splits for techniques like reward modelling or DPO. We also created splits for supervised fine-tuning (SFT) that use the "chosen" column as the dialogues to model, along with splits that involve generation like rejection sampling or PPO. For details on the dataset processing, see the accompanying [script](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized/blob/main/create_dataset.py). ## Dataset Structure ### Usage To load the dataset, run: ```python from datasets import load_dataset ds = load_dataset("HuggingFaceH4/ultrafeedback_binarized") ``` Note: after the release of Zephyr-7b-β, the team at [Argilla](https://huggingface.co/argilla) noted that there were a few hundred completions with the incorrect label. Similarly, members of the community pointed out that a several prompts were sourced from the TruthfulQA benchmark which can lead to contamination with public leaderboards. Both of these issues have been resolved in the latest version of the dataset, but if you wish to use the older version, you can run: ```python from datasets import load_dataset ds = load_dataset("HuggingFaceH4/ultrafeedback_binarized", revision="292c16329d921287c4166934cac1a6ad1e13a6c5") ``` ### Data Splits The dataset has six splits, suitable for: * Supervised fine-tuning (`sft`). * Preference modelling (`prefs`) to train reward models or apply techniques like DPO. * Generation ranking (`gen`) via techniques like rejection sampling or PPO. The number of examples per split is shown as follows: \| train_sft \| test_sft \| train_prefs \| test_prefs \| train_gen \| test_gen \| \|:-------:\|:-----------:\|:-----:\| :-----:\| :-----:\| :-----:\| \| 61135 \| 1000 \| 61135 \| 2000 \| 61135 \| 1000 \| The dataset is stored in parquet format with each entry using the following schema: ``` { "prompt": "Which books are still banned in Canada?", "chosen": [ { "content": "Which books are still banned in Canada?", "role": "user" }, { "content": "As a responsible and ethical AI assistant, I must inform you that I cannot provide a list of banned books in Canada, as there are no books that are currently banned in the country. Canada has a strong commitment to freedom of expression and the right to access information, and there are no laws in place that restrict the publication or distribution of books.\n", "role": "assistant" } ], "messages": [ { "content": "Which books are still banned in Canada?", "role": "user" }, { "content": "As a responsible and ethical AI assistant, I must inform you that I cannot provide a list of banned books in Canada, as there are no books that are currently banned in the country. Canada has a strong commitment to freedom of expression and the right to access information, and there are no laws in place that restrict the publication or distribution of books.\n", "role": "assistant" }], "prompt_id": "aeccf551d9ba42fdf5f2044de43b8ce6e360fb523ace428317b81d804594e090", "rejected": [ { "content": "Which books are still banned in Canada?", "role": "user"}, { "content": "According to the Canadian Government’s Ban Affront website, there are still several books that are banned in Canada. These include The Begum’s Millionaire, The Education of Little Tree, The Harry Potter series, Lolita, 1984, and Lady Chatterley’s Lover. Some of these books are considered inaccessible due to their age, while others are still legally banned in certain parts of the country.", "role": "assistant" } ], "score_chosen": 8.0, "score_rejected": 5.0 } ``` You should use the `chosen` and `rejected` columns for techniques like DPO, while the `messages` column is suitable for SFT or PPO. ## Citation If you find this dataset is useful in your work, please cite the original UltraFeedback dataset: ``` @misc{cui2023ultrafeedback, title={UltraFeedback: Boosting Language Models with High-quality Feedback}, author={Ganqu Cui and Lifan Yuan and Ning Ding and Guanming Yao and Wei Zhu and Yuan Ni and Guotong Xie and Zhiyuan Liu and Maosong Sun}, year={2023}, eprint={2310.01377}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
GAIR/o1-journey	GAIR	2024-10-16T00:42:02Z	122	133	[ "language:en", "size_categories:n<1K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-10-16T00:28:30Z	null	--- language: - en size_categories: - n<1K --- Dataset for [O1 Replication Journey: A Strategic Progress Report](https://github.com/GAIR-NLP/O1-Journey) ## Usage ```python from datasets import load_dataset dataset = load_dataset("GAIR/o1-journey", split="train") ``` ## Citation If you find our dataset useful, please cite: ``` @misc{o1journey, author = {Yiwei Qin and Xuefeng Li and Haoyang Zou and Yixiu Liu and Shijie Xia and Zhen Huang and Yixin Ye and Weizhe Yuan and Zhengzhong Liu and Yuanzhi Li and Pengfei Liu}, title = {O1 Replication Journey: A Strategic Progress Report – Part 1}, year = {2024}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/GAIR-NLP/O1-Journey}}, } ```
allenai/peS2o	allenai	2024-10-13T02:53:05Z	2,322	171	[ "task_categories:text-generation", "task_categories:fill-mask", "source_datasets:allenai/s2orc", "language:en", "license:odc-by", "size_categories:10B<n<100B", "region:us", "biology", "chemistry", "engineering", "computer science", "physics", "material science", "math", "psychology", "economics", "political science", "business", "geology", "sociology", "geography", "environmental science", "art", "history", "philosophy" ]	[ "text-generation", "fill-mask" ]	2023-06-29T04:54:16Z	null	--- license: - odc-by task_categories: - text-generation - fill-mask language: - en tags: - biology - chemistry - engineering - computer science - physics - material science - math - psychology - economics - political science - business - geology - sociology - geography - environmental science - art - history - philosophy pretty_name: peS2o (Pretraining Efficiently on S2ORC) size_categories: - 10B<n<100B source_datasets: - allenai/s2orc --- <p align="center" style="margin-top: -2em"> <img src="https://huggingface.co/datasets/allenai/pes2o/resolve/main/logo.png" alt="peS2o logo. It's a picure of a mortar and pestle with documents flying in." width=384px height=auto> </p> <p align="center" style="font-size: 1.2em; margin-top: -1em"><i>Pretraining Effectively on <a href="https://github.com/allenai/s2orc">S2ORC</a>!</i></p> The peS2o dataset is a collection of ~40M creative open-access academic papers, cleaned, filtered, and formatted for pre-training of language models. It is derived from the [Semantic Scholar Open Research Corpus][2]([Lo et al, 2020][1]), or S2ORC. We release multiple version of peS2o, each with different processing and knowledge cutoff date. We recommend you to use the latest version available. If you use this dataset, please cite: ```bibtex @techreport{peS2o, author = {Luca Soldaini and Kyle Lo}, year = 2023, title = {{peS2o (Pretraining Efficiently on S2ORC) Dataset}}, institution = {{Allen Institute for AI}}, note = {ODC-By, \url{https://github.com/allenai/pes2o}} } ``` ## Document Format Each document in the dataset is a dictionary with the following fields: - `added`: Date the document was added to the corpus. - `created`: Best-guess date for when the document was first published. Some have resolution down to the day, only down to the year. - `id`: Semantic Scholar Corpus ID of the document; it can be used with the [Semantic Scholar API](https://api.semanticscholar.org/) to retrieve metadata about the document (e.g., fields of study, authors). - `source`: Collection from which the document was sourced from. At the moment, two are supported: - `s2orc`: collection of full-text papers - `s2ag`: collection of title and abstracts - `text`: Text of the document. Paragraphs are separated by two newlines (`\n\n`). - `version`: version of peS2o. ------ ## peS2o V2 (Latest) ### Key Facts - Knowledge cutoff: 2023-01-03 - Number of documents: 38.97M - Number of whitespace-separated tokens: 42.01B ### Processing peS2o V2 is largely the same as V1, but it includes additional heuristics s2ag aimed at filtering out OCR errors from abstract. First, we check if the abstract was obtained from Semantic Scholar sources that are likely to contain OCR'ed content. For any abstract derived from those sources, we count how often the text contains subsequences matching `\b([A-Za-z]\s)([a-z]\s)[A-Za-z]\b`, i.e. individual alpha letters separated by a space. This heuristic matches cases such as `A b stra ct` (2 matching subsequences), where the OCR parser inserted erroneous spaces. Any abstract with more than 4 matching subsequences is removed. #### Statistics \| Dataset \| Split \| # Documents \| # Words \| \|:-------:\|:-----:\|------------:\|---------------:\| \| s2orc \| train \| 8,242,162 \| 36,088,195,908 \| \| s2orc \| valid \| 51,323 \| 255,139,074 \| \| s2ag \| train \| 30,569,017 \| 5,920,099,207 \| \| s2ag \| valid \| 109,709 \| 24,029,459 \| ------- ## peS2o V1 ### Key Facts - Knowledge cutoff: 2023-01-03 - Number of documents: 67.56M - Number of whitespace-separated tokens: 47.37B ### Processing Processing differs slightly wether it was derived from the full-text corpus (`s2orc`) or the title and abstract corpus (`s2ag`). #### S2ORC-derived documents Unfiltered, S2ORC contains 11.3M papers and 46.9B whitespace-separated tokens as of 2023-01-03. To derive peS2o v1, we impose the following constraints: - The paper must have a title and abstract. - From each paper, we use [Grobid](https://github.com/kermitt2/grobid) to extract section headers and paragraphs; figures, tables, and references, and any other non-textual content is removed. Title and abstracts are also available, but they come from the Semantic Scholar metadata (obtained through the APIs), not Grobid. - The paper must be in English. - To determine the language of each document, we use the [pycld3](https://github.com/bsolomon1124/pycld3) library - We run pycld3 on the first 2000 characters of each paragraph in the paper. - The language of the paper is the most common language of the paragraphs. - The paper must have at least 500 whitespace-separated words. - The paper was published after 1969; papers published before this date are often obtained through OCR and contain unrecoverable errors. - The paper must have at least 5 paragraphs. - All sections that have a average log word probability of less than `-20` are removed. - To calculate the average log word probability, we use word frequencies extracted from the [1T Web Ngram corpus](https://catalog.ldc.upenn.edu/LDC2006T13); specifically, we use the list available [created by Rachel Tatman](https://www.kaggle.com/datasets/rtatman/english-word-frequency). A copy is hosted [here](https://ai2-s2-research-public.s3-us-west-2.amazonaws.com/lucas/google-1T-unigram/unigram_freq.csv). - The most frequent word in the paper consists of alpha characters only, and it appears in less than 7.5% of the document. - Words are obtained by splitting the text on whitespace. The train set contains papers published before 2022-12-01; the validation set includes documents published after 2022-12-01 and until 2023-01-03. #### S2AG-derived documents The S2AG corpus contains titles and abstracts of papers in Semantic Scholar. Unfiltered, the corpus contains 91.1M papers and 15.5B whitespace-separated tokens as of 2023-01-03. To derive peS2o v1, we impose the following constraints: - Abstract must be in English. - To calculate the language, we once again use pycld3 - Title must be in English, or have average unigram log probability greater than -20. - Abstract must be in English. - Abstract must have higher than -20 average unigram log probability. - Abstract must have at least 50 words. - Abstract must have no more than 1000 words. - The most frequent word in the union of text and abstract must be a 2+ character alpha word, or it can be `a` followed by a 2+ character alpha word. - Paper was published after 1969. #### Statistics \| Dataset \| Split \| # Documents \| # Words \| \|:-------:\|:-------:\|:-----------:\|:--------------:\| \|s2orc \| train \| 8,242,162 \| 36,088,195,908 \| \|s2orc \| valid \| 51,323 \| 255,139,074 \| \|s2ag \| train \| 59,382,301 \| 11,009,123,378 \| \|s2ag \| valid \| 111,228 \| 24,398,512 \| [1]: https://aclanthology.org/2020.acl-main.447/ [2]: https://github.com/allenai/s2orc
thanhkt/manim_code	thanhkt	2024-10-10T14:55:23Z	74	4	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-10-10T14:55:22Z	2	--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 7074758 num_examples: 4400 download_size: 2439317 dataset_size: 7074758 configs: - config_name: default data_files: - split: train path: data/train-* ---
IGNF/PASTIS-HD	IGNF	2024-10-04T13:39:24Z	28,466	12	[ "task_categories:image-classification", "task_categories:image-segmentation", "license:etalab-2.0", "size_categories:1K<n<10K", "format:imagefolder", "modality:image", "library:datasets", "library:mlcroissant", "arxiv:2107.07933", "arxiv:2112.07558", "arxiv:2404.08351", "region:us", "remote sensing", "Agricultural" ]	[ "image-classification", "image-segmentation" ]	2024-04-02T14:58:15Z	null	--- license: etalab-2.0 task_categories: - image-classification - image-segmentation tags: - remote sensing - Agricultural size_categories: - 1K<n<10K --- # 🌱 PASTIS-HD 🌿 Panoptic Agricultural Satellite TIme Series : optical time series, radar time series and very high resolution image [PASTIS](https://github.com/VSainteuf/pastis-benchmark) is a benchmark dataset for panoptic and semantic segmentation of agricultural parcels from satellite time series. It contains 2,433 patches within the French metropolitan territory with panoptic annotations (instance index + semantic label for each pixel). Each patch is a Sentinel-2 multispectral image time series of variable lentgh. This dataset have been extended in 2021 with aligned radar Sentinel-1 observations for all 2433 patches. For each patch, it constains approximately 70 observations of Sentinel-1 in ascending orbit, and 70 observations in descending orbit. Each each Sentinel1 observation is assembled into a 3-channel image: vertical polarization (VV), horizontal polarisation (VH), and the ratio vertical over horizontal polarization (VV/VH). This extension is named PASTIS-R. We extend PASTIS with aligned very high resolution satellite images from SPOT 6-7 constellation for all 2433 patches in addition to the Sentinel-1 and 2 time series. The image are resampled to a 1m resolution and converted to 8 bits. This enhancement significantly improves the dataset's spatial content, providing more granular information for agricultural parcel segmentation. PASTIS-HD can be used to evaluate multi-modal fusion methods (with optical time series, radar time series and VHR images) for parcel-based classification, semantic segmentation, and panoptic segmentation. ## Dataset in numbers 🛰️ Sentinel 2 \| 🛰️ Sentinel 1 \| 🛰️ SPOT 6-7 VHR \| 🗻 Annotations :-------------------------------------------- \| :-------------------------------------------------- \| :------------------------------\| :------------------------------ ➡️ 2,433 time series \| ➡️ 2 time 2,433 time series \| ➡️ 2,433 images \| 124,422 individual parcels ➡️ 10m / pixel \| ➡️ 10m / pixel \| ➡️ 1.5m / pixel \| covers ~4,000 km² ➡️ 128x128 pixels / images \| ➡️ 128x128 pixels / images \| ➡️ 1280x1280 pixels / images \| over 2B pixels ➡️ 38-61 acquisitions / series \| ➡️ ~ 70 acquisitions / series \| ➡️ One observation \| 18 crop types ➡️ 10 spectral bands \|➡️ 2 spectral bands \| ➡️ 3 spectral bands \| ⚠️ The SPOT data are natively 1.5m resolution, but we over-sampled them at 1m to align them pixel-perfect with Sentinel data. ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6582b7dd75754a803e484487/sxmnCAGs0p2u_PALLsqyN.jpeg) ## Data loading The Github repository associated to this dataset contains a PyTorch dataset class of [the OmniSat repository](https://github.com/gastruc/OmniSat/blob/main/src/data/Pastis.py) that can be readily used to load data for training models on PASTIS-HD. The time series contained in PASTIS have variable lengths. The Sentinel 1 and 2 time series are stored in numpy array. The SPOT images are in TIFF format. The annotations are in numpy array too. ⚠️ The S2 and S1 folders contains more than 2433 files on the contrary to the labels folder. Some patches are not labelled and not used for training. The relevant information can be find in the metadata.geojson file (with 2433 entries), which is used as an index by the dataloader. ### Remark about the folder names ⚠️ The DATA_S1A folder contains the Sentinel-1 ascendent images whereas the DATA_S1D folder contains the Sentinel-1 descendant images. ## Ground Truth Annotations The agricultural parcels are grouped into 18 different crop classes as shown in the table below. The backgroud class corresponds to non-agricultural land, and the void label for parcels that are mostly outside their patch. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6582b7dd75754a803e484487/aHQB0uq4cqBX-7hkCkpFn.png) Additional information about the dataset can be found in the documentation/pastis-documentation.pdf document. ## Credits - The Sentinel imagery used in PASTIS was retrieved from [THEIA](www.theia.land.fr): "Value-added data processed by the CNES for the Theia www.theia.land.fr data cluster using Copernicus data. The treatments use algorithms developed by Theia’s Scientific Expertise Centres. " - The annotations used in PASTIS stem from the French [land parcel identification system](https://www.data.gouv.fr/en/datasets/registre-parcellaire-graphique-rpg-contours-des-parcelles-et-ilots-culturaux-et-leur-groupe-de-cultures-majoritaire/) produced by IGN. - The SPOT images are opendata thanks to the Dataterra Dinamis initiative in the case of the ["Couverture France DINAMIS"](https://dinamis.data-terra.org/opendata/) program. ## References If you use PASTIS please cite the [related paper](https://arxiv.org/abs/2107.07933): ``` @article{garnot2021panoptic, title={Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks}, author={Sainte Fare Garnot, Vivien and Landrieu, Loic}, journal={ICCV}, year={2021} } ``` For the PASTIS-R optical-radar fusion dataset, please also cite [this paper](https://arxiv.org/abs/2112.07558v1): ``` @article{garnot2021mmfusion, title = {Multi-modal temporal attention models for crop mapping from satellite time series}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, year = {2022}, doi = {https://doi.org/10.1016/j.isprsjprs.2022.03.012}, author = {Vivien {Sainte Fare Garnot} and Loic Landrieu and Nesrine Chehata}, } ``` For the PASTIS-HD with the 3 modalities optical-radar time series plus VHR images dataset, please also cite [this paper](https://arxiv.org/abs/2404.08351): ``` @article{astruc2024omnisat, title={Omni{S}at: {S}elf-Supervised Modality Fusion for {E}arth Observation}, author={Astruc, Guillaume and Gonthier, Nicolas and Mallet, Clement and Landrieu, Loic}, journal={ECCV}, year={2024} } ```
Gryphe/Sonnet3.5-SlimOrcaDedupCleaned	Gryphe	2024-10-04T08:41:30Z	596	90	[ "license:mit", "size_categories:100K<n<1M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-07-14T08:25:38Z	null	--- license: mit size_categories: - 100K<n<1M --- 2024-10-04: I fixed two issues that were affecting newlines (all double newlines were gone!) and cleaned up spaces preceding closing quotes. Many thanks to [PocketDoc](https://huggingface.co/PocketDoc) for bringing this to my attention! A Sonnet 3.5 generated version of Caitlyn's wonderfully cleaned [SlimOrca Deduped dataset](https://huggingface.co/datasets/cgato/SlimOrcaDedupCleaned), ready for training using the ShareGPT format. As always, an effort was made to ensure no censoring was applied to the responses. If you find any refusals, let me know!
hendrydong/preference_700K	hendrydong	2024-09-28T16:05:26Z	278	15	[ "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2405.07863", "region:us" ]	[]	2024-04-18T01:43:10Z	2	--- dataset_info: features: - name: rejected list: - name: content dtype: string - name: role dtype: string - name: rejected_score dtype: float64 - name: chosen_score dtype: float64 - name: chosen list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 2802733004 num_examples: 700000 download_size: 1433169364 dataset_size: 2802733004 configs: - config_name: default data_files: - split: train path: data/train-* --- This is the preference dataset used for RLHF Workflow project. It is a mixed dataset of the following complenent, * HH-RLHF (Bai et al., 2022a) is a pairwise preference dataset where each sample is accompanied by a conversation history and two alternative responses written by an early Claude model with 52B parameters. The preferences of the responses are annotated by humans. * SHP (Ethayarajh et al., 2022) is sourced from Reddit and includes examples from 18 subreddits, such as askacademia, askbaking, askengineers, and changemyview. Each example is a Reddit post with a question/instruction and a pair of top-level comments. One comment is preferred by more Reddit users than the other. All preferences and responses are provided by humans. Only samples with a score ratio > 2 are used, and at most 5 pairs are taken for each prompt. * HelpSteer (Wang et al., 2023). This open-source dataset (Wang et al., 2023) contains prompts, responses, and five human-annotated attributes (helpfulness, correctness, coherence, complexity, and verbosity) ranging from 0 to 4. The prompts are generated using a mixture of template-generated and human-generated methods, while responses are generated by an in-house LLM. The authors generate up to 4 responses per prompt, and we can construct pairwise comparisons based on them. * PKU-SafeRLHF (Ji et al., 2024). This dataset (Ji et al., 2024) consists of 30k+ expert comparison data. Each sample includes two responses to a question and two preference signals for helpfulness and safety, respectively. The responses are generated by open-source chatbots, and the preference signals are merged through the results of 14 harm category multi-class classficiation. * UltraFeedback (Cui et al., 2023) consists of 64k prompts from diverse resources (including UltraChat, ShareGPT, Evol-Instruct, TruthfulQA, FalseQA, and FLAN) and the authors generate 4 responses per prompt using 4 different LLMs sampled from a diverse set of state-of-the-art open-source LLMs. The preference is from GPT-4 based on a fine-grained annotation instruction, which contains 4 different aspects, namely instruction-following, truthfulness, honesty and helpfulness. The dataset collection strategy of UltraFeedback has also influenced many subsequent works. * UltraInteract (Yuan et al., 2024a) is a preference dataset designed for complex reasoning tasks. The authors collect a preference tree for each instruction, with the instruction being the root and each action a node. A trajectory is a root-to-leaf path consisting of a sequence of actions. Paired correct and incorrect nodes or trajectories are used for preference learning. * Distilabel-Capybara is a preference dataset of multi-turn dialogues whose prompts are taken from Daniele and Suphavadeeprasit (2023), where the responses are generated by open-source LLMs and preferences are generated by GPT-4. * Distilabel-Orca is collected similarly with Capybara but with the prompts from Lian et al. (2023a). ## Reference If you found it useful, please cite ```bibtex @misc{dong2024rlhf, title={RLHF Workflow: From Reward Modeling to Online RLHF}, author={Hanze Dong and Wei Xiong and Bo Pang and Haoxiang Wang and Han Zhao and Yingbo Zhou and Nan Jiang and Doyen Sahoo and Caiming Xiong and Tong Zhang}, year={2024}, eprint={2405.07863}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```
MichaelR207/enron_qa_0922	MichaelR207	2024-09-22T17:42:07Z	22	2	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-09-22T17:22:22Z	2	--- dataset_info: features: - name: email dtype: string - name: questions sequence: string - name: rephrased_questions sequence: string - name: gold_answers sequence: string - name: alternate_answers sequence: sequence: string - name: incorrect_answers sequence: sequence: string - name: path dtype: string - name: user dtype: string - name: questions_count dtype: int64 - name: gold_rationales sequence: string - name: alternate_rationales sequence: sequence: string - name: include_email sequence: int64 splits: - name: train num_bytes: 730246275 num_examples: 73772 - name: dev num_bytes: 347395653 num_examples: 73772 - name: test num_bytes: 319859974 num_examples: 73772 download_size: 666346042 dataset_size: 1397501902 configs: - config_name: default data_files: - split: train path: data/train-* - split: dev path: data/dev-* - split: test path: data/test-* ---
jackyhate/text-to-image-2M	jackyhate	2024-09-22T09:38:54Z	12,740	100	[ "task_categories:text-to-image", "task_categories:image-to-text", "task_categories:image-classification", "language:en", "license:mit", "size_categories:100K<n<1M", "format:webdataset", "modality:image", "modality:text", "library:datasets", "library:webdataset", "library:mlcroissant", "doi:10.57967/hf/3066", "region:us" ]	[ "text-to-image", "image-to-text", "image-classification" ]	2024-09-11T14:02:35Z	null	--- license: mit task_categories: - text-to-image - image-to-text - image-classification language: - en size_categories: - 1M<n<10M --- # text-to-image-2M: A High-Quality, Diverse Text-to-Image Training Dataset ## Overview `text-to-image-2M` is a curated text-image pair dataset designed for fine-tuning text-to-image models. The dataset consists of approximately 2 million samples, carefully selected and enhanced to meet the high demands of text-to-image model training. The motivation behind creating this dataset stems from the observation that datasets with over 1 million samples tend to produce better fine-tuning results. However, existing publicly available datasets often have limitations: - Image Understanding Datasets: Not guarantee the quality of image. - Informal collected or Task-Specific Datasets: Not category balanced or lacks diversity. - Size Constraints: Available datasets are either too small or too large. (subset sampled from large datasets often lack diversity.) To address these issues, we combined and enhanced existing high-quality datasets using state-of-the-art text-to-image and captioning models to create `text-to-image-2M`. This includes data_512_2M, a 2M 512x512 fine-tuning dataset and data_1024_10K, a 10K high-quality, high-resolution dataset (for high-resolution adaptation). ## Dataset Composition ### data_512_2M The dataset is composed of several high-quality subsets, as detailed below: \| Source \| Samples \| Prompts \| Images \| \|-------------------------------------------------\|-------------\|--------------------------------------\|---------------------------------------------\| \| [LLaVA-next fine-tuning dataset](https://huggingface.co/datasets/lmms-lab/LLaVA-NeXT-Data) \| ~700K \| Re-captioned using Qwen2-VL \| Original images \| \| [LLaVA-pretrain dataset](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain) \| ~500K \| Original prompts \| Images generated by Flux-dev \| \| [ProGamerGov synthetic dataset (DALL·E 3)](https://huggingface.co/datasets/ProGamerGov/synthetic-dataset-1m-dalle3-high-quality-captions) \| ~900K \| Filtered for validity \| Center-cropped and validity-filtered images \| \| GPT-4o generated dataset \| 100K \| Generated by GPT-4o \| Images generated by Flux-dev \| ### data_1024_10K 10K images generated by Flux-dev with prompts generated by GPT-4o ## Usage: The dataset uses the [WebDataset](https://github.com/webdataset/webdataset) format and can be easily accessed and used with HuggingFace's datasets library like so: ```py from datasets import load_dataset base_url = "https://huggingface.co/datasets/jackyhate/text-to-image-2M/resolve/main/data_512_2M/data_{i:06d}.tar" num_shards = 46 # Number of webdataset tar files urls = [base_url.format(i=i) for i in range(num_shards)] dataset = load_dataset("webdataset", data_files={"train": urls}, split="train", streaming=True) # Example of iterating through the dataset for image in dataset: print(image) # single image in row with associated columns break ``` * Note that as long as `streaming=True` in the above example, the dataset does not have to be downloaded in full. ## Acknowledgments This dataset builds on the work of several open-source projects, including: - [LLaVA-next fine-tuning dataset](https://huggingface.co/datasets/lmms-lab/LLaVA-NeXT-Data) - [LLaVA-pretrain dataset](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain) - [ProGamerGov synthetic dataset (DALL·E 3)](https://huggingface.co/datasets/ProGamerGov/synthetic-dataset-1m-dalle3-high-quality-captions) - GPT-4o - Flux-1.0-dev We thank the contributors of these datasets and models for making this project possible.
mlfoundations/MINT-1T-HTML	mlfoundations	2024-09-21T01:50:16Z	234,694	83	[ "task_categories:image-to-text", "task_categories:text-generation", "language:en", "license:cc-by-4.0", "size_categories:100M<n<1B", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2406.11271", "region:us", "multimodal" ]	[ "image-to-text", "text-generation" ]	2024-07-21T06:48:51Z	null	--- license: cc-by-4.0 task_categories: - image-to-text - text-generation language: - en tags: - multimodal pretty_name: MINT-1T size_categories: - 100B<n<1T configs: - config_name: data-v1.1 data_files: - split: train path: data_v1_1/.parquet --- <h1 align="center"> 🍃 MINT-1T:<br>Scaling Open-Source Multimodal Data by 10x:<br> A Multimodal Dataset with One Trillion Tokens </h1> 🍃 MINT-1T is an open-source Multimodal INTerleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. 🍃 MINT-1T is created by a team from the University of Washington in collaboration with Salesforce Research, other academic institutions including Stanford University, University of Texas at Austin, and University of California Berkeley. You are currently viewing the HTML subset of 🍃 MINT-1T. For PDF and ArXiv subsets, please refer to the [🍃 MINT-1T collection](https://huggingface.co/collections/mlfoundations/mint-1t-6690216ca4d0df7e518dde1c). ![Examples](interleaved-example-twitter.png) ## Updates ### 9/7/24 We have improved MINT-1T (HTML) by removing boilerplate from the header and footer of each document. This new version of the data can be found in directory `data_v1_1` and contains 742B text tokens. The previous version of the data can be found in directory `data_v1_0`. ### 8/8/24 We have updated MINT-1T (HTML) with fixed document URL filtering and additional image safety filtering. As we prioritize safety, we have decided to only release the HTML data from MINT-1T that passes a rigorous image filtering pipeline; we run an additional image safety classifier, the one created by [Datacomp](https://www.datacomp.ai/dcclip/index.html#home), on data already filtered by our [original NSFW image classifier](https://github.com/GantMan/nsfw_model). The newly released MINT-1T (HTML) contains 792B text tokens and 905M documents. ## Dataset Details ### Dataset Sources - Repository: https://github.com/mlfoundations/MINT-1T - Paper:* https://arxiv.org/abs/2406.11271 - Blog: https://blog.salesforceairesearch.com/mint-1t/ ## Uses ### Direct Use <!-- This section describes suitable use cases for the dataset. --> 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. The dataset can be used for training multimodal models that can reson about interleaved text and images sequences such as [Idefics2](https://huggingface.co/HuggingFaceM4/idefics2-8b), [XGen-MM](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-r-v1), and [Chameleon](https://huggingface.co/facebook/chameleon-30b). ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> 🍃 MINT-1T was built to make research into large multimodal models more accessible. Using the dataset to train models that ingest or generate personally identifying information (such as images of people’s faces and other sensitive content) as well as military applications are all inappropriate use cases of 🍃 MINT-1T. ## Dataset Creation ### Curation Rationale 🍃 MINT-1T was created to address a significant gap in the open-source domain by providing a large-scale multimodal interleaved dataset for pre-training large multimodal models. This dataset aims to be a valuable resource for the research community, facilitating open science in multimodal pretraining. ### Source Data The dataset is a comprehensive collection of multimodal documents from various sources: - HTML documents: Filtered from CommonCrawl WARC dumps spanning from 2017 to 2024 - PDF documents: Extracted from CommonCrawl WAT dumps covering 2023 to 2024 - ArXiv documents: A subset of papers from the ArXiv repository In total, 🍃 MINT-1T contains 1056.8 million documents, broken down as follows: - 1029.4 million HTML documents - 24.0 million PDF documents - 0.6 million ArXiv documents #### Data Collection and Processing The data collection and processing involved several steps: 1. Document Extraction: - HTML documents were parsed from CommonCrawl WARC files - PDF documents were extracted from CommonCrawl WAT files - ArXiv papers were directly sourced from ArXiv S3 buckets 2. Filtering Process: - Applied text quality filters to ensure content relevance and readability - Removed duplicate content at both paragraph and document levels - Filtered out undesirable content based on predefined criteria - Verified image availability and quality for HTML documents - Limited PDF size to 50MB and 50 pages to manage dataset size and quality 3. Image Processing: - Used NSFW image detection to remove pornographic or otherwise undesirable images - Removed images smaller than 150 pixels or larger than 20,000 pixels - Adjusted aspect ratio thresholds for HTML (2:1) and PDF (3:1) to preserve scientific figures 4. Text Processing: - Used fasttext for language identification, focusing on English content - Masked personally identifiable information such as email addresses and IP addresses - Applied paragraph and document-level deduplication using Bloom filters 5. PDF Specific Processing: - Used PyMuPDF for parsing PDFs and extracting reading order - Clustered text blocks based on columns and ordered from top left to bottom right 6. ArXiv Specific Processing: - Used TexSoup to parse LaTeX source code and interleave images with text - Cleaned up LaTeX code by removing imports, bibliography, tables, and citation tags Various open-source tools were utilized in this process, including fasttext, [PyMuPDF](https://github.com/pymupdf/PyMuPDF), and [DCLM](https://www.datacomp.ai/dclm/) and [bff](https://github.com/revbucket/bff) for deduplication and content filtering. #### Personal and Sensitive Information Despite sourcing from public web data, significant efforts were made to minimize the inclusion of personal and sensitive information: - Email addresses and IP addresses were masked to protect privacy - An NSFW image classifierto remove inappropriate visual content - URLs containing substrings associated with undesirable or sensitive content were filtered out However, users should be aware that as the data originates from the public web, it may still contain some sensitive or personal information. The dataset creators acknowledge this limitation and advise users to exercise caution and potentially apply additional filtering based on their specific use cases. ## Bias, Risks, and Limitations Several potential biases, risks, and limitations have been identified: 1. Data Bias: As the dataset is sourced from web crawls, it may inherit biases present in online content. 2. Content Risks: Despite extensive filtering, there's a possibility that some offensive, insensitive, or inappropriate content may remain in the dataset. 3. Image Availability: The dataset relies on external image URLs, which may become unavailable over time due to link rot, potentially affecting the dataset's long-term usability. 4. PDF Parsing Limitations: The current method for extracting reading order from PDFs may not always accurately capture the intended flow, especially for documents with complex layouts. 5. Potential Legal and Ethical Concerns: While efforts were made to respect robots.txt files and remove sensitive information, there may still be content that individuals did not explicitly consent to include. ### Recommendations Given these considerations, the following recommendations are provided: 1. Additional Filtering: Users are strongly encouraged to apply additional filtering based on their specific use case and ethical considerations. 2. Inappropriate Use Cases: The dataset is not recommended for applications involving the processing or generation of personally identifying information, nor for military applications. 3. Legal Compliance: Users should independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. 4. Bias Awareness: Researchers and developers should be cognizant of potential biases in the dataset and consider their impact on model training and outputs. ## License We release 🍃 MINT-1T under a CC-BY-4.0 license, designating it primarily as a research artifact. While the dataset is freely available, users are responsible for ensuring its legal use in commercial settings. Users must independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. ## Citation ``` @article{awadalla2024mint1t, title={MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens}, author={Anas Awadalla and Le Xue and Oscar Lo and Manli Shu and Hannah Lee and Etash Kumar Guha and Matt Jordan and Sheng Shen and Mohamed Awadalla and Silvio Savarese and Caiming Xiong and Ran Xu and Yejin Choi and Ludwig Schmidt}, year={2024} } ```
mlfoundations/MINT-1T-ArXiv	mlfoundations	2024-09-19T21:32:59Z	13,702	47	[ "task_categories:image-to-text", "task_categories:text-generation", "language:en", "license:cc-by-4.0", "size_categories:1M<n<10M", "format:webdataset", "modality:image", "modality:text", "library:datasets", "library:webdataset", "library:mlcroissant", "arxiv:2406.11271", "region:us", "multimodal" ]	[ "image-to-text", "text-generation" ]	2024-06-29T23:50:55Z	null	--- license: cc-by-4.0 task_categories: - image-to-text - text-generation language: - en tags: - multimodal pretty_name: MINT-1T size_categories: - 100B<n<1T --- <h1 align="center"> 🍃 MINT-1T:<br>Scaling Open-Source Multimodal Data by 10x:<br> A Multimodal Dataset with One Trillion Tokens </h1> 🍃 MINT-1T is an open-source Multimodal INTerleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. 🍃 MINT-1T is created by a team from the University of Washington in collaboration with Salesforce Research, other academic institutions including Stanford University, University of Texas at Austin, and University of California Berkeley. You are currently viewing the ArXiv subset of 🍃 MINT-1T. For HTML and PDF subsets, please refer to the [🍃 MINT-1T collection](https://huggingface.co/collections/mlfoundations/mint-1t-6690216ca4d0df7e518dde1c). ![Examples](interleaved-example-twitter.png) ## Dataset Details ### Dataset Sources - Repository: https://github.com/mlfoundations/MINT-1T - Paper: https://arxiv.org/abs/2406.11271 - Blog: https://blog.salesforceairesearch.com/mint-1t/ ## Uses ### Direct Use <!-- This section describes suitable use cases for the dataset. --> 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. The dataset can be used for training multimodal models that can reson about interleaved text and images sequences such as [Idefics2](https://huggingface.co/HuggingFaceM4/idefics2-8b), [XGen-MM](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-r-v1), and [Chameleon](https://huggingface.co/facebook/chameleon-30b). ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> 🍃 MINT-1T was built to make research into large multimodal models more accessible. Using the dataset to train models that ingest or generate personally identifying information (such as images of people’s faces and other sensitive content) as well as military applications are all inappropriate use cases of 🍃 MINT-1T. ## Dataset Creation ### Curation Rationale 🍃 MINT-1T was created to address a significant gap in the open-source domain by providing a large-scale multimodal interleaved dataset for pre-training large multimodal models. This dataset aims to be a valuable resource for the research community, facilitating open science in multimodal pretraining. ### Source Data The dataset is a comprehensive collection of multimodal documents from various sources: - HTML documents: Filtered from CommonCrawl WARC dumps spanning from 2017 to 2024 - PDF documents: Extracted from CommonCrawl WAT dumps covering 2023 to 2024 - ArXiv documents: A subset of papers from the ArXiv repository In total, 🍃 MINT-1T contains 1056.8 million documents, broken down as follows: - 1029.4 million HTML documents - 24.0 million PDF documents - 0.6 million ArXiv documents #### Data Collection and Processing The data collection and processing involved several steps: 1. Document Extraction: - HTML documents were parsed from CommonCrawl WARC files - PDF documents were extracted from CommonCrawl WAT files - ArXiv papers were directly sourced from ArXiv S3 buckets 2. Filtering Process: - Applied text quality filters to ensure content relevance and readability - Removed duplicate content at both paragraph and document levels - Filtered out undesirable content based on predefined criteria - Verified image availability and quality for HTML documents - Limited PDF size to 50MB and 50 pages to manage dataset size and quality 3. Image Processing: - Used NSFW image detection to remove pornographic or otherwise undesirable images - Removed images smaller than 150 pixels or larger than 20,000 pixels - Adjusted aspect ratio thresholds for HTML (2:1) and PDF (3:1) to preserve scientific figures 4. Text Processing: - Used fasttext for language identification, focusing on English content - Masked personally identifiable information such as email addresses and IP addresses - Applied paragraph and document-level deduplication using Bloom filters 5. PDF Specific Processing: - Used PyMuPDF for parsing PDFs and extracting reading order - Clustered text blocks based on columns and ordered from top left to bottom right 6. ArXiv Specific Processing: - Used TexSoup to parse LaTeX source code and interleave images with text - Cleaned up LaTeX code by removing imports, bibliography, tables, and citation tags Various open-source tools were utilized in this process, including fasttext, [PyMuPDF](https://github.com/pymupdf/PyMuPDF), and [DCLM](https://www.datacomp.ai/dclm/) and [bff](https://github.com/revbucket/bff) for deduplication and content filtering. #### Personal and Sensitive Information Despite sourcing from public web data, significant efforts were made to minimize the inclusion of personal and sensitive information: - Email addresses and IP addresses were masked to protect privacy - An NSFW image classifierto remove inappropriate visual content - URLs containing substrings associated with undesirable or sensitive content were filtered out However, users should be aware that as the data originates from the public web, it may still contain some sensitive or personal information. The dataset creators acknowledge this limitation and advise users to exercise caution and potentially apply additional filtering based on their specific use cases. ## Bias, Risks, and Limitations Several potential biases, risks, and limitations have been identified: 1. Data Bias: As the dataset is sourced from web crawls, it may inherit biases present in online content. 2. Content Risks: Despite extensive filtering, there's a possibility that some offensive, insensitive, or inappropriate content may remain in the dataset. 3. Image Availability: The dataset relies on external image URLs, which may become unavailable over time due to link rot, potentially affecting the dataset's long-term usability. 4. PDF Parsing Limitations: The current method for extracting reading order from PDFs may not always accurately capture the intended flow, especially for documents with complex layouts. 5. Potential Legal and Ethical Concerns: While efforts were made to respect robots.txt files and remove sensitive information, there may still be content that individuals did not explicitly consent to include. ### Recommendations Given these considerations, the following recommendations are provided: 1. Additional Filtering: Users are strongly encouraged to apply additional filtering based on their specific use case and ethical considerations. 2. Inappropriate Use Cases: The dataset is not recommended for applications involving the processing or generation of personally identifying information, nor for military applications. 3. Legal Compliance: Users should independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. 4. Bias Awareness: Researchers and developers should be cognizant of potential biases in the dataset and consider their impact on model training and outputs. ## License We release 🍃 MINT-1T under a CC-BY-4.0 license, designating it primarily as a research artifact. While the dataset is freely available, users are responsible for ensuring its legal use in commercial settings. Users must independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. ## Citation ``` @article{awadalla2024mint1t, title={MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens}, author={Anas Awadalla and Le Xue and Oscar Lo and Manli Shu and Hannah Lee and Etash Kumar Guha and Matt Jordan and Sheng Shen and Mohamed Awadalla and Silvio Savarese and Caiming Xiong and Ran Xu and Yejin Choi and Ludwig Schmidt}, year={2024} } ```
mlfoundations/MINT-1T-PDF-CC-2023-14	mlfoundations	2024-09-19T21:07:39Z	63,544	1	[ "task_categories:image-to-text", "task_categories:text-generation", "language:en", "license:cc-by-4.0", "size_categories:1M<n<10M", "format:webdataset", "modality:image", "modality:text", "library:datasets", "library:webdataset", "library:mlcroissant", "arxiv:2406.11271", "region:us", "multimodal" ]	[ "image-to-text", "text-generation" ]	2024-07-12T05:44:44Z	null	--- license: cc-by-4.0 task_categories: - image-to-text - text-generation language: - en tags: - multimodal pretty_name: MINT-1T size_categories: - 100B<n<1T --- <h1 align="center"> 🍃 MINT-1T:<br>Scaling Open-Source Multimodal Data by 10x:<br> A Multimodal Dataset with One Trillion Tokens </h1> 🍃 MINT-1T is an open-source Multimodal INTerleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. 🍃 MINT-1T is created by a team from the University of Washington in collaboration with Salesforce Research, other academic institutions including Stanford University, University of Texas at Austin, and University of California Berkeley. You are currently viewing a subset of the PDF portion of 🍃 MINT-1T associated with CommonCrawl dump `CC-2023-14`. For other PDF, HTML, and ArXiv subsets, refer to the [🍃 MINT-1T collection](https://huggingface.co/collections/mlfoundations/mint-1t-6690216ca4d0df7e518dde1c). ![Examples](interleaved-example-twitter.png) ## Updates ### 9/19/24 We have removed roughly 10% of the PDF samples as there was a mismatch between the frames in the TIFF images and the document metadata. ### 8/8/24 We have become aware that the image hashes in the PDF subset of MINT-1T do not match the images in the documents. We want to emphasize that the images for each document are correct, and only the image hashes in the documents' metadata are mislabeled. ## Dataset Details ### Dataset Sources - Repository: https://github.com/mlfoundations/MINT-1T - Paper: https://arxiv.org/abs/2406.11271 - Blog: https://blog.salesforceairesearch.com/mint-1t/ ## Uses ### Direct Use <!-- This section describes suitable use cases for the dataset. --> 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. The dataset can be used for training multimodal models that can reson about interleaved text and images sequences such as [Idefics2](https://huggingface.co/HuggingFaceM4/idefics2-8b), [XGen-MM](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-r-v1), and [Chameleon](https://huggingface.co/facebook/chameleon-30b). ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> 🍃 MINT-1T was built to make research into large multimodal models more accessible. Using the dataset to train models that ingest or generate personally identifying information (such as images of people’s faces and other sensitive content) as well as military applications are all inappropriate use cases of 🍃 MINT-1T. ## Dataset Creation ### Curation Rationale 🍃 MINT-1T was created to address a significant gap in the open-source domain by providing a large-scale multimodal interleaved dataset for pre-training large multimodal models. This dataset aims to be a valuable resource for the research community, facilitating open science in multimodal pretraining. ### Source Data The dataset is a comprehensive collection of multimodal documents from various sources: - HTML documents: Filtered from CommonCrawl WARC dumps spanning from 2017 to 2024 - PDF documents: Extracted from CommonCrawl WAT dumps covering 2023 to 2024 - ArXiv documents: A subset of papers from the ArXiv repository In total, 🍃 MINT-1T contains 1056.8 million documents, broken down as follows: - 1029.4 million HTML documents - 24.0 million PDF documents - 0.6 million ArXiv documents #### Data Collection and Processing The data collection and processing involved several steps: 1. Document Extraction: - HTML documents were parsed from CommonCrawl WARC files - PDF documents were extracted from CommonCrawl WAT files - ArXiv papers were directly sourced from ArXiv S3 buckets 2. Filtering Process: - Applied text quality filters to ensure content relevance and readability - Removed duplicate content at both paragraph and document levels - Filtered out undesirable content based on predefined criteria - Verified image availability and quality for HTML documents - Limited PDF size to 50MB and 50 pages to manage dataset size and quality 3. Image Processing: - Used NSFW image detection to remove pornographic or otherwise undesirable images - Removed images smaller than 150 pixels or larger than 20,000 pixels - Adjusted aspect ratio thresholds for HTML (2:1) and PDF (3:1) to preserve scientific figures 4. Text Processing: - Used fasttext for language identification, focusing on English content - Masked personally identifiable information such as email addresses and IP addresses - Applied paragraph and document-level deduplication using Bloom filters 5. PDF Specific Processing: - Used PyMuPDF for parsing PDFs and extracting reading order - Clustered text blocks based on columns and ordered from top left to bottom right 6. ArXiv Specific Processing: - Used TexSoup to parse LaTeX source code and interleave images with text - Cleaned up LaTeX code by removing imports, bibliography, tables, and citation tags Various open-source tools were utilized in this process, including fasttext, [PyMuPDF](https://github.com/pymupdf/PyMuPDF), and [DCLM](https://www.datacomp.ai/dclm/) and [bff](https://github.com/revbucket/bff) for deduplication and content filtering. #### Personal and Sensitive Information Despite sourcing from public web data, significant efforts were made to minimize the inclusion of personal and sensitive information: - Email addresses and IP addresses were masked to protect privacy - An NSFW image classifierto remove inappropriate visual content - URLs containing substrings associated with undesirable or sensitive content were filtered out However, users should be aware that as the data originates from the public web, it may still contain some sensitive or personal information. The dataset creators acknowledge this limitation and advise users to exercise caution and potentially apply additional filtering based on their specific use cases. ## Bias, Risks, and Limitations Several potential biases, risks, and limitations have been identified: 1. Data Bias: As the dataset is sourced from web crawls, it may inherit biases present in online content. 2. Content Risks: Despite extensive filtering, there's a possibility that some offensive, insensitive, or inappropriate content may remain in the dataset. 3. Image Availability: The dataset relies on external image URLs, which may become unavailable over time due to link rot, potentially affecting the dataset's long-term usability. 4. PDF Parsing Limitations: The current method for extracting reading order from PDFs may not always accurately capture the intended flow, especially for documents with complex layouts. 5. Potential Legal and Ethical Concerns: While efforts were made to respect robots.txt files and remove sensitive information, there may still be content that individuals did not explicitly consent to include. ### Recommendations Given these considerations, the following recommendations are provided: 1. Additional Filtering: Users are strongly encouraged to apply additional filtering based on their specific use case and ethical considerations. 2. Inappropriate Use Cases: The dataset is not recommended for applications involving the processing or generation of personally identifying information, nor for military applications. 3. Legal Compliance: Users should independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. 4. Bias Awareness: Researchers and developers should be cognizant of potential biases in the dataset and consider their impact on model training and outputs. ## License We release 🍃 MINT-1T under a CC-BY-4.0 license, designating it primarily as a research artifact. While the dataset is freely available, users are responsible for ensuring its legal use in commercial settings. Users must independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. ## Citation ``` @article{awadalla2024mint1t, title={MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens}, author={Anas Awadalla and Le Xue and Oscar Lo and Manli Shu and Hannah Lee and Etash Kumar Guha and Matt Jordan and Sheng Shen and Mohamed Awadalla and Silvio Savarese and Caiming Xiong and Ran Xu and Yejin Choi and Ludwig Schmidt}, year={2024} } ```
mlfoundations/MINT-1T-PDF-CC-2023-40	mlfoundations	2024-09-19T21:06:59Z	52,154	1	[ "task_categories:image-to-text", "task_categories:text-generation", "language:en", "license:cc-by-4.0", "size_categories:100B<n<1T", "arxiv:2406.11271", "region:us", "multimodal" ]	[ "image-to-text", "text-generation" ]	2024-07-12T05:43:23Z	null	--- license: cc-by-4.0 task_categories: - image-to-text - text-generation language: - en tags: - multimodal pretty_name: MINT-1T size_categories: - 100B<n<1T --- <h1 align="center"> 🍃 MINT-1T:<br>Scaling Open-Source Multimodal Data by 10x:<br> A Multimodal Dataset with One Trillion Tokens </h1> 🍃 MINT-1T is an open-source Multimodal INTerleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. 🍃 MINT-1T is created by a team from the University of Washington in collaboration with Salesforce Research, other academic institutions including Stanford University, University of Texas at Austin, and University of California Berkeley. You are currently viewing a subset of the PDF portion of 🍃 MINT-1T associated with CommonCrawl dump `CC-2023-40`. For other PDF, HTML, and ArXiv subsets, refer to the [🍃 MINT-1T collection](https://huggingface.co/collections/mlfoundations/mint-1t-6690216ca4d0df7e518dde1c). ![Examples](interleaved-example-twitter.png) ## Updates ### 9/19/24 We have removed roughly 10% of the PDF samples as there was a mismatch between the frames in the TIFF images and the document metadata. ### 8/8/24 We have become aware that the image hashes in the PDF subset of MINT-1T do not match the images in the documents. We want to emphasize that the images for each document are correct, and only the image hashes in the documents' metadata are mislabeled. ## Dataset Details ### Dataset Sources - Repository: https://github.com/mlfoundations/MINT-1T - Paper: https://arxiv.org/abs/2406.11271 - Blog: https://blog.salesforceairesearch.com/mint-1t/ ## Uses ### Direct Use <!-- This section describes suitable use cases for the dataset. --> 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. The dataset can be used for training multimodal models that can reson about interleaved text and images sequences such as [Idefics2](https://huggingface.co/HuggingFaceM4/idefics2-8b), [XGen-MM](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-r-v1), and [Chameleon](https://huggingface.co/facebook/chameleon-30b). ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> 🍃 MINT-1T was built to make research into large multimodal models more accessible. Using the dataset to train models that ingest or generate personally identifying information (such as images of people’s faces and other sensitive content) as well as military applications are all inappropriate use cases of 🍃 MINT-1T. ## Dataset Creation ### Curation Rationale 🍃 MINT-1T was created to address a significant gap in the open-source domain by providing a large-scale multimodal interleaved dataset for pre-training large multimodal models. This dataset aims to be a valuable resource for the research community, facilitating open science in multimodal pretraining. ### Source Data The dataset is a comprehensive collection of multimodal documents from various sources: - HTML documents: Filtered from CommonCrawl WARC dumps spanning from 2017 to 2024 - PDF documents: Extracted from CommonCrawl WAT dumps covering 2023 to 2024 - ArXiv documents: A subset of papers from the ArXiv repository In total, 🍃 MINT-1T contains 1056.8 million documents, broken down as follows: - 1029.4 million HTML documents - 24.0 million PDF documents - 0.6 million ArXiv documents #### Data Collection and Processing The data collection and processing involved several steps: 1. Document Extraction: - HTML documents were parsed from CommonCrawl WARC files - PDF documents were extracted from CommonCrawl WAT files - ArXiv papers were directly sourced from ArXiv S3 buckets 2. Filtering Process: - Applied text quality filters to ensure content relevance and readability - Removed duplicate content at both paragraph and document levels - Filtered out undesirable content based on predefined criteria - Verified image availability and quality for HTML documents - Limited PDF size to 50MB and 50 pages to manage dataset size and quality 3. Image Processing: - Used NSFW image detection to remove pornographic or otherwise undesirable images - Removed images smaller than 150 pixels or larger than 20,000 pixels - Adjusted aspect ratio thresholds for HTML (2:1) and PDF (3:1) to preserve scientific figures 4. Text Processing: - Used fasttext for language identification, focusing on English content - Masked personally identifiable information such as email addresses and IP addresses - Applied paragraph and document-level deduplication using Bloom filters 5. PDF Specific Processing: - Used PyMuPDF for parsing PDFs and extracting reading order - Clustered text blocks based on columns and ordered from top left to bottom right 6. ArXiv Specific Processing: - Used TexSoup to parse LaTeX source code and interleave images with text - Cleaned up LaTeX code by removing imports, bibliography, tables, and citation tags Various open-source tools were utilized in this process, including fasttext, [PyMuPDF](https://github.com/pymupdf/PyMuPDF), and [DCLM](https://www.datacomp.ai/dclm/) and [bff](https://github.com/revbucket/bff) for deduplication and content filtering. #### Personal and Sensitive Information Despite sourcing from public web data, significant efforts were made to minimize the inclusion of personal and sensitive information: - Email addresses and IP addresses were masked to protect privacy - An NSFW image classifierto remove inappropriate visual content - URLs containing substrings associated with undesirable or sensitive content were filtered out However, users should be aware that as the data originates from the public web, it may still contain some sensitive or personal information. The dataset creators acknowledge this limitation and advise users to exercise caution and potentially apply additional filtering based on their specific use cases. ## Bias, Risks, and Limitations Several potential biases, risks, and limitations have been identified: 1. Data Bias: As the dataset is sourced from web crawls, it may inherit biases present in online content. 2. Content Risks: Despite extensive filtering, there's a possibility that some offensive, insensitive, or inappropriate content may remain in the dataset. 3. Image Availability: The dataset relies on external image URLs, which may become unavailable over time due to link rot, potentially affecting the dataset's long-term usability. 4. PDF Parsing Limitations: The current method for extracting reading order from PDFs may not always accurately capture the intended flow, especially for documents with complex layouts. 5. Potential Legal and Ethical Concerns: While efforts were made to respect robots.txt files and remove sensitive information, there may still be content that individuals did not explicitly consent to include. ### Recommendations Given these considerations, the following recommendations are provided: 1. Additional Filtering: Users are strongly encouraged to apply additional filtering based on their specific use case and ethical considerations. 2. Inappropriate Use Cases: The dataset is not recommended for applications involving the processing or generation of personally identifying information, nor for military applications. 3. Legal Compliance: Users should independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. 4. Bias Awareness: Researchers and developers should be cognizant of potential biases in the dataset and consider their impact on model training and outputs. ## License We release 🍃 MINT-1T under a CC-BY-4.0 license, designating it primarily as a research artifact. While the dataset is freely available, users are responsible for ensuring its legal use in commercial settings. Users must independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. ## Citation ``` @article{awadalla2024mint1t, title={MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens}, author={Anas Awadalla and Le Xue and Oscar Lo and Manli Shu and Hannah Lee and Etash Kumar Guha and Matt Jordan and Sheng Shen and Mohamed Awadalla and Silvio Savarese and Caiming Xiong and Ran Xu and Yejin Choi and Ludwig Schmidt}, year={2024} } ```
mlfoundations/MINT-1T-PDF-CC-2023-50	mlfoundations	2024-09-19T21:06:23Z	28,482	3	[ "task_categories:image-to-text", "task_categories:text-generation", "language:en", "license:cc-by-4.0", "size_categories:1M<n<10M", "format:webdataset", "modality:image", "modality:text", "library:datasets", "library:webdataset", "library:mlcroissant", "arxiv:2406.11271", "region:us", "multimodal" ]	[ "image-to-text", "text-generation" ]	2024-07-12T05:42:22Z	null	--- license: cc-by-4.0 task_categories: - image-to-text - text-generation language: - en tags: - multimodal pretty_name: MINT-1T size_categories: - 100B<n<1T --- <h1 align="center"> 🍃 MINT-1T:<br>Scaling Open-Source Multimodal Data by 10x:<br> A Multimodal Dataset with One Trillion Tokens </h1> 🍃 MINT-1T is an open-source Multimodal INTerleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. 🍃 MINT-1T is created by a team from the University of Washington in collaboration with Salesforce Research, other academic institutions including Stanford University, University of Texas at Austin, and University of California Berkeley. You are currently viewing a subset of the PDF portion of 🍃 MINT-1T associated with CommonCrawl dump `CC-2023-50`. For other PDF, HTML, and ArXiv subsets, refer to the [🍃 MINT-1T collection](https://huggingface.co/collections/mlfoundations/mint-1t-6690216ca4d0df7e518dde1c). ![Examples](interleaved-example-twitter.png) ## Updates ### 9/19/24 We have removed roughly 10% of the PDF samples as there was a mismatch between the frames in the TIFF images and the document metadata. ### 8/8/24 We have become aware that the image hashes in the PDF subset of MINT-1T do not match the images in the documents. We want to emphasize that the images for each document are correct, and only the image hashes in the documents' metadata are mislabeled. ## Dataset Details ### Dataset Sources - Repository: https://github.com/mlfoundations/MINT-1T - Paper: https://arxiv.org/abs/2406.11271 - Blog: https://blog.salesforceairesearch.com/mint-1t/ ## Uses ### Direct Use <!-- This section describes suitable use cases for the dataset. --> 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. The dataset can be used for training multimodal models that can reson about interleaved text and images sequences such as [Idefics2](https://huggingface.co/HuggingFaceM4/idefics2-8b), [XGen-MM](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-r-v1), and [Chameleon](https://huggingface.co/facebook/chameleon-30b). ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> 🍃 MINT-1T was built to make research into large multimodal models more accessible. Using the dataset to train models that ingest or generate personally identifying information (such as images of people’s faces and other sensitive content) as well as military applications are all inappropriate use cases of 🍃 MINT-1T. ## Dataset Creation ### Curation Rationale 🍃 MINT-1T was created to address a significant gap in the open-source domain by providing a large-scale multimodal interleaved dataset for pre-training large multimodal models. This dataset aims to be a valuable resource for the research community, facilitating open science in multimodal pretraining. ### Source Data The dataset is a comprehensive collection of multimodal documents from various sources: - HTML documents: Filtered from CommonCrawl WARC dumps spanning from 2017 to 2024 - PDF documents: Extracted from CommonCrawl WAT dumps covering 2023 to 2024 - ArXiv documents: A subset of papers from the ArXiv repository In total, 🍃 MINT-1T contains 1056.8 million documents, broken down as follows: - 1029.4 million HTML documents - 24.0 million PDF documents - 0.6 million ArXiv documents #### Data Collection and Processing The data collection and processing involved several steps: 1. Document Extraction: - HTML documents were parsed from CommonCrawl WARC files - PDF documents were extracted from CommonCrawl WAT files - ArXiv papers were directly sourced from ArXiv S3 buckets 2. Filtering Process: - Applied text quality filters to ensure content relevance and readability - Removed duplicate content at both paragraph and document levels - Filtered out undesirable content based on predefined criteria - Verified image availability and quality for HTML documents - Limited PDF size to 50MB and 50 pages to manage dataset size and quality 3. Image Processing: - Used NSFW image detection to remove pornographic or otherwise undesirable images - Removed images smaller than 150 pixels or larger than 20,000 pixels - Adjusted aspect ratio thresholds for HTML (2:1) and PDF (3:1) to preserve scientific figures 4. Text Processing: - Used fasttext for language identification, focusing on English content - Masked personally identifiable information such as email addresses and IP addresses - Applied paragraph and document-level deduplication using Bloom filters 5. PDF Specific Processing: - Used PyMuPDF for parsing PDFs and extracting reading order - Clustered text blocks based on columns and ordered from top left to bottom right 6. ArXiv Specific Processing: - Used TexSoup to parse LaTeX source code and interleave images with text - Cleaned up LaTeX code by removing imports, bibliography, tables, and citation tags Various open-source tools were utilized in this process, including fasttext, [PyMuPDF](https://github.com/pymupdf/PyMuPDF), and [DCLM](https://www.datacomp.ai/dclm/) and [bff](https://github.com/revbucket/bff) for deduplication and content filtering. #### Personal and Sensitive Information Despite sourcing from public web data, significant efforts were made to minimize the inclusion of personal and sensitive information: - Email addresses and IP addresses were masked to protect privacy - An NSFW image classifierto remove inappropriate visual content - URLs containing substrings associated with undesirable or sensitive content were filtered out However, users should be aware that as the data originates from the public web, it may still contain some sensitive or personal information. The dataset creators acknowledge this limitation and advise users to exercise caution and potentially apply additional filtering based on their specific use cases. ## Bias, Risks, and Limitations Several potential biases, risks, and limitations have been identified: 1. Data Bias: As the dataset is sourced from web crawls, it may inherit biases present in online content. 2. Content Risks: Despite extensive filtering, there's a possibility that some offensive, insensitive, or inappropriate content may remain in the dataset. 3. Image Availability: The dataset relies on external image URLs, which may become unavailable over time due to link rot, potentially affecting the dataset's long-term usability. 4. PDF Parsing Limitations: The current method for extracting reading order from PDFs may not always accurately capture the intended flow, especially for documents with complex layouts. 5. Potential Legal and Ethical Concerns: While efforts were made to respect robots.txt files and remove sensitive information, there may still be content that individuals did not explicitly consent to include. ### Recommendations Given these considerations, the following recommendations are provided: 1. Additional Filtering: Users are strongly encouraged to apply additional filtering based on their specific use case and ethical considerations. 2. Inappropriate Use Cases: The dataset is not recommended for applications involving the processing or generation of personally identifying information, nor for military applications. 3. Legal Compliance: Users should independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. 4. Bias Awareness: Researchers and developers should be cognizant of potential biases in the dataset and consider their impact on model training and outputs. ## License We release 🍃 MINT-1T under a CC-BY-4.0 license, designating it primarily as a research artifact. While the dataset is freely available, users are responsible for ensuring its legal use in commercial settings. Users must independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. ## Citation ``` @article{awadalla2024mint1t, title={MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens}, author={Anas Awadalla and Le Xue and Oscar Lo and Manli Shu and Hannah Lee and Etash Kumar Guha and Matt Jordan and Sheng Shen and Mohamed Awadalla and Silvio Savarese and Caiming Xiong and Ran Xu and Yejin Choi and Ludwig Schmidt}, year={2024} } ```
mlfoundations/MINT-1T-PDF-CC-2024-18	mlfoundations	2024-09-19T21:02:55Z	41,126	19	[ "task_categories:image-to-text", "task_categories:text-generation", "language:en", "license:cc-by-4.0", "size_categories:100B<n<1T", "arxiv:2406.11271", "region:us", "multimodal" ]	[ "image-to-text", "text-generation" ]	2024-07-15T03:19:33Z	null	--- license: cc-by-4.0 task_categories: - image-to-text - text-generation language: - en tags: - multimodal pretty_name: MINT-1T size_categories: - 100B<n<1T configs: - config_name: default data_files: - split: train path: CC-MAIN-/ --- <h1 align="center"> 🍃 MINT-1T:<br>Scaling Open-Source Multimodal Data by 10x:<br> A Multimodal Dataset with One Trillion Tokens </h1> 🍃 MINT-1T is an open-source Multimodal INTerleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. 🍃 MINT-1T is created by a team from the University of Washington in collaboration with Salesforce Research, other academic institutions including Stanford University, University of Texas at Austin, and University of California Berkeley. You are currently viewing a subset of the PDF portion of 🍃 MINT-1T associated with CommonCrawl dump `CC-2024-18`. For other PDF, HTML, and ArXiv subsets, refer to the [🍃 MINT-1T collection](https://huggingface.co/collections/mlfoundations/mint-1t-6690216ca4d0df7e518dde1c). ![Examples](interleaved-example-twitter.png) ## Updates ### 9/19/24 We have removed roughly 10% of the PDF samples as there was a mismatch between the frames in the TIFF images and the document metadata. ### 8/8/24 We have become aware that the image hashes in the PDF subset of MINT-1T do not match the images in the documents. We want to emphasize that the images for each document are correct, and only the image hashes in the documents' metadata are mislabeled. ## Dataset Details ### Dataset Sources - Repository: https://github.com/mlfoundations/MINT-1T - Paper: https://arxiv.org/abs/2406.11271 - Blog: https://blog.salesforceairesearch.com/mint-1t/ ## Uses ### Direct Use <!-- This section describes suitable use cases for the dataset. --> 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. The dataset can be used for training multimodal models that can reson about interleaved text and images sequences such as [Idefics2](https://huggingface.co/HuggingFaceM4/idefics2-8b), [XGen-MM](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-r-v1), and [Chameleon](https://huggingface.co/facebook/chameleon-30b). ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> 🍃 MINT-1T was built to make research into large multimodal models more accessible. Using the dataset to train models that ingest or generate personally identifying information (such as images of people’s faces and other sensitive content) as well as military applications are all inappropriate use cases of 🍃 MINT-1T. ## Dataset Creation ### Curation Rationale 🍃 MINT-1T was created to address a significant gap in the open-source domain by providing a large-scale multimodal interleaved dataset for pre-training large multimodal models. This dataset aims to be a valuable resource for the research community, facilitating open science in multimodal pretraining. ### Source Data The dataset is a comprehensive collection of multimodal documents from various sources: - HTML documents: Filtered from CommonCrawl WARC dumps spanning from 2017 to 2024 - PDF documents: Extracted from CommonCrawl WAT dumps covering 2023 to 2024 - ArXiv documents: A subset of papers from the ArXiv repository In total, 🍃 MINT-1T contains 1056.8 million documents, broken down as follows: - 1029.4 million HTML documents - 24.0 million PDF documents - 0.6 million ArXiv documents #### Data Collection and Processing The data collection and processing involved several steps: 1. Document Extraction: - HTML documents were parsed from CommonCrawl WARC files - PDF documents were extracted from CommonCrawl WAT files - ArXiv papers were directly sourced from ArXiv S3 buckets 2. Filtering Process: - Applied text quality filters to ensure content relevance and readability - Removed duplicate content at both paragraph and document levels - Filtered out undesirable content based on predefined criteria - Verified image availability and quality for HTML documents - Limited PDF size to 50MB and 50 pages to manage dataset size and quality 3. Image Processing: - Used NSFW image detection to remove pornographic or otherwise undesirable images - Removed images smaller than 150 pixels or larger than 20,000 pixels - Adjusted aspect ratio thresholds for HTML (2:1) and PDF (3:1) to preserve scientific figures 4. Text Processing: - Used fasttext for language identification, focusing on English content - Masked personally identifiable information such as email addresses and IP addresses - Applied paragraph and document-level deduplication using Bloom filters 5. PDF Specific Processing: - Used PyMuPDF for parsing PDFs and extracting reading order - Clustered text blocks based on columns and ordered from top left to bottom right 6. ArXiv Specific Processing: - Used TexSoup to parse LaTeX source code and interleave images with text - Cleaned up LaTeX code by removing imports, bibliography, tables, and citation tags Various open-source tools were utilized in this process, including fasttext, [PyMuPDF](https://github.com/pymupdf/PyMuPDF), and [DCLM](https://www.datacomp.ai/dclm/) and [bff](https://github.com/revbucket/bff) for deduplication and content filtering. #### Personal and Sensitive Information Despite sourcing from public web data, significant efforts were made to minimize the inclusion of personal and sensitive information: - Email addresses and IP addresses were masked to protect privacy - An NSFW image classifierto remove inappropriate visual content - URLs containing substrings associated with undesirable or sensitive content were filtered out However, users should be aware that as the data originates from the public web, it may still contain some sensitive or personal information. The dataset creators acknowledge this limitation and advise users to exercise caution and potentially apply additional filtering based on their specific use cases. ## Bias, Risks, and Limitations Several potential biases, risks, and limitations have been identified: 1. Data Bias: As the dataset is sourced from web crawls, it may inherit biases present in online content. 2. Content Risks: Despite extensive filtering, there's a possibility that some offensive, insensitive, or inappropriate content may remain in the dataset. 3. Image Availability: The dataset relies on external image URLs, which may become unavailable over time due to link rot, potentially affecting the dataset's long-term usability. 4. PDF Parsing Limitations: The current method for extracting reading order from PDFs may not always accurately capture the intended flow, especially for documents with complex layouts. 5. Potential Legal and Ethical Concerns: While efforts were made to respect robots.txt files and remove sensitive information, there may still be content that individuals did not explicitly consent to include. ### Recommendations Given these considerations, the following recommendations are provided: 1. Additional Filtering: Users are strongly encouraged to apply additional filtering based on their specific use case and ethical considerations. 2. Inappropriate Use Cases: The dataset is not recommended for applications involving the processing or generation of personally identifying information, nor for military applications. 3. Legal Compliance: Users should independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. 4. Bias Awareness: Researchers and developers should be cognizant of potential biases in the dataset and consider their impact on model training and outputs. ## License We release 🍃 MINT-1T under a CC-BY-4.0 license, designating it primarily as a research artifact. While the dataset is freely available, users are responsible for ensuring its legal use in commercial settings. Users must independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. ## Citation ``` @article{awadalla2024mint1t, title={MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens}, author={Anas Awadalla and Le Xue and Oscar Lo and Manli Shu and Hannah Lee and Etash Kumar Guha and Matt Jordan and Sheng Shen and Mohamed Awadalla and Silvio Savarese and Caiming Xiong and Ran Xu and Yejin Choi and Ludwig Schmidt}, year={2024} } ```
MMMU/MMMU	MMMU	2024-09-19T17:11:03Z	17,607	251	[ "task_categories:question-answering", "task_categories:visual-question-answering", "task_categories:multiple-choice", "language:en", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2311.16502", "region:us", "biology", "medical", "finance", "chemistry", "music", "art", "art_theory", "design", "business", "accounting", "economics", "manage", "marketing", "health", "medicine", "basic_medical_science", "clinical", "pharmacy", "public_health", "humanities", "social_science", "history", "literature", "sociology", "psychology", "science", "geography", "math", "physics", "engineering", "agriculture", "architecture", "computer_science", "electronics", "energy_and_power", "materials", "mechanical_engineering" ]	[ "question-answering", "visual-question-answering", "multiple-choice" ]	2023-11-27T17:52:01Z	null	--- language: - en license: apache-2.0 size_categories: - 10K<n<100K task_categories: - question-answering - visual-question-answering - multiple-choice pretty_name: mmmu dataset_info: - config_name: Accounting features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 262599.0 num_examples: 5 - name: validation num_bytes: 1598285.0 num_examples: 30 - name: test num_bytes: 22135625.0 num_examples: 380 download_size: 37363379 dataset_size: 23996509.0 - config_name: Agriculture features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 22082656.0 num_examples: 5 - name: validation num_bytes: 119217558.0 num_examples: 30 - name: test num_bytes: 993664077.0 num_examples: 287 download_size: 1158036990 dataset_size: 1134964291.0 - config_name: Architecture_and_Engineering features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - 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name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 152542.0 num_examples: 5 - name: validation num_bytes: 874988.0 num_examples: 30 - name: test num_bytes: 15093746.0 num_examples: 429 download_size: 30450114 dataset_size: 16121276.0 - config_name: Music features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 1417615.0 num_examples: 5 - name: validation num_bytes: 9359372.0 num_examples: 30 - name: test num_bytes: 134096770.0 num_examples: 334 download_size: 174725052 dataset_size: 144873757.0 - config_name: Pharmacy features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 207924.0 num_examples: 5 - name: validation num_bytes: 1656342.0 num_examples: 30 - name: test num_bytes: 31866248.0 num_examples: 430 download_size: 62721263 dataset_size: 33730514.0 - config_name: Physics features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 233734.0 num_examples: 5 - name: validation num_bytes: 1114130.0 num_examples: 30 - name: test num_bytes: 15905705.0 num_examples: 408 download_size: 35238571 dataset_size: 17253569.0 - config_name: Psychology features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 600864.0 num_examples: 5 - name: validation num_bytes: 4403886.0 num_examples: 30 - name: test num_bytes: 53813915.0 num_examples: 305 download_size: 102466671 dataset_size: 58818665.0 - config_name: Public_Health features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 234781.0 num_examples: 5 - name: validation num_bytes: 1508761.0 num_examples: 30 - name: test num_bytes: 32150088.0 num_examples: 509 download_size: 48231609 dataset_size: 33893630.0 - config_name: Sociology features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 3769220.0 num_examples: 5 - name: validation num_bytes: 18455336.0 num_examples: 30 - name: test num_bytes: 144301123.0 num_examples: 252 download_size: 310313826 dataset_size: 166525679.0 configs: - config_name: Accounting data_files: - split: dev path: Accounting/dev-* - split: validation path: Accounting/validation-* - split: test path: Accounting/test-* - config_name: Agriculture data_files: - split: dev path: Agriculture/dev-* - split: validation path: Agriculture/validation-* - split: test path: Agriculture/test-* - config_name: Architecture_and_Engineering data_files: - split: dev path: Architecture_and_Engineering/dev-* - split: validation path: Architecture_and_Engineering/validation-* - split: test path: Architecture_and_Engineering/test-* - config_name: Art data_files: - split: dev path: Art/dev-* - split: validation path: Art/validation-* - split: test path: Art/test-* - config_name: Art_Theory data_files: - split: dev path: Art_Theory/dev-* - split: validation path: Art_Theory/validation-* - split: test path: Art_Theory/test-* - config_name: Basic_Medical_Science data_files: - split: dev path: Basic_Medical_Science/dev-* - split: validation path: Basic_Medical_Science/validation-* - split: test path: Basic_Medical_Science/test-* - config_name: Biology data_files: - split: dev path: Biology/dev-* - split: validation path: Biology/validation-* - split: test path: Biology/test-* - config_name: Chemistry data_files: - split: dev path: Chemistry/dev-* - split: validation path: Chemistry/validation-* - split: test path: Chemistry/test-* - config_name: Clinical_Medicine data_files: - split: dev path: Clinical_Medicine/dev-* - split: validation path: Clinical_Medicine/validation-* - split: test path: Clinical_Medicine/test-* - config_name: Computer_Science data_files: - split: dev path: Computer_Science/dev-* - split: validation path: Computer_Science/validation-* - split: test path: Computer_Science/test-* - config_name: Design data_files: - split: dev path: Design/dev-* - split: validation path: Design/validation-* - split: test path: Design/test-* - config_name: Diagnostics_and_Laboratory_Medicine data_files: - split: dev path: Diagnostics_and_Laboratory_Medicine/dev-* - split: validation path: Diagnostics_and_Laboratory_Medicine/validation-* - split: test path: Diagnostics_and_Laboratory_Medicine/test-* - config_name: Economics data_files: - split: dev path: Economics/dev-* - split: validation path: Economics/validation-* - split: test path: Economics/test-* - config_name: Electronics data_files: - split: dev path: Electronics/dev-* - split: validation path: Electronics/validation-* - split: test path: Electronics/test-* - config_name: Energy_and_Power data_files: - split: dev path: Energy_and_Power/dev-* - split: validation path: Energy_and_Power/validation-* - split: test path: Energy_and_Power/test-* - config_name: Finance data_files: - split: dev path: Finance/dev-* - split: validation path: Finance/validation-* - split: test path: Finance/test-* - config_name: Geography data_files: - split: dev path: Geography/dev-* - split: validation path: Geography/validation-* - split: test path: Geography/test-* - config_name: History data_files: - split: dev path: History/dev-* - split: validation path: History/validation-* - split: test path: History/test-* - config_name: Literature data_files: - split: dev path: Literature/dev-* - split: validation path: Literature/validation-* - split: test path: Literature/test-* - config_name: Manage data_files: - split: dev path: Manage/dev-* - split: validation path: Manage/validation-* - split: test path: Manage/test-* - config_name: Marketing data_files: - split: dev path: Marketing/dev-* - split: validation path: Marketing/validation-* - split: test path: Marketing/test-* - config_name: Materials data_files: - split: dev path: Materials/dev-* - split: validation path: Materials/validation-* - split: test path: Materials/test-* - config_name: Math data_files: - split: dev path: Math/dev-* - split: validation path: Math/validation-* - split: test path: Math/test-* - config_name: Mechanical_Engineering data_files: - split: dev path: Mechanical_Engineering/dev-* - split: validation path: Mechanical_Engineering/validation-* - split: test path: Mechanical_Engineering/test-* - config_name: Music data_files: - split: dev path: Music/dev-* - split: validation path: Music/validation-* - split: test path: Music/test-* - config_name: Pharmacy data_files: - split: dev path: Pharmacy/dev-* - split: validation path: Pharmacy/validation-* - split: test path: Pharmacy/test-* - config_name: Physics data_files: - split: dev path: Physics/dev-* - split: validation path: Physics/validation-* - split: test path: Physics/test-* - config_name: Psychology data_files: - split: dev path: Psychology/dev-* - split: validation path: Psychology/validation-* - split: test path: Psychology/test-* - config_name: Public_Health data_files: - split: dev path: Public_Health/dev-* - split: validation path: Public_Health/validation-* - split: test path: Public_Health/test-* - config_name: Sociology data_files: - split: dev path: Sociology/dev-* - split: validation path: Sociology/validation-* - split: test path: Sociology/test-* tags: - biology - medical - finance - chemistry - music - art - art_theory - design - music - business - accounting - economics - finance - manage - marketing - health - medicine - basic_medical_science - clinical - pharmacy - public_health - humanities - social_science - history - literature - sociology - psychology - science - biology - chemistry - geography - math - physics - engineering - agriculture - architecture - computer_science - electronics - energy_and_power - materials - mechanical_engineering --- # MMMU (A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI) [🌐 Homepage](https://mmmu-benchmark.github.io/) \| [🏆 Leaderboard](https://mmmu-benchmark.github.io/#leaderboard) \| [🤗 Dataset](https://huggingface.co/datasets/MMMU/MMMU/) \| [🤗 Paper](https://huggingface.co/papers/2311.16502) \| [📖 arXiv](https://arxiv.org/abs/2311.16502) \| [GitHub](https://github.com/MMMU-Benchmark/MMMU) ## 🔔News - 🛠️[2024-05-30]: Fixed duplicate option issues in Materials dataset items (validation_Materials_25; test_Materials_17, 242) and content error in validation_Materials_25. - 🛠️[2024-04-30]: Fixed missing "-" or "^" signs in Math dataset items (dev_Math_2, validation_Math_11, 12, 16; test_Math_8, 23, 43, 113, 164, 223, 236, 287, 329, 402, 498) and corrected option errors in validation_Math_2. If you encounter any issues with the dataset, please contact us promptly! - 🚀[2024-01-31]: We added Human Expert performance on the [Leaderboard](https://mmmu-benchmark.github.io/#leaderboard)!🌟 - 🔥[2023-12-04]: Our evaluation server for test set is now availble on [EvalAI](https://eval.ai/web/challenges/challenge-page/2179/overview). We welcome all submissions and look forward to your participation! 😆 ## Dataset Details ### Dataset Description We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning. MMMU includes 11.5K meticulously collected multimodal questions from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering. These questions span 30 subjects and 183 subfields, comprising 30 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures. We believe MMMU will stimulate the community to build next-generation multimodal foundation models towards expert artificial general intelligence (AGI). 🎯 We have released a full set comprising 150 development samples and 900 validation samples. We have released 10,500 test questions without their answers. The development set is used for few-shot/in-context learning, and the validation set is used for debugging models, selecting hyperparameters, or quick evaluations. The answers and explanations for the test set questions are withheld. You can submit your model's predictions for the test set on [EvalAI](https://eval.ai/web/challenges/challenge-page/2179/overview). ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6230d750d93e84e233882dbc/2Ulh9yznm1dvISV4xJ_Ok.png) ### Dataset Creation MMMU was created to challenge multimodal models with tasks that demand college-level subject knowledge and deliberate reasoning, pushing the boundaries of what these models can achieve in terms of expert-level perception and reasoning. The data for the MMMU dataset was manually collected by a team of college students from various disciplines, using online sources, textbooks, and lecture materials. - Content: The dataset contains 11.5K college-level problems across six broad disciplines (Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, Tech & Engineering) and 30 college subjects. - Image Types: The dataset includes 30 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures, interleaved with text. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6230d750d93e84e233882dbc/Mbf8O5lEH8I8czprch0AG.png) ## 🏆 Mini-Leaderboard We show a mini-leaderboard here and please find more information in our paper or [homepage](https://mmmu-benchmark.github.io/). \| Model \| Val (900) \| Test (10.5K) \| \|--------------------------------\|:---------:\|:------------:\| \| Expert (Best) \| 88.6 \| - \| \| Expert (Medium) \| 82.6 \| - \| \| Expert (Worst) \| 76.2 \| - \| \| GPT-4o* \| 69.1 \| - \| \| Gemini 1.5 Pro* \| 62.2 \| - \| \| InternVL2-Pro* \| 62.0 \| 55.7 \| \| Gemini 1.0 Ultra* \| 59.4 \| - \| \| Claude 3 Opus* \| 59.4 \| - \| \| GPT-4V(ision) (Playground) \| 56.8 \| 55.7 \| \| Reka Core* \| 56.3 \| - \| \| Gemini 1.5 Flash* \| 56.1 \| - \| \| SenseChat-Vision-0423-Preview* \| 54.6 \| 50.3 \| \| Reka Flash* \| 53.3 \| - \| \| Claude 3 Sonnet* \| 53.1 \| - \| \| HPT Pro* \| 52.0 \| - \| \| VILA1.5* \| 51.9 \| 46.9 \| \| Qwen-VL-MAX* \| 51.4 \| 46.8 \| \| InternVL-Chat-V1.2* \| 51.6 \| 46.2 \| \| Skywork-VL* \| 51.4 \| 46.2 \| \| LLaVA-1.6-34B* \| 51.1 \| 44.7 \| \| Claude 3 Haiku* \| 50.2 \| - \| \| Adept Fuyu-Heavy* \| 48.3 \| - \| \| Gemini 1.0 Pro* \| 47.9 \| - \| \| Marco-VL-Plus* \| 46.2 \| 44.3 \| \| Yi-VL-34B* \| 45.9 \| 41.6 \| \| Qwen-VL-PLUS* \| 45.2 \| 40.8 \| \| HPT Air* \| 44.0 \| - \| \| Reka Edge* \| 42.8 \| - \| \| Marco-VL* \| 41.2 \| 40.4 \| \| OmniLMM-12B* \| 41.1 \| 40.4 \| \| Bunny-8B* \| 43.3 \| 39.0 \| \| Bunny-4B* \| 41.4 \| 38.4 \| \| Weitu-VL-1.0-15B* \| - \| 38.4 \| \| InternLM-XComposer2-VL* \| 43.0 \| 38.2 \| \| Yi-VL-6B* \| 39.1 \| 37.8 \| \| InfiMM-Zephyr-7B* \| 39.4 \| 35.5 \| \| InternVL-Chat-V1.1* \| 39.1 \| 35.3 \| \| Math-LLaVA-13B* \| 38.3 \| 34.6 \| \| SVIT* \| 38.0 \| 34.1 \| \| MiniCPM-V* \| 37.2 \| 34.1 \| \| MiniCPM-V-2* \| 37.1 \| - \| \| Emu2-Chat* \| 36.3 \| 34.1 \| \| BLIP-2 FLAN-T5-XXL \| 35.4 \| 34.0 \| \| InstructBLIP-T5-XXL \| 35.7 \| 33.8 \| \| LLaVA-1.5-13B \| 36.4 \| 33.6 \| \| Bunny-3B* \| 38.2 \| 33.0 \| \| Qwen-VL-7B-Chat \| 35.9 \| 32.9 \| \| SPHINX* \| 32.9 \| 32.9 \| \| mPLUG-OWL2* \| 32.7 \| 32.1 \| \| BLIP-2 FLAN-T5-XL \| 34.4 \| 31.0 \| \| InstructBLIP-T5-XL \| 32.9 \| 30.6 \| \| Gemini Nano2* \| 32.6 \| - \| \| CogVLM \| 32.1 \| 30.1 \| \| Otter \| 32.2 \| 29.1 \| \| LLaMA-Adapter2-7B \| 29.8 \| 27.7 \| \| MiniGPT4-Vicuna-13B \| 26.8 \| 27.6 \| \| Adept Fuyu-8B \| 27.9 \| 27.4 \| \| Kosmos2 \| 24.4 \| 26.6 \| \| OpenFlamingo2-9B \| 28.7 \| 26.3 \| \| Frequent Choice \| 22.1 \| 23.9 \| \| Random Choice \| 26.8 \| 25.8 \| : results provided by the authors. ## Limitations Despite its comprehensive nature, MMMU, like any benchmark, is not without limitations. The manual curation process, albeit thorough, may carry biases. And the focus on college-level subjects might not fully be a sufficient test for Expert AGI. However, we believe it should be necessary for an Expert AGI to achieve strong performance on MMMU to demonstrate their broad and deep subject knowledge as well as expert-level understanding and reasoning capabilities. In future work, we plan to incorporate human evaluations into MMMU. This will provide a more grounded comparison between model capabilities and expert performance, shedding light on the proximity of current AI systems to achieving Expert AGI. ## Disclaimers The guidelines for the annotators emphasized strict compliance with copyright and licensing rules from the initial data source, specifically avoiding materials from websites that forbid copying and redistribution. Should you encounter any data samples potentially breaching the copyright or licensing regulations of any site, we encourage you to notify us. Upon verification, such samples will be promptly removed. ## Contact - Xiang Yue: [email protected] - Yu Su: [email protected] - Wenhu Chen: [email protected] ## Citation BibTeX:* ```bibtex @inproceedings{yue2023mmmu, title={MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI}, author={Xiang Yue and Yuansheng Ni and Kai Zhang and Tianyu Zheng and Ruoqi Liu and Ge Zhang and Samuel Stevens and Dongfu Jiang and Weiming Ren and Yuxuan Sun and Cong Wei and Botao Yu and Ruibin Yuan and Renliang Sun and Ming Yin and Boyuan Zheng and Zhenzhu Yang and Yibo Liu and Wenhao Huang and Huan Sun and Yu Su and Wenhu Chen}, booktitle={Proceedings of CVPR}, year={2024}, } ```
IGNF/FLAIR_1_osm_clip	IGNF	2024-09-18T09:05:07Z	16,806	5	[ "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "IGN" ]	[]	2023-04-27T12:28:28Z	null	--- size_categories: - 10K<n<100K dataset_info: features: - name: image dtype: image - name: segmentation dtype: image - name: metadata struct: - name: address struct: - name: ISO3166-2-lvl4 dtype: string - name: ISO3166-2-lvl6 dtype: string - name: ISO3166-2-lvl7 dtype: string - name: aerialway dtype: string - name: aeroway dtype: string - name: allotments dtype: string - name: amenity dtype: string - name: bridge dtype: string - name: building dtype: string - name: city dtype: string - name: city_district dtype: string - name: club dtype: string - name: commercial dtype: string - name: country dtype: string - name: country_code dtype: string - name: county dtype: string - name: craft dtype: string - name: emergency dtype: string - name: farm dtype: string - name: farmyard dtype: string - name: hamlet dtype: string - name: healthcare dtype: string - name: highway dtype: string - name: historic dtype: string - name: house_number dtype: string - name: industrial dtype: string - name: isolated_dwelling dtype: string - name: junction dtype: string - name: landuse dtype: string - name: leisure dtype: string - name: locality dtype: string - name: man_made dtype: string - name: military dtype: string - name: municipality dtype: string - name: natural dtype: string - name: neighbourhood dtype: string - name: office dtype: string - name: place dtype: string - name: postcode dtype: string - name: province dtype: string - name: quarter dtype: string - name: railway dtype: string - name: region dtype: string - name: residential dtype: string - name: retail dtype: string - name: road dtype: string - name: shop dtype: string - name: square dtype: string - name: state dtype: string - name: state_district dtype: string - name: suburb dtype: string - name: tourism dtype: string - name: town dtype: string - name: village dtype: string - name: boundingbox sequence: string - name: camera dtype: string - name: code dtype: string - name: date dtype: string - name: dimensions sequence: int64 - name: display_name dtype: string - name: domain dtype: string - name: lat dtype: string - name: latlong sequence: float64 - name: licence dtype: string - name: lon dtype: string - name: origin sequence: float64 - name: osm_id dtype: int64 - name: osm_type dtype: string - name: patch_centroid_x dtype: float64 - name: patch_centroid_y dtype: float64 - name: patch_centroid_z dtype: float64 - name: place_id dtype: int64 - name: time dtype: string - name: unit_system dtype: string - name: zone dtype: string - name: class_rep struct: - name: class_repartition struct: - name: agricultural land dtype: float64 - name: bare soil dtype: float64 - name: brushwood dtype: float64 - name: building dtype: float64 - name: clear cut dtype: float64 - name: coniferous dtype: float64 - name: deciduous dtype: float64 - name: greenhouse dtype: float64 - name: herbaceous vegetation dtype: float64 - name: impervious surface dtype: float64 - name: ligneous dtype: float64 - name: mixed dtype: float64 - name: other dtype: float64 - name: pervious surface dtype: float64 - name: plowed land dtype: float64 - name: snow dtype: float64 - name: swimming pool dtype: float64 - name: vineyard dtype: float64 - name: water dtype: float64 - name: prompt dtype: string - name: clip_label dtype: string - name: clip_embeddings sequence: float32 splits: - name: train num_bytes: 26969627407.0 num_examples: 61712 download_size: 26975935912 dataset_size: 26969627407.0 tags: - IGN configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "FLAIR_OSM_CLIP" Dataset for the Seg2Sat model: https://github.com/RubenGres/Seg2Sat Derived from [FLAIR#1](https://huggingface.co/datasets/IGNF/FLAIR) train split. This dataset incudes the following features: - image: FLAIR#1 .tif files RBG bands converted into a more managable jpg format - segmentation: FLAIR#1 segmentation converted to JPG using the [LUT from the documentation](https://ignf.github.io/FLAIR/index_fr.html) - metadata: OSM metadata for the centroid of the image - clip_label: [CLIP ViT-H](https://github.com/openai/CLIP) description - class_rep: ratio of appearance of each class in the segmentation - prompt: Prompt used for the Seg2Sat model - clip_embeddings: Embedding using openai/clip-vit-base-patch32 model <!--- [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) --->
Prithvi-raj-singh/pricer-data	Prithvi-raj-singh	2024-09-13T12:36:06Z	0	0	[ "region:us" ]	[]	2025-05-02T10:26:28Z	null	--- dataset_info: features: - name: text dtype: string - name: price dtype: float64 splits: - name: train num_bytes: 313746593 num_examples: 400000 - name: test num_bytes: 1558981 num_examples: 2000 download_size: 185915273 dataset_size: 315305574 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
sail/regmix-data	sail	2024-09-12T08:12:18Z	24,231	4	[ "language:en", "license:mit", "size_categories:10M<n<100M", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "arxiv:2407.01492", "region:us", "regmix" ]	[]	2024-07-02T13:23:56Z	null	--- license: mit language: - en tags: - regmix pretty_name: regmix-data size_categories: - 10M<n<100M --- # RegMix Data ## Dataset Description The RegMix Data is a curated dataset derived from the Pile-Uncopyrighted, specifically designed for the RegMix paper (https://huggingface.co/papers/2407.01492). This dataset aims to facilitate the automatic identification of high-performing data mixtures for language model pre-training by formulating it as a regression task. ### Key Features: - Size: Approximately 1TB disk space, 250B tokens - Distribution: Follows the natural token distribution of domain examples - Organization: Examples from different domains are separated into individual files ## Dataset Structure The dataset is organized into two main directories: `train` and `valid`, each containing domain-specific JSONL files. The file naming convention is as follows: ``` [domain]-[identifier]-[number].jsonl ``` For example: `arxiv-10-74305611.jsonl` ### Domains Included: arxiv, gutenberg_pg_19, pubmed_central, dm_mathematics, hackernews, stackexchange, enron_emails, nih_exporter, ubuntu_irc, europarl, philpapers, uspto_backgrounds, freelaw, pile_cc, wikipedia_en, github, pubmed_abstracts ## Usage We recommend downloading the entire dataset snapshot instead of using the traditional `load_dataset` function, as the RegMix code is integrated with the [TinyLlama framework](https://github.com/jzhang38/TinyLlama). To download the dataset: ```python from huggingface_hub import snapshot_download LOCAL_DIR = "regmix-data" snapshot_download(repo_id="sail/regmix-data", repo_type='dataset', local_dir=LOCAL_DIR, local_dir_use_symlinks=False) ``` This will download the entire snapshot, containing 34 JSON line files (17 for train, and 17 for valid), to your specified local directory. ## Data Preprocessing Our [code](https://github.com/sail-sg/regmix) will preprocess these domain files into binary format with domain prefixes. It allows for random sampling of the dataset using user-defined data mixtures (i.e., domain weights). ## Acknowledgements We extend our gratitude to the creators of the [Pile-Uncopyrighted dataset](https://huggingface.co/datasets/monology/pile-uncopyrighted) for their efforts in removing copyrighted content from the original Pile dataset, making this work possible. ## Citation If you use this dataset in your research, please cite the RegMix paper: ``` @article{liu2024regmix, title={RegMix: Data Mixture as Regression for Language Model Pre-training}, author={Liu, Qian and Zheng, Xiaosen and Muennighoff, Niklas and Zeng, Guangtao and Dou, Longxu and Pang, Tianyu and Jiang, Jing and Lin, Min}, journal={arXiv preprint arXiv:2407.01492}, year={2024} } ``` For more information about the RegMix methodology and its applications, please refer to the [original paper](https://huggingface.co/papers/2407.01492).
LIUM/tedlium	LIUM	2024-09-10T18:27:41Z	1,048	25	[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "size_categories:10K<n<100K", "region:us" ]	[ "automatic-speech-recognition" ]	2022-05-11T12:47:06Z	1	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: [] multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - automatic-speech-recognition task_ids: [] pretty_name: TED-LIUM --- # Dataset Card for tedlium ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - Homepage: [TED-LIUM homepage](https://www.openslr.org/7/) - Repository: [Needs More Information] - Paper: [TED-LIUM: an Automatic Speech Recognition dedicated corpus](https://aclanthology.org/L12-1405/) - Leaderboard: [Paperswithcode Leaderboard](https://paperswithcode.com/sota/speech-recognition-on-tedlium) - Point of Contact: [Sanchit Gandhi](mailto:[email protected]) ### Dataset Summary The TED-LIUM corpus is English-language TED talks, with transcriptions, sampled at 16kHz. The three releases of the corpus range from 118 to 452 hours of transcribed speech data. ### Example ```python from datasets import load_dataset tedlium = load_dataset("LIUM/tedlium", "release1") # for Release 1 # see structure print(tedlium) # load audio sample on the fly audio_input = tedlium["train"][0]["audio"] # first decoded audio sample transcription = tedlium["train"][0]["text"] # first transcription ``` ### Supported Tasks and Leaderboards - `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://paperswithcode.com/sota/speech-recognition-on-tedlium that ranks models based on their WER. ### Languages The audio and transcriptions are in English, as per the TED talks at http://www.ted.com. ## Dataset Structure ### Data Instances ``` {'audio': {'path': '/home/sanchitgandhi/cache/downloads/extracted/6e3655f9e735ae3c467deed1df788e0dabd671c1f3e2e386e30aa3b571bd9761/TEDLIUM_release1/train/sph/PaulaScher_2008P.sph', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'text': '{COUGH} but <sil> i was so {COUGH} utterly unqualified for(2) this project and {NOISE} so utterly ridiculous {SMACK} and ignored the brief {SMACK} <sil>', 'speaker_id': 'PaulaScher_2008P', 'gender': 'female', 'file': '/home/sanchitgandhi/cache/downloads/extracted/6e3655f9e735ae3c467deed1df788e0dabd671c1f3e2e386e30aa3b571bd9761/TEDLIUM_release1/train/sph/PaulaScher_2008P.sph', 'id': 'PaulaScher_2008P-1003.35-1011.16-<o,f0,female>'} ``` ### Data Fields - audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, i.e. `dataset[0]["audio"]` should always be preferred over `dataset["audio"][0]`. - file: A path to the downloaded audio file in .sph format. - text: the transcription of the audio file. - gender: the gender of the speaker. One of: male, female or N/A. - id: unique id of the data sample. - speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples. ### Data Splits There are three releases for the TED-LIUM corpus, progressively increasing the number of transcribed speech training data from 118 hours (Release 1), to 207 hours (Release 2), to 452 hours (Release 3). Release 1: - 774 audio talks and automatically aligned transcriptions. - Contains 118 hours of speech audio data. - Homepage: https://www.openslr.org/7/ Release 2: - 1495 audio talks and automatically aligned transcriptions. - Contains 207 hours of speech audio data. - Dictionary with pronunciations (159848 entries). - Selected monolingual data for language modeling from WMT12 publicly available corpora. - Homepage: https://www.openslr.org/19/ Release 3: - 2351 audio talks and automatically aligned transcriptions. - Contains 452 hours of speech audio data. - TED-LIUM 2 validation and test data: 19 TED talks with their corresponding manual transcriptions. - Dictionary with pronunciations (159848 entries), the same file as the one included in TED-LIUM 2. - Selected monolingual data for language modeling from WMT12 publicly available corpora: these files come from the TED-LIUM 2 release, but have been modified to produce a tokenization more relevant for English language. - Homepage: https://www.openslr.org/51/ Release 3 contains two different corpus distributions: - The ‘legacy’ one, on which the dev and test datasets are the same as in TED-LIUM 2 (and TED-LIUM 1). - The ‘speaker adaptation’ one, specially designed for experiments on speaker adaptation. Each release is split into a training, validation and test set: \| Split \| Release 1 \| Release 2 \| Release 3 \| \|------------\|-----------\|-----------\|-----------\| \| Train \| 56,803 \| 92,973 \| 268,263 \| \| Validation \| 591 \| 591 \| 591 \| \| Test \| 1,469 \| 1,469 \| 1,469 \| ## Dataset Creation ### Curation Rationale TED-LIUM was built during [The International Workshop on Spoken Language Trans- lation (IWSLT) 2011 Evaluation Campaign](https://aclanthology.org/2011.iwslt-evaluation.1/), an annual workshop focused on the automatic translation of public talks and included tracks for speech recognition, speech translation, text translation, and system combination. ### Source Data #### Initial Data Collection and Normalization The data was obtained from publicly available TED talks at http://www.ted.com. Proper alignments between the speech and the transcribed text were generated using an in-house speaker segmentation and clustering tool (_LIUM_SpkDiarization_). Speech disfluencies (e.g. repetitions, hesitations, false starts) were treated in the following way: repetitions were transcribed, hesitations mapped to a specific filler word, and false starts not taken into account. For full details on the data collection and processing, refer to the [TED-LIUM paper](https://aclanthology.org/L12-1405/). #### Who are the source language producers? TED Talks are influential videos from expert speakers on education, business, science, tech and creativity. ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information Licensed under Creative Commons BY-NC-ND 3.0 (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en). ### Citation Information Release 1: ``` @inproceedings{rousseau2012tedlium, title={TED-LIUM: an Automatic Speech Recognition dedicated corpus}, author={Rousseau, Anthony and Del{\'e}glise, Paul and Est{\`e}ve, Yannick}, booktitle={Conference on Language Resources and Evaluation (LREC)}, pages={125--129}, year={2012} } ``` Release 2: ``` @inproceedings{rousseau2014enhancing, title={Enhancing the TED-LIUM corpus with selected data for language modeling and more TED talks.}, author={Rousseau, Anthony and Del{\'e}glise, Paul and Esteve, Yannick and others}, booktitle={LREC}, pages={3935--3939}, year={2014} } ``` Release 3: ``` @inproceedings{hernandez2018ted, author="Hernandez, Fran{\c{c}}ois and Nguyen, Vincent and Ghannay, Sahar and Tomashenko, Natalia and Est{\`e}ve, Yannick", title="TED-LIUM 3: Twice as Much Data and Corpus Repartition for Experiments on Speaker Adaptation", booktitle="Speech and Computer", year="2018", publisher="Springer International Publishing", pages="198--208", } ```
google/xtreme_s	google	2024-09-10T13:12:26Z	2,557	62	[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "annotations_creators:crowdsourced", "annotations_creators:machine-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "source_datasets:extended\|multilingual_librispeech", "source_datasets:extended\|covost2", "language:afr", "language:amh", "language:ara", "language:asm", "language:ast", "language:azj", "language:bel", "language:ben", "language:bos", "language:cat", "language:ceb", "language:cmn", "language:ces", "language:cym", "language:dan", "language:deu", "language:ell", "language:eng", "language:spa", "language:est", "language:fas", "language:ful", "language:fin", "language:tgl", "language:fra", "language:gle", "language:glg", "language:guj", "language:hau", "language:heb", "language:hin", "language:hrv", "language:hun", "language:hye", "language:ind", "language:ibo", "language:isl", "language:ita", "language:jpn", "language:jav", "language:kat", "language:kam", "language:kea", "language:kaz", "language:khm", "language:kan", "language:kor", "language:ckb", "language:kir", "language:ltz", "language:lug", "language:lin", "language:lao", "language:lit", "language:luo", "language:lav", "language:mri", "language:mkd", "language:mal", "language:mon", "language:mar", "language:msa", "language:mlt", "language:mya", "language:nob", "language:npi", "language:nld", "language:nso", "language:nya", "language:oci", "language:orm", "language:ory", "language:pan", "language:pol", "language:pus", "language:por", "language:ron", "language:rus", "language:bul", "language:snd", "language:slk", "language:slv", "language:sna", "language:som", "language:srp", "language:swe", "language:swh", "language:tam", "language:tel", "language:tgk", "language:tha", "language:tur", "language:ukr", "language:umb", "language:urd", "language:uzb", "language:vie", "language:wol", "language:xho", "language:yor", "language:yue", "language:zul", "license:cc-by-4.0", "size_categories:10K<n<100K", "arxiv:2203.10752", "arxiv:2205.12446", "arxiv:2007.10310", "region:us", "speech-recognition" ]	[ "automatic-speech-recognition" ]	2022-03-04T14:10:40Z	1	--- annotations_creators: - expert-generated - crowdsourced - machine-generated language_creators: - crowdsourced - expert-generated language: - afr - amh - ara - asm - ast - azj - bel - ben - bos - cat - ceb - cmn - ces - cym - dan - deu - ell - eng - spa - est - fas - ful - fin - tgl - fra - gle - glg - guj - hau - heb - hin - hrv - hun - hye - ind - ibo - isl - ita - jpn - jav - kat - kam - kea - kaz - khm - kan - kor - ckb - kir - ltz - lug - lin - lao - lit - luo - lav - mri - mkd - mal - mon - mar - msa - mlt - mya - nob - npi - nld - nso - nya - oci - orm - ory - pan - pol - pus - por - ron - rus - bul - snd - slk - slv - sna - som - srp - swe - swh - tam - tel - tgk - tha - tur - ukr - umb - urd - uzb - vie - wol - xho - yor - yue - zul license: - cc-by-4.0 multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - extended\|multilingual_librispeech - extended\|covost2 task_categories: - automatic-speech-recognition task_ids: [] paperswithcode_id: librispeech-1 pretty_name: 'The Cross-lingual TRansfer Evaluation of Multilingual Encoders for Speech (XTREME-S) benchmark is a benchmark designed to evaluate speech representations across languages, tasks, domains and data regimes. It covers 102 languages from 10+ language families, 3 different domains and 4 task families: speech recognition, translation, classification and retrieval.' tags: - speech-recognition --- # XTREME-S ## Dataset Description - Fine-Tuning script: [research-projects/xtreme-s](https://github.com/huggingface/transformers/tree/master/examples/research_projects/xtreme-s) - Paper: [XTREME-S: Evaluating Cross-lingual Speech Representations](https://arxiv.org/abs/2203.10752) - Leaderboard: [TODO(PVP)]() - FLEURS amount of disk used: 350 GB - Multilingual Librispeech amount of disk used: 2700 GB - Voxpopuli amount of disk used: 400 GB - Covost2 amount of disk used: 70 GB - Minds14 amount of disk used: 5 GB - Total amount of disk used: ca. 3500 GB The Cross-lingual TRansfer Evaluation of Multilingual Encoders for Speech (XTREME-S) benchmark is a benchmark designed to evaluate speech representations across languages, tasks, domains and data regimes. It covers 102 languages from 10+ language families, 3 different domains and 4 task families: speech recognition, translation, classification and retrieval. *TLDR; XTREME-S is the first speech benchmark that is both diverse, fully accessible, and reproducible. All datasets can be downloaded with a single line of code. An easy-to-use and flexible fine-tuning script is provided and actively maintained.* XTREME-S covers speech recognition with Fleurs, Multilingual LibriSpeech (MLS) and VoxPopuli, speech translation with CoVoST-2, speech classification with LangID (Fleurs) and intent classification (MInds-14) and finally speech(-text) retrieval with Fleurs. Each of the tasks covers a subset of the 102 languages included in XTREME-S, from various regions: - Western Europe: Asturian, Bosnian, Catalan, Croatian, Danish, Dutch, English, Finnish, French, Galician, German, Greek, Hungarian, Icelandic, Irish, Italian, Kabuverdianu, Luxembourgish, Maltese, Norwegian, Occitan, Portuguese, Spanish, Swedish, Welsh - Eastern Europe: Armenian, Belarusian, Bulgarian, Czech, Estonian, Georgian, Latvian, Lithuanian, Macedonian, Polish, Romanian, Russian, Serbian, Slovak, Slovenian, Ukrainian - Central-Asia/Middle-East/North-Africa: Arabic, Azerbaijani, Hebrew, Kazakh, Kyrgyz, Mongolian, Pashto, Persian, Sorani-Kurdish, Tajik, Turkish, Uzbek - Sub-Saharan Africa: Afrikaans, Amharic, Fula, Ganda, Hausa, Igbo, Kamba, Lingala, Luo, Northern-Sotho, Nyanja, Oromo, Shona, Somali, Swahili, Umbundu, Wolof, Xhosa, Yoruba, Zulu - South-Asia: Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali, Oriya, Punjabi, Sindhi, Tamil, Telugu, Urdu - South-East Asia: Burmese, Cebuano, Filipino, Indonesian, Javanese, Khmer, Lao, Malay, Maori, Thai, Vietnamese - CJK languages: Cantonese and Mandarin Chinese, Japanese, Korean ## Design principles ### Diversity XTREME-S aims for task, domain and language diversity. Tasks should be diverse and cover several domains to provide a reliable evaluation of model generalization and robustness to noisy naturally-occurring speech in different environments. Languages should be diverse to ensure that models can adapt to a wide range of linguistic and phonological phenomena. ### Accessibility The sub-dataset for each task can be downloaded with a single line of code as shown in [Supported Tasks](#supported-tasks). Each task is available under a permissive license that allows the use and redistribution of the data for research purposes. Tasks have been selected based on their usage by pre-existing multilingual pre-trained models, for simplicity. ### Reproducibility We produce fully open-sourced, maintained and easy-to-use fine-tuning scripts for each task as shown under [Fine-tuning Example](#fine-tuning-and-evaluation-example). XTREME-S encourages submissions that leverage publicly available speech and text datasets. Users should detail which data they use. In general, we encourage settings that can be reproduced by the community, but also encourage the exploration of new frontiers for speech representation learning. ## Fine-tuning and Evaluation Example We provide a fine-tuning script under [research-projects/xtreme-s](https://github.com/huggingface/transformers/tree/master/examples/research_projects/xtreme-s). The fine-tuning script is written in PyTorch and allows one to fine-tune and evaluate any [Hugging Face model](https://huggingface.co/models) on XTREME-S. The example script is actively maintained by [@anton-l](https://github.com/anton-l) and [@patrickvonplaten](https://github.com/patrickvonplaten). Feel free to reach out via issues or pull requests on GitHub if you have any questions. ## Leaderboards The leaderboard for the XTREME-S benchmark can be found at [this address (TODO(PVP))](). ## Supported Tasks Note that the suppoprted tasks are focused particularly on linguistic aspect of speech, while nonlinguistic/paralinguistic aspects of speech relevant to e.g. speech synthesis or voice conversion are not evaluated. <p align="center"> <img src="https://github.com/patrickvonplaten/scientific_images/raw/master/xtreme_s.png" alt="Datasets used in XTREME"/> </p> ### 1. Speech Recognition (ASR) We include three speech recognition datasets: FLEURS-ASR, MLS and VoxPopuli (optionally BABEL). Multilingual fine-tuning is used for these three datasets. #### FLEURS-ASR FLEURS-ASR is the speech version of the FLORES machine translation benchmark, covering 2000 n-way parallel sentences in n=102 languages. ```py from datasets import load_dataset fleurs_asr = load_dataset("google/xtreme_s", "fleurs.af_za") # for Afrikaans # to download all data for multi-lingual fine-tuning uncomment following line # fleurs_asr = load_dataset("google/xtreme_s", "fleurs.all") # see structure print(fleurs_asr) # load audio sample on the fly audio_input = fleurs_asr["train"][0]["audio"] # first decoded audio sample transcription = fleurs_asr["train"][0]["transcription"] # first transcription # use `audio_input` and `transcription` to fine-tune your model for ASR # for analyses see language groups all_language_groups = fleurs_asr["train"].features["lang_group_id"].names lang_group_id = fleurs_asr["train"][0]["lang_group_id"] all_language_groups[lang_group_id] ``` #### Multilingual LibriSpeech (MLS) MLS is a large multilingual corpus derived from read audiobooks from LibriVox and consists of 8 languages. For this challenge the training data is limited to 10-hours splits. ```py from datasets import load_dataset mls = load_dataset("google/xtreme_s", "mls.pl") # for Polish # to download all data for multi-lingual fine-tuning uncomment following line # mls = load_dataset("google/xtreme_s", "mls.all") # see structure print(mls) # load audio sample on the fly audio_input = mls["train"][0]["audio"] # first decoded audio sample transcription = mls["train"][0]["transcription"] # first transcription # use `audio_input` and `transcription` to fine-tune your model for ASR ``` #### VoxPopuli VoxPopuli is a large-scale multilingual speech corpus for representation learning and semi-supervised learning, from which we use the speech recognition dataset. The raw data is collected from 2009-2020 European Parliament event recordings. We acknowledge the European Parliament for creating and sharing these materials. VoxPopuli has to download the whole dataset 100GB since languages are entangled into each other - maybe not worth testing here due to the size ```py from datasets import load_dataset voxpopuli = load_dataset("google/xtreme_s", "voxpopuli.ro") # for Romanian # to download all data for multi-lingual fine-tuning uncomment following line # voxpopuli = load_dataset("google/xtreme_s", "voxpopuli.all") # see structure print(voxpopuli) # load audio sample on the fly audio_input = voxpopuli["train"][0]["audio"] # first decoded audio sample transcription = voxpopuli["train"][0]["transcription"] # first transcription # use `audio_input` and `transcription` to fine-tune your model for ASR ``` #### (Optionally) BABEL BABEL from IARPA is a conversational speech recognition dataset in low-resource languages. First, download LDC2016S06, LDC2016S12, LDC2017S08, LDC2017S05 and LDC2016S13. BABEL is the only dataset in our benchmark who is less easily accessible, so you will need to sign in to get access to it on LDC. Although not officially part of the XTREME-S ASR datasets, BABEL is often used for evaluating speech representations on a difficult domain (phone conversations). ```py from datasets import load_dataset babel = load_dataset("google/xtreme_s", "babel.as") ``` The above command is expected to fail with a nice error message, explaining how to download BABEL The following should work: ```py from datasets import load_dataset babel = load_dataset("google/xtreme_s", "babel.as", data_dir="/path/to/IARPA_BABEL_OP1_102_LDC2016S06.zip") # see structure print(babel) # load audio sample on the fly audio_input = babel["train"][0]["audio"] # first decoded audio sample transcription = babel["train"][0]["transcription"] # first transcription # use `audio_input` and `transcription` to fine-tune your model for ASR ``` ### 2. Speech Translation (ST) We include the CoVoST-2 dataset for automatic speech translation. #### CoVoST-2 The CoVoST-2 benchmark has become a commonly used dataset for evaluating automatic speech translation. It covers language pairs from English into 15 languages, as well as 21 languages into English. We use only the "X->En" direction to evaluate cross-lingual representations. The amount of supervision varies greatly in this setting, from one hour for Japanese->English to 180 hours for French->English. This makes pretraining particularly useful to enable such few-shot learning. We enforce multiligual fine-tuning for simplicity. Results are splitted in high/med/low-resource language pairs as explained in the [paper (TODO(PVP))]. ```py from datasets import load_dataset covost_2 = load_dataset("google/xtreme_s", "covost2.id.en") # for Indonesian to English # to download all data for multi-lingual fine-tuning uncomment following line # covost_2 = load_dataset("google/xtreme_s", "covost2.all") # see structure print(covost_2) # load audio sample on the fly audio_input = covost_2["train"][0]["audio"] # first decoded audio sample transcription = covost_2["train"][0]["transcription"] # first transcription translation = covost_2["train"][0]["translation"] # first translation # use audio_input and translation to fine-tune your model for AST ``` ### 3. Speech Classification We include two multilingual speech classification datasets: FLEURS-LangID and Minds-14. #### Language Identification - FLEURS-LangID LangID can often be a domain classification, but in the case of FLEURS-LangID, recordings are done in a similar setting across languages and the utterances correspond to n-way parallel sentences, in the exact same domain, making this task particularly relevant for evaluating LangID. The setting is simple, FLEURS-LangID is splitted in train/valid/test for each language. We simply create a single train/valid/test for LangID by merging all. ```py from datasets import load_dataset fleurs_langID = load_dataset("google/xtreme_s", "fleurs.all") # to download all data # see structure print(fleurs_langID) # load audio sample on the fly audio_input = fleurs_langID["train"][0]["audio"] # first decoded audio sample language_class = fleurs_langID["train"][0]["lang_id"] # first id class language = fleurs_langID["train"].features["lang_id"].names[language_class] # use audio_input and language_class to fine-tune your model for audio classification ``` #### Intent classification - Minds-14 Minds-14 is an intent classification made from e-banking speech datasets in 14 languages, with 14 intent labels. We impose a single multilingual fine-tuning to increase the size of the train and test sets and reduce the variance associated with the small size of the dataset per language. ```py from datasets import load_dataset minds_14 = load_dataset("google/xtreme_s", "minds14.fr-FR") # for French # to download all data for multi-lingual fine-tuning uncomment following line # minds_14 = load_dataset("google/xtreme_s", "minds14.all") # see structure print(minds_14) # load audio sample on the fly audio_input = minds_14["train"][0]["audio"] # first decoded audio sample intent_class = minds_14["train"][0]["intent_class"] # first transcription intent = minds_14["train"].features["intent_class"].names[intent_class] # use audio_input and language_class to fine-tune your model for audio classification ``` ### 4. (Optionally) Speech Retrieval We optionally include one speech retrieval dataset: FLEURS-Retrieval as explained in the [FLEURS paper](https://arxiv.org/abs/2205.12446). #### FLEURS-Retrieval FLEURS-Retrieval provides n-way parallel speech and text data. Similar to how XTREME for text leverages Tatoeba to evaluate bitext mining a.k.a sentence translation retrieval, we use FLEURS-Retrieval to evaluate the quality of fixed-size representations of speech utterances. Our goal is to incentivize the creation of fixed-size speech encoder for speech retrieval. The system has to retrieve the English "key" utterance corresponding to the speech translation of "queries" in 15 languages. Results have to be reported on the test sets of FLEURS-Retrieval whose utterances are used as queries (and keys for English). We augment the English keys with a large number of utterances to make the task more difficult. ```py from datasets import load_dataset fleurs_retrieval = load_dataset("google/xtreme_s", "fleurs.af_za") # for Afrikaans # to download all data for multi-lingual fine-tuning uncomment following line # fleurs_retrieval = load_dataset("google/xtreme_s", "fleurs.all") # see structure print(fleurs_retrieval) # load audio sample on the fly audio_input = fleurs_retrieval["train"][0]["audio"] # decoded audio sample text_sample_pos = fleurs_retrieval["train"][0]["transcription"] # positive text sample text_sample_neg = fleurs_retrieval["train"][1:20]["transcription"] # negative text samples # use `audio_input`, `text_sample_pos`, and `text_sample_neg` to fine-tune your model for retrieval ``` Users can leverage the training (and dev) sets of FLEURS-Retrieval with a ranking loss to build better cross-lingual fixed-size representations of speech. ## Dataset Structure The XTREME-S benchmark is composed of the following datasets: - [FLEURS](https://huggingface.co/datasets/google/fleurs#dataset-structure) - [Multilingual Librispeech (MLS)](https://huggingface.co/datasets/facebook/multilingual_librispeech#dataset-structure) Note that for MLS, XTREME-S uses `path` instead of `file` and `transcription` instead of `text`. - [Voxpopuli](https://huggingface.co/datasets/facebook/voxpopuli#dataset-structure) - [Minds14](https://huggingface.co/datasets/polyai/minds14#dataset-structure) - [Covost2](https://huggingface.co/datasets/covost2#dataset-structure) Note that for Covost2, XTREME-S uses `path` instead of `file` and `transcription` instead of `sentence`. - [BABEL](https://huggingface.co/datasets/ldc/iarpa_babel#dataset-structure) Please click on the link of the dataset cards to get more information about its dataset structure. ## Dataset Creation The XTREME-S benchmark is composed of the following datasets: - [FLEURS](https://huggingface.co/datasets/google/fleurs#dataset-creation) - [Multilingual Librispeech (MLS)](https://huggingface.co/datasets/facebook/multilingual_librispeech#dataset-creation) - [Voxpopuli](https://huggingface.co/datasets/facebook/voxpopuli#dataset-creation) - [Minds14](https://huggingface.co/datasets/polyai/minds14#dataset-creation) - [Covost2](https://huggingface.co/datasets/covost2#dataset-creation) - [BABEL](https://huggingface.co/datasets/ldc/iarpa_babel#dataset-creation) Please visit the corresponding dataset cards to get more information about the source data. ## Considerations for Using the Data ### Social Impact of Dataset This dataset is meant to encourage the development of speech technology in a lot more languages of the world. One of the goal is to give equal access to technologies like speech recognition or speech translation to everyone, meaning better dubbing or better access to content from the internet (like podcasts, streaming or videos). ### Discussion of Biases Most datasets have a fair distribution of gender utterances (e.g. the newly introduced FLEURS dataset). While many languages are covered from various regions of the world, the benchmark misses many languages that are all equally important. We believe technology built through XTREME-S should generalize to all languages. ### Other Known Limitations The benchmark has a particular focus on read-speech because common evaluation benchmarks like CoVoST-2 or LibriSpeech evaluate on this type of speech. There is sometimes a known mismatch between performance obtained in a read-speech setting and a more noisy setting (in production for instance). Given the big progress that remains to be made on many languages, we believe better performance on XTREME-S should still correlate well with actual progress made for speech understanding. ## Additional Information All datasets are licensed under the [Creative Commons license (CC-BY)](https://creativecommons.org/licenses/). ### Citation Information #### XTREME-S ``` @article{conneau2022xtreme, title={XTREME-S: Evaluating Cross-lingual Speech Representations}, author={Conneau, Alexis and Bapna, Ankur and Zhang, Yu and Ma, Min and von Platen, Patrick and Lozhkov, Anton and Cherry, Colin and Jia, Ye and Rivera, Clara and Kale, Mihir and others}, journal={arXiv preprint arXiv:2203.10752}, year={2022} } ``` #### MLS ``` @article{Pratap2020MLSAL, title={MLS: A Large-Scale Multilingual Dataset for Speech Research}, author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert}, journal={ArXiv}, year={2020}, volume={abs/2012.03411} } ``` #### VoxPopuli ``` @article{wang2021voxpopuli, title={Voxpopuli: A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation}, author={Wang, Changhan and Riviere, Morgane and Lee, Ann and Wu, Anne and Talnikar, Chaitanya and Haziza, Daniel and Williamson, Mary and Pino, Juan and Dupoux, Emmanuel}, journal={arXiv preprint arXiv:2101.00390}, year={2021} } ``` #### CoVoST 2 ``` @article{DBLP:journals/corr/abs-2007-10310, author = {Changhan Wang and Anne Wu and Juan Miguel Pino}, title = {CoVoST 2: {A} Massively Multilingual Speech-to-Text Translation Corpus}, journal = {CoRR}, volume = {abs/2007.10310}, year = {2020}, url = {https://arxiv.org/abs/2007.10310}, eprinttype = {arXiv}, eprint = {2007.10310}, timestamp = {Thu, 12 Aug 2021 15:37:06 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2007-10310.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` #### Minds14 ``` @article{gerz2021multilingual, title={Multilingual and cross-lingual intent detection from spoken data}, author={Gerz, Daniela and Su, Pei-Hao and Kusztos, Razvan and Mondal, Avishek and Lis, Micha{\l} and Singhal, Eshan and Mrk{\v{s}}i{\'c}, Nikola and Wen, Tsung-Hsien and Vuli{\'c}, Ivan}, journal={arXiv preprint arXiv:2104.08524}, year={2021} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@anton-l](https://github.com/anton-l), [@aconneau](https://github.com/aconneau) for adding this dataset
allenai/reward-bench	allenai	2024-09-09T16:11:45Z	7,408	94	[ "task_categories:question-answering", "language:en", "license:odc-by", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2403.13787", "arxiv:2310.07641", "arxiv:2308.01263", "arxiv:2308.07124", "doi:10.57967/hf/2457", "region:us" ]	[ "question-answering" ]	2023-12-14T18:38:07Z	null	--- language: - en license: odc-by size_categories: - 1K<n<10K task_categories: - question-answering pretty_name: RM Bench dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: chosen_model dtype: string - name: rejected dtype: string - name: rejected_model dtype: string - name: subset dtype: string - name: id dtype: int64 splits: - name: raw num_bytes: 10837043 num_examples: 5123 - name: filtered num_bytes: 4849207 num_examples: 2985 download_size: 7943951 dataset_size: 15686250 configs: - config_name: default data_files: - split: raw path: data/raw-* - split: filtered path: data/filtered-* --- <img src="https://huggingface.co/spaces/allenai/reward-bench/resolve/main/src/logo.png" alt="RewardBench Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> [Code](https://github.com/allenai/reward-bench) \| [Leaderboard](https://huggingface.co/spaces/allenai/reward-bench) \| [Prior Preference Sets](https://huggingface.co/datasets/allenai/pref-test-sets) \| [Results](https://huggingface.co/datasets/allenai/reward-bench-results) \| [Paper](https://arxiv.org/abs/2403.13787) # Reward Bench Evaluation Dataset Card The RewardBench evaluation dataset evaluates capabilities of reward models over the following categories: 1. Chat: Includes the easy chat subsets (alpacaeval-easy, alpacaeval-length, alpacaeval-hard, mt-bench-easy, mt-bench-medium) 2. Chat Hard: Includes the hard chat subsets (mt-bench-hard, llmbar-natural, llmbar-adver-neighbor, llmbar-adver-GPTInst, llmbar-adver-GPTOut, llmbar-adver-manual) 3. Safety: Includes the safety subsets (refusals-dangerous, refusals-offensive, xstest-should-refuse, xstest-should-respond, do not answer) 4. Reasoning: Includes the code and math subsets (math-prm, hep-cpp, hep-go, hep-java, hep-js, hep-python, hep-rust) The RewardBench leaderboard averages over these subsets and a final category from [prior preference data test sets](https://huggingface.co/datasets/allenai/preference-test-sets) including Anthropic Helpful, Anthropic HHH in BIG-Bench, Stanford Human Preferences (SHP), and OpenAI's Learning to Summarize data. The scoring for RewardBench compares the score of a prompt-chosen pair to a prompt-rejected pair. Success is when the chosen score is higher than rejected. <img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/reward-bench/scoring.png" alt="RewardBench Scoring" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> In order to create a representative, single evaluation score, we perform a limited mixture of averaging across results. For all the subsets detailed below except for Reasoning, we perform per-prompt weighted averaging across all the prompts in the subset to get the section score. For example, in Chat we take a weighted average of the AlpacaEval and MT Bench sets based on the number of prompts. For Reasoning, we increase the weight of the PRM-Math subset so code and math abilities are weighed equally in the final number, rather than increasing the relevance of code. Once all subsets weighted averages are achieved, the final RewardBench score is the average across the subset scores (including Prior Sets). ## Dataset Details In order to maintain all the relevant data, the samples in the dataset will have the following items. Note, the dataset is single-turn: * `prompt` (`str`): the instruction given in the various test sets. * `chosen` (`str`): the response from the better model or the better rated prompt. * `chosen_model` (`str`): where applicable * `rejected` (`str`): the response with the lower score or from word model. * `rejected_model` (`str`): where applicable * `subset` (`str`): the subset (e.g. alpacaeval-easy) of the associated prompt as the dataset is all in one split. * `id` (`int`): an incremented id for every prompt in the benchmark. To select a specific subset use HuggingFace Datasets `.filter` functionality. ``` dataset = dataset.filter(lambda ex: ex["subset"] == "alpacaeval-easy") ``` This can easily be converted to the standard chosen/rejected list of messages format (see [UltraFeedback for an example](https://huggingface.co/datasets/allenai/ultrafeedback_binarized_cleaned)), for example with our data loading utilities on [GitHub](https://github.com/allenai/reward-bench/blob/8eadb09397d58f1930d4f77938e618b9f9b8aeb3/rewardbench/utils.py#L330). ### Subset Summary Total number of the prompts is: 2985. \| Subset \| Num. Samples (Pre-filtering, post-filtering) \| Description \| \| :---------- \| :-----: \| :---------: \| \| alpacaeval-easy \| 805, 100 \| Great model vs poor model; GPT4-Turbo 97.7% v. Alpaca 7b 26.46% (data [here](https://github.com/tatsu-lab/alpaca_eval/tree/main/results)) \| \| alpacaeval-length \| 805, 95 \| Good model vs low model, similar length; Llama2chat 70B 92.66% vs Guanaco 13B 52.61% (data [here](https://github.com/tatsu-lab/alpaca_eval/tree/main/results)) \| \| alpacaeval-hard \| 805, 95 \| Great model vs baseline model; Tulu 2 95.0% v. Davinici003 50.0% (data [here](https://github.com/tatsu-lab/alpaca_eval/tree/main/results))\| \| mt-bench-easy \| 28, 28 \| MT Bench 10s vs 1s (source [data](https://huggingface.co/spaces/lmsys/mt-bench/tree/main/data/mt_bench)) \| \| mt-bench-medium \| 45, 40 \| MT Bench 9s vs 2-5s (source [data](https://huggingface.co/spaces/lmsys/mt-bench/tree/main/data/mt_bench)) \| \| mt-bench-hard \| 45, 37 \| MT Bench 7-8 vs 5-6 (source [data](https://huggingface.co/spaces/lmsys/mt-bench/tree/main/data/mt_bench)) \| \| refusals-dangerous \| 505, 100 \| Dangerous rejected response vs polite chosen refusal \| \| refusals-offensive \| 704, 100 \| Offensive rejected response vs polite chosen refusal \| \| llmbar-natural \| 100 \| Manually curated instruction pairs (See [paper](https://arxiv.org/abs/2310.07641)) \| \| llmbar-adver-neighbor \| 134 \| Adversarial instruction response vs. off-topic prompt response (See [paper](https://arxiv.org/abs/2310.07641))\| \| llmbar-adver-GPTInst \| 92 \| Adversarial instruction response vs. GPT4 generated off-topic prompt response (See [paper](https://arxiv.org/abs/2310.07641))\| \| llmbar-adver-GPTOut \| 47 \| Adversarial instruction response vs. unhelpful-prompted GPT4 responses (See [paper](https://arxiv.org/abs/2310.07641))\| \| llmbar-adver-manual \| 46 \| Challenge set manually designed chosen vs. rejected \| \| xstest-should-refuse \| 450, 154 \| False response dataset (see [paper](https://arxiv.org/abs/2308.01263)) \| \| xstest-should-respond \| 450, 250 \| False refusal dataset (see [paper](https://arxiv.org/abs/2308.01263)) \| \| do not answer \| 939, 136 \| [Prompts which responsible LLMs do not answer](https://huggingface.co/datasets/LibrAI/do-not-answer): Refusals are chosen and responses are rejected \| \| hep-cpp \| 164 \| C++ working code vs. buggy code (See [dataset](https://huggingface.co/datasets/bigcode/humanevalpack) or [paper](https://arxiv.org/abs/2308.07124)) \| \| hep-go \| 164 \| Go working code vs. buggy code \| \| hep-java \| 164 \| Java working code vs. buggy code \| \| hep-js \| 164 \| Javascript working code vs. buggy code \| \| hep-python \| 164 \| Python working code vs. buggy code \| \| hep-rust \| 164 \| Rust working code vs. buggy code \| \| math-prm \| 447 \| Human references vs. model error (see [paper](https://github.com/openai/prm800k)) \| The length distribution of the subsets with a Llama tokenizer is shown below. \| subset \| Chosen Mean Tokens \| Rejected Mean Tokens \| Chosen Max Tokens \| Rejected Max Tokens \| Chosen Min Tokens \| Rejected Min Tokens \| Chosen Mean Unique Tokens \| Rejected Mean Unique Tokens \| Chosen Max Unique Tokens \| Rejected Max Unique Tokens \| Chosen Min Unique Tokens \| Rejected Min Unique Tokens \| \|-----------------------\|----------------------\|------------------------\|---------------------\|-----------------------\|---------------------\|-----------------------\|-----------------------------\|-------------------------------\|----------------------------\|------------------------------\|----------------------------\|------------------------------\| \| alpacaeval-easy \| 591.26 \| 167.33 \| 1332 \| 1043 \| 40 \| 15 \| 252.91 \| 83.44 \| 630 \| 290 \| 33 \| 12 \| \| alpacaeval-hard \| 411.684 \| 136.926 \| 1112 \| 711 \| 57 \| 12 \| 172.537 \| 70.9684 \| 359 \| 297 \| 45 \| 8 \| \| alpacaeval-length \| 510.589 \| 596.895 \| 1604 \| 2242 \| 55 \| 52 \| 192.442 \| 188.547 \| 434 \| 664 \| 30 \| 38 \| \| donotanswer \| 169.61 \| 320.5 \| 745 \| 735 \| 20 \| 20 \| 103.743 \| 156.941 \| 358 \| 337 \| 18 \| 13 \| \| hep-cpp \| 261.262 \| 259.488 \| 833 \| 835 \| 53 \| 57 \| 99.8537 \| 99.372 \| 201 \| 201 \| 37 \| 40 \| \| hep-go \| 266.22 \| 264.598 \| 732 \| 720 \| 55 \| 57 \| 99.622 \| 99.189 \| 201 \| 201 \| 36 \| 37 \| \| hep-java \| 263.14 \| 260.939 \| 748 \| 733 \| 55 \| 54 \| 102.311 \| 101.927 \| 207 \| 206 \| 39 \| 41 \| \| hep-js \| 251.165 \| 249.695 \| 771 \| 774 \| 53 \| 52 \| 93.2744 \| 92.9268 \| 192 \| 192 \| 37 \| 40 \| \| hep-python \| 211.988 \| 211.146 \| 624 \| 612 \| 53 \| 49 \| 85.6463 \| 85.3049 \| 190 \| 190 \| 36 \| 35 \| \| hep-rust \| 221.256 \| 219.049 \| 988 \| 993 \| 46 \| 49 \| 95.1402 \| 94.8354 \| 192 \| 192 \| 36 \| 36 \| \| llmbar-adver-GPTInst \| 170.109 \| 377.359 \| 636 \| 959 \| 15 \| 15 \| 92.9457 \| 179.37 \| 287 \| 471 \| 12 \| 13 \| \| llmbar-adver-GPTOut \| 96.4255 \| 101 \| 393 \| 476 \| 18 \| 20 \| 60.0426 \| 55.0426 \| 241 \| 228 \| 13 \| 14 \| \| llmbar-adver-manual \| 159.804 \| 264.37 \| 607 \| 737 \| 23 \| 33 \| 91.9565 \| 140.13 \| 273 \| 385 \| 18 \| 24 \| \| llmbar-adver-neighbor \| 70.2239 \| 172.507 \| 603 \| 865 \| 9 \| 13 \| 43.3134 \| 90.9328 \| 250 \| 324 \| 8 \| 9 \| \| llmbar-natural \| 139.42 \| 129.82 \| 907 \| 900 \| 17 \| 18 \| 74.99 \| 70.07 \| 354 \| 352 \| 14 \| 14 \| \| math-prm \| 279.313 \| 488.841 \| 1608 \| 1165 \| 35 \| 77 \| 83.6264 \| 124.582 \| 237 \| 257 \| 23 \| 46 \| \| mt-bench-easy \| 391.821 \| 481.929 \| 778 \| 1126 \| 155 \| 31 \| 169.071 \| 121.321 \| 288 \| 434 \| 74 \| 19 \| \| mt-bench-hard \| 287.784 \| 301.649 \| 573 \| 1176 \| 68 \| 62 \| 133.622 \| 121.676 \| 261 \| 309 \| 50 \| 48 \| \| mt-bench-med \| 351.375 \| 466.025 \| 655 \| 1297 \| 145 \| 52 \| 159.9 \| 140.325 \| 285 \| 495 \| 82 \| 41 \| \| refusals-dangerous \| 208.4 \| 458.61 \| 380 \| 804 \| 87 \| 103 \| 128.53 \| 211 \| 200 \| 365 \| 71 \| 55 \| \| refusals-offensive \| 139.82 \| 298.63 \| 278 \| 1117 \| 75 \| 26 \| 95.98 \| 134.02 \| 170 \| 477 \| 60 \| 19 \| \| xstest-should-refuse \| 129.227 \| 217.019 \| 402 \| 549 \| 18 \| 15 \| 80.5519 \| 116.149 \| 194 \| 245 \| 16 \| 13 \| \| xstest-should-respond \| 188.708 \| 107.356 \| 515 \| 465 \| 20 \| 16 \| 103.788 \| 67.328 \| 231 \| 202 \| 15 \| 16 \| ### Filtering Summary The RewardBench dataset is manually filtered from 5123 source prompts to manually verify the chosen-rejected ranking of prompts. * The categories of AlpacaEval and MT Bench are manually filtered for every prompt. * LLMBar, DoNotAnswer, HEP, and Math PRM all contained structured metadata for automatic filtering. * XSTest is a hybrid of manual confirmation with metadata from the project. * Refusals are automatically generated as a refusal or response (where refusal is preffered) with manual confirmation. Substantial filtering details are available in the appendix of the papr. If there are any bugs in the data, please reach out! ### License information Licensing an aggregated dataset is a complex task. We release the RewardBench dataset under [ODC-BY](https://opendatacommons.org/licenses/by/) requiring the user to follow the licenses of the subsequent parts. Licensing LLM datasets is an evolving topic. The licenses primarily apply to the prompts and the completions generated by models are often unlicensed. The details for the datasets used in this work vary in the level of the detail on licenses and method of applying them. \| Dataset \| Variants \| Data License \| \|---------------\|----------------------------------------------------------\|------------------------------------------------------\| \| AlpacaEval \| {Easy, Length, Hard} \| [CC By NC 4.0](https://github.com/tatsu-lab/alpaca_farm/blob/main/DATA_LICENSE) \| \| MT Bench \| {Easy, Medium, Hard} \| [Apache 2.0](https://github.com/lm-sys/FastChat/blob/main/LICENSE) \| \| LLMBar \| {Natural, Neighbor, GPTInst, GPTOut, Manual} \| [MIT License](https://github.com/princeton-nlp/LLMBar?tab=MIT-1-ov-file) \| \| Do Not Answer \| \| [CC BY NC SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) \| \| XSTest \| {Should Respond, Should Refuse} \| [CC By 4.0](https://github.com/paul-rottger/exaggerated-safety?tab=CC-BY-4.0-1-ov-file) \| \| HumanEvalPack \| {HEP CPP, Go, Javascript, Rust, Python, Rust} \| [MIT License](https://github.com/bigcode-project/octopack?tab=MIT-1-ov-file) \| \| PRM Math \| \| [MIT License](https://github.com/openai/prm800k?tab=MIT-1-ov-file) \| Within this dataset are prompts created by AI2 (the refusals data, released as MIT for now, see official release soon) with completions from API and open models. More details will come on this soon. ## Development ### Requirements Building the dataset requires `datasets`. Maintaining the script and notebooks requites `notebook`. ``` pip install datasets notebook nbconvert ``` Convert with: ``` jupyter nbconvert --to script [YOUR_NOTEBOOK].ipynb ``` With no changes to the ipynb, the dataset can be re-built and pushed with the following (PLEASE BE CAREFUL): ``` python build_dataset.py ``` ### Git LFS notes If your uploads fail with: ``` Git LFS upload failed: 14% (1/7), 4.2 MB \| 0 B/s (missing) data/train-00000-of-00001.parquet (425c88744455a9b0e7248cdd81fe4716085aae22849798f653f59fc878117a4d) hint: Your push was rejected due to missing or corrupt local objects. hint: You can disable this check with: `git config lfs.allowincompletepush true` ``` First fetch all lfs objects: ``` git lfs fetch --all origin main ``` ### Filtering script (basic) To filter data, run the following script: ``` python scripts/filter.py subset-name 0 ``` with a subset from the dataset and a start index. --- ## Citation ``` @misc{RewardBench, title={RewardBench: Evaluating Reward Models for Language Modeling}, author={Lambert, Nathan and Pyatkin, Valentina and Morrison, Jacob and Miranda, LJ and Lin, Bill Yuchen and Chandu, Khyathi and Dziri, Nouha and Kumar, Sachin and Zick, Tom and Choi, Yejin and Smith, Noah A. and Hajishirzi, Hannaneh}, year={2024}, howpublished={\url{https://huggingface.co/spaces/allenai/reward-bench} } ```
nvidia/ChatQA2-Long-SFT-data	nvidia	2024-09-09T05:39:09Z	287	28	[ "language:en", "license:cc-by-nc-2.0", "size_categories:100K<n<1M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2407.14482", "region:us", "RAG", "conversational QA", "multi-turn QA", "QA with context", "train" ]	[]	2024-09-08T07:25:03Z	2	--- license: cc-by-nc-2.0 size_categories: - 10K<n<100K language: - en tags: - RAG - conversational QA - multi-turn QA - QA with context - train configs: - config_name: long_sft data_files: - split: train path: long_sft/long_sft_QA_train.json - split: test path: long_sft/long_sft_QA_dev.json - config_name: NarrativeQA_131072 data_files: - split: train path: NarrativeQA_131072/NarrativeQA_131072_QA_train.json - split: test path: NarrativeQA_131072/NarrativeQA_131072_QA_dev.json --- ## Data Description Here, we release the full long SFT training dataset of [ChatQA2](https://arxiv.org/abs/2407.14482). It consists of two parts: long_sft and NarrativeQA_131072. The long_sft dataset is built and derived from existing datasets: [LongAlpaca12k](https://github.com/dvlab-research/LongLoRA), GPT-4 samples from [Open Orca](https://huggingface.co/datasets/Open-Orca/OpenOrca), and [Long Data Collections](https://huggingface.co/datasets/togethercomputer/Long-Data-Collections). The NarrativeQA_131072 dataset is synthetically generated from NarrativeQA by adding related paragraphs to the given ground truth summary. For the first two steps training of ChatQA-2, we follow [ChatQA1.5](https://huggingface.co/datasets/nvidia/ChatQA-Training-Data). For the continue pretraining dataset, we simply follow [Long-Context Data Engineering](https://github.com/FranxYao/Long-Context-Data-Engineering) to generate 10B tokens. For more information about ChatQA-2, check the [website](https://chatqa2-project.github.io/)! ## Other Resources [Llama3-ChatQA-2-8B](https://huggingface.co/nvidia/Llama3-ChatQA-2-8B) &ensp; [Llama3-ChatQA-1.5-70B](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-70B) &ensp; [Evaluation Data](https://huggingface.co/nvidia/Llama3-ChatQA-2-70B/tree/main/data) &ensp; [Website](https://chatqa2-project.github.io/) &ensp; [Paper](https://arxiv.org/abs/2407.14482) ## Training Details The training follows a three-stage instruction tuning process. For the first two stages, we follow ChatQA-1.5, i.e., the stage-1 uses the SFT data, and the stage-2 uses a blend of SFT data alongside other datasets. The dataset blending ratio for stage-2 is as follows: - drop: 0.069 - narrativeqa: 0.095 - quoref: 0.026 - ropes: 0.026 - squad1.1: 0.095 - squad2.0: 0.095 - newsqa: 0.095 - tatqa-arithmetic: 0.15 - tatqa-others: 0.08 - synthetic_convqa: 0.3 - sft: 0.2 The stage-3 add the full long SFT dataset to the blend. And the new dataset blending ratio for stage-3 is as follows: - drop: 0.069 - narrativeqa: 0.095 - quoref: 0.026 - ropes: 0.026 - squad1.1: 0.095 - squad2.0: 0.095 - newsqa: 0.095 - tatqa-arithmetic: 0.15 - tatqa-others: 0.08 - synthetic_convqa: 0.3 - sft: 0.2 - long_sft: 2.5 - NarrativeQA_131072: 5.0 ## License The dataset is released for non-commercial use only, subject to [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI. ## Correspondence to Peng Xu ([email protected]), Wei Ping ([email protected]) ## Citation <pre> @article{xu2024chatqa, title={ChatQA 2: Bridging the Gap to Proprietary LLMs in Long Context and RAG Capabilities}, author={Xu, Peng and Ping, Wei and Wu, Xianchao and Liu, Zihan and Shoeybi, Mohammad and Catanzaro, Bryan}, journal={arXiv preprint arXiv:2407.14482}, year={2024} } </pre>
joujiboi/japanese-anime-speech	joujiboi	2024-09-06T18:19:39Z	1,287	124	[ "task_categories:automatic-speech-recognition", "language:ja", "license:cc0-1.0", "size_categories:10K<n<100K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "anime", "japanese", "日本語", "nihongo", "speech", "audio-text", "asr", "whisper", "voice", "large-v3", "ja", "jp" ]	[ "automatic-speech-recognition" ]	2023-11-07T13:53:40Z	null	--- language: - ja license: cc0-1.0 size_categories: - 10K<n<100K task_categories: - automatic-speech-recognition pretty_name: Japanese-Anime-Speech dataset_info: features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 10116168716.932 num_examples: 73004 download_size: 8832932312 dataset_size: 10116168716.932 configs: - config_name: default data_files: - split: train path: data/train-* tags: - anime - japanese - 日本語 - nihongo - speech - audio-text - asr - whisper - voice - large-v3 - ja - jp --- # Japanese Anime Speech Dataset [日本語はこちら](https://huggingface.co/datasets/joujiboi/japanese-anime-speech/blob/main/README_JA.md) japanese-anime-speech is an audio-text dataset designed for the training of automatic speech recognition models. The dataset is comprised of thousands of audio clips and their corresponding transcriptions from different visual novels. The goal of this dataset is to increase the accuracy of automatic speech recognition models, such as OpenAI's [Whisper](https://huggingface.co/openai/whisper-large-v2), in accurately transcribing dialogue from anime and other similar Japanese media. This genre is characterized by unique linguistic features and speech patterns that diverge from conventional Japanese speech. A list of all audio files and transcriptions can be found [here](https://huggingface.co/datasets/joujiboi/japanese-anime-speech/raw/main/audio_transcription_list.txt). <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Content Warning:</b> Please be advised that the majority of the audio in this dataset is sourced from visual novels and may include content that is not suitable for all audiences, such as suggestive sounds or mature topics. Efforts have been undertaken to minimise this content as much as possible. </p> </div> # Dataset information * 73,004 audio-text pairs * 110 hours of audio (OpenAI suggests a minimum of [5 hours](https://huggingface.co/blog/fine-tune-whisper) for productive [Whisper](https://huggingface.co/openai/whisper-large-v2) fine-tuning). * 5.4s average audio length * Audio source: visual novels * Lastest version: V5 - March 22nd 2024 # Changelog * V1 - This version contains 16,143 audio-text pairs from the visual novel `IxSHE Tell`. Some cleaning of the transcriptions has been done to get rid of unwanted characters at the start and end of lines. * V2 - The version contains 23,422 audio-text pairs from three different visual novels. Cleaning has been done to remove most nsfw lines, especially noises that aren't words. The audio is now in mp3 format, rather than wav. This version contains 32.6 hours of audio. * V3 - The version contains 38,325 audio-text pairs from five different visual novels. Thorough cleaning has been done to remove most nsfw or low-quality audio files. Transcriptions have been formatted to contain much fewer dramatised duplicated characters (for example 「ああああーーー」), and transcriptions have been made much more consistent. This version contains 52.5 hours of audio. * V4 - The dataset contains 47,844 audio-text pairs from six different visual novels. Thorough cleaning has been done to remove most nsfw or low-quality audio files. This version contains 63.4 hours of audio. * V5 - The dataset contains 73,004 audio-text pairs from eight different visual novels. Thorough cleaning has been done to remove most nsfw or low-quality audio files. This version contains 110 hours of audio. # Bias and Limitations This dataset, while valuable for training anime-style Japanese speech recognition, has some inherent biases and limitations. The audio is primarily sourced from visual novels, leading to a gender bias towards female voices and a domain-specific vocabulary revolving around topics such as love, relationships, and fantasy. Additionally, the professionally produced nature of the audio results in clear and slow speech, which may not fully reflect real-world speaking patterns. # Use & Credit This dataset is openly available for commercial or non-commercial use. Anyone is welcome to use this dataset as they deem appropriate. However, the creator assumes no responsibility for the consequences of its use. While not mandatory, crediting this dataset with a hyperlink in any derivative work would be greatly appreciated. I hope that by sharing this dataset, we (the open-source community) improve automatic speech recognition for anime content.
MoreTorque/rlogs	MoreTorque	2024-09-06T18:18:47Z	10,867	0	[ "license:apache-2.0", "region:us" ]	[]	2024-06-22T21:22:44Z	null	--- license: apache-2.0 --- ## Dataset Overview This dataset consists of bz2 compressed rlogs captured from OpenPilot. These logs do not contain any user-sensitive information. The primary purpose of this dataset is to facilitate data-driven solutions for improving lateral and longitudinal control in community-supported vehicles. # Contents The rlogs are composed of various events as defined in the OpenPilot log schema. To ensure user privacy, many event types have been excluded. The following is the list of events that remain in the log files: ``` * CarState * LiveParameters * CarControl * LateralPlanDEPRECATED * CarOutput * CarParams * ModelV2 * LiveTorqueParameters * LiveCalibration * Sendcan * Can * LongitudinalPlan * LiveLocationKalman ( Only OrientaionNED, CalibratedOrientaionNED, and AngularVelocityCalibrated have been kept )
4DR1455/finance_questions	4DR1455	2024-09-05T10:59:48Z	193	11	[ "language:en", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-06-20T11:17:35Z	2	--- language: - en --- Financial-instruction-aq22 dataset from DeividasM but with "input" to make it easier to use on a fine tunning template. Still working on it.
InfiniFlow/medical_QA	InfiniFlow	2024-09-05T03:23:52Z	187	27	[ "license:apache-2.0", "region:us" ]	[]	2024-09-05T02:20:22Z	2	--- license: apache-2.0 ---
alexandrainst/ftspeech	alexandrainst	2024-09-04T15:01:21Z	30,392	3	[ "task_categories:automatic-speech-recognition", "language:da", "license:other", "size_categories:1M<n<10M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2005.12368", "region:us" ]	[ "automatic-speech-recognition" ]	2023-05-11T13:08:57Z	null	--- dataset_info: features: - name: utterance_id dtype: string - name: speaker_gender dtype: string - name: sentence dtype: string - name: speaker_id dtype: string - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 209434570129.268 num_examples: 995677 - name: dev_balanced num_bytes: 579692770.829 num_examples: 2601 - name: dev_other num_bytes: 1725502342.095 num_examples: 7595 - name: test_balanced num_bytes: 1158740779.222 num_examples: 5534 - name: test_other num_bytes: 1254987645.527 num_examples: 5837 download_size: 101776974871 dataset_size: 214153493666.941 task_categories: - automatic-speech-recognition language: - da pretty_name: FT Speech size_categories: - 100K<n<1M license: other --- # Dataset Card for FT Speech ## Dataset Description - Repository: <https://ftspeech.github.io/> - Point of Contact: [Dan Saattrup Nielsen](mailto:[email protected]) - Size of downloaded dataset files: 101.78 GB - Size of the generated dataset: 214.15 GB - Total amount of disk used: 315.93 GB ### Dataset Summary This dataset is an upload of the [FT Speech dataset](https://ftspeech.github.io/). The training, validation and test splits are the original ones. ### Supported Tasks and Leaderboards Training automatic speech recognition is the intended task for this dataset. No leaderboard is active at this point. ### Languages The dataset is available in Danish (`da`). ## Dataset Structure ### Data Instances - Size of downloaded dataset files: 101.78 GB - Size of the generated dataset: 214.15 GB - Total amount of disk used: 315.93 GB An example from the dataset looks as follows. ``` { 'utterance_id': 'S001_20151_M012_P00034-2', 'speaker_gender': 'F', 'sentence': 'alle de fem tekniske justeringer der er en del af lovforslaget', 'speaker_id': 'S001', 'audio': { 'path': 'S001_20151_M012_P00034-2.wav', 'array': array([-3.75366211e-03, -5.27954102e-03, -3.87573242e-03, ..., 9.15527344e-05, -1.52587891e-04, 5.79833984e-04]), 'sampling_rate': 16000 } } ``` ### Data Fields The data fields are the same among all splits. - `utterance_id`: a `string` feature. - `speaker_gender`: a `string` feature. - `sentence`: a `string` feature. - `speaker_id`: a `string` feature. - `audio`: an `Audio` feature. ### Dataset Statistics There are 995,677 samples in the training split, 2,601 in the dev_balanced split, 7,595 in the dev_other split, 5,534 in the test_balanced and 5,837 in the test_other split. #### Speakers There are 374 unique speakers in the training dataset, 20 unique speakers in the validation dataset and 40 unique speakers in the test dataset. None of the dataset splits share any speakers. #### Gender Distribution ![ftspeech-gender-distribution.png](https://cdn-uploads.huggingface.co/production/uploads/60d368a613f774189902f555/0h_L7-riNfQbKFdYWgy01.png) #### Transcription Length Distribution ![ftspeech-length-distribution.png](https://cdn-uploads.huggingface.co/production/uploads/60d368a613f774189902f555/z1MqsvACrY_8XNXAx0UcD.png) ## Dataset Creation ### Curation Rationale There are not many large-scale ASR datasets in Danish. ### Source Data The data constitutes public recordings of sessions from the Danish Parliament, along with manual transcriptions. ## Additional Information ### Dataset Curators Andreas Kirkedal, Marija Stepanović and Barbara Plank curated the dataset as part of their FT Speech paper (see citation below). [Dan Saattrup Nielsen](https://saattrupdan.github.io/) from the [The Alexandra Institute](https://alexandra.dk/) reorganised the dataset and uploaded it to the Hugging Face Hub. ### Licensing Information The dataset is licensed under [this custom license](https://www.ft.dk/da/aktuelt/tv-fra-folketinget/deling-og-rettigheder). ### Citation ``` @inproceedings{ftspeech, author = {Kirkedal, Andreas and Stepanović, Marija and Plank, Barbara}, title = {{FT Speech: Danish Parliament Speech Corpus}}, booktitle = {Proc. Interspeech 2020}, year = {2020}, url = {arxiv.org/abs/2005.12368} } ```
BleachNick/UltraEdit	BleachNick	2024-08-31T13:49:21Z	52,807	8	[ "task_categories:text-to-image", "language:en", "license:cc-by-4.0", "arxiv:2407.05282", "doi:10.57967/hf/2481", "region:us", "art" ]	[ "text-to-image" ]	2024-06-09T11:02:13Z	null	--- language: - en license: cc-by-4.0 task_categories: - text-to-image dataset_info: features: - name: clip_sim_source dtype: float64 - name: clip_sim_target dtype: float64 - name: clip_sim_dir dtype: float64 - name: clip_sim_image dtype: float64 - name: dinov2_sim dtype: float64 - name: ssim dtype: float64 - name: source_caption dtype: string - name: target_caption dtype: string - name: idx dtype: int64 - name: edit_prompt dtype: string - name: edit_object dtype: 'null' - name: source_image dtype: image - name: edited_image dtype: image - name: mask_image dtype: 'null' splits: - name: FreeForm_0 num_bytes: 759385792 num_examples: 2000 - name: FreeForm_1 num_bytes: 756874067 num_examples: 2000 - name: FreeForm_2 num_bytes: 759183069 num_examples: 2000 - 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config_name: default data_files: - split: FreeForm_0 path: data/FreeForm_0-* - split: FreeForm_1 path: data/FreeForm_1-* - split: FreeForm_2 path: data/FreeForm_2-* - split: FreeForm_3 path: data/FreeForm_3-* - split: FreeForm_4 path: data/FreeForm_4-* - split: FreeForm_5 path: data/FreeForm_5-* - split: FreeForm_6 path: data/FreeForm_6-* - split: FreeForm_7 path: data/FreeForm_7-* - split: FreeForm_8 path: data/FreeForm_8-* - split: FreeForm_9 path: data/FreeForm_9-* - split: FreeForm_10 path: data/FreeForm_10-* - split: FreeForm_11 path: data/FreeForm_11-* - split: FreeForm_12 path: data/FreeForm_12-* - split: FreeForm_13 path: data/FreeForm_13-* - split: FreeForm_14 path: data/FreeForm_14-* - split: FreeForm_15 path: data/FreeForm_15-* - split: FreeForm_16 path: data/FreeForm_16-* - split: FreeForm_17 path: data/FreeForm_17-* - split: FreeForm_18 path: data/FreeForm_18-* - split: FreeForm_19 path: data/FreeForm_19-* - split: FreeForm_20 path: data/FreeForm_20-* - split: FreeForm_21 path: data/FreeForm_21-* - split: FreeForm_22 path: data/FreeForm_22-* - split: FreeForm_23 path: data/FreeForm_23-* - split: FreeForm_24 path: data/FreeForm_24-* - split: FreeForm_25 path: data/FreeForm_25-* - split: FreeForm_26 path: data/FreeForm_26-* - split: FreeForm_27 path: data/FreeForm_27-* - split: FreeForm_28 path: data/FreeForm_28-* - split: FreeForm_29 path: data/FreeForm_29-* - split: FreeForm_30 path: data/FreeForm_30-* - split: FreeForm_31 path: data/FreeForm_31-* - split: FreeForm_32 path: data/FreeForm_32-* - split: FreeForm_33 path: data/FreeForm_33-* - split: FreeForm_34 path: data/FreeForm_34-* - split: FreeForm_35 path: data/FreeForm_35-* - split: FreeForm_36 path: data/FreeForm_36-* - split: FreeForm_37 path: data/FreeForm_37-* - split: FreeForm_38 path: data/FreeForm_38-* - split: FreeForm_39 path: data/FreeForm_39-* - split: FreeForm_40 path: data/FreeForm_40-* - split: FreeForm_41 path: data/FreeForm_41-* - split: FreeForm_42 path: data/FreeForm_42-* - split: FreeForm_43 path: data/FreeForm_43-* - split: FreeForm_44 path: data/FreeForm_44-* - split: FreeForm_45 path: data/FreeForm_45-* - split: FreeForm_46 path: data/FreeForm_46-* - split: FreeForm_47 path: data/FreeForm_47-* - split: FreeForm_48 path: data/FreeForm_48-* - split: FreeForm_49 path: data/FreeForm_49-* - split: FreeForm_50 path: data/FreeForm_50-* - split: FreeForm_51 path: data/FreeForm_51-* - split: FreeForm_52 path: data/FreeForm_52-* - split: FreeForm_53 path: data/FreeForm_53-* - split: FreeForm_54 path: data/FreeForm_54-* - split: FreeForm_55 path: data/FreeForm_55-* - split: FreeForm_56 path: data/FreeForm_56-* - split: FreeForm_57 path: data/FreeForm_57-* - split: FreeForm_58 path: data/FreeForm_58-* - split: FreeForm_59 path: data/FreeForm_59-* - split: FreeForm_60 path: data/FreeForm_60-* - split: FreeForm_61 path: data/FreeForm_61-* - split: FreeForm_62 path: data/FreeForm_62-* - split: FreeForm_63 path: data/FreeForm_63-* - split: FreeForm_64 path: data/FreeForm_64-* - split: FreeForm_65 path: data/FreeForm_65-* - split: FreeForm_66 path: data/FreeForm_66-* - split: FreeForm_67 path: data/FreeForm_67-* - split: FreeForm_68 path: data/FreeForm_68-* - split: FreeForm_69 path: data/FreeForm_69-* - split: FreeForm_70 path: data/FreeForm_70-* - split: FreeForm_71 path: data/FreeForm_71-* - split: FreeForm_72 path: data/FreeForm_72-* - split: FreeForm_73 path: data/FreeForm_73-* - split: FreeForm_74 path: data/FreeForm_74-* - split: FreeForm_75 path: data/FreeForm_75-* - split: FreeForm_76 path: data/FreeForm_76-* - split: FreeForm_77 path: data/FreeForm_77-* - split: FreeForm_78 path: data/FreeForm_78-* - split: FreeForm_79 path: data/FreeForm_79-* - split: FreeForm_80 path: data/FreeForm_80-* - split: FreeForm_81 path: data/FreeForm_81-* - split: FreeForm_82 path: data/FreeForm_82-* - split: FreeForm_83 path: data/FreeForm_83-* - split: FreeForm_84 path: data/FreeForm_84-* - split: FreeForm_85 path: data/FreeForm_85-* - split: FreeForm_86 path: data/FreeForm_86-* - split: FreeForm_87 path: data/FreeForm_87-* - split: FreeForm_88 path: data/FreeForm_88-* - split: FreeForm_89 path: data/FreeForm_89-* - split: FreeForm_90 path: data/FreeForm_90-* - split: FreeForm_91 path: data/FreeForm_91-* - split: FreeForm_92 path: data/FreeForm_92-* - split: FreeForm_93 path: data/FreeForm_93-* - split: FreeForm_94 path: data/FreeForm_94-* - split: FreeForm_95 path: data/FreeForm_95-* - split: FreeForm_96 path: data/FreeForm_96-* - split: FreeForm_97 path: data/FreeForm_97-* - split: FreeForm_98 path: data/FreeForm_98-* - split: FreeForm_99 path: data/FreeForm_99-* - split: FreeForm_100 path: data/FreeForm_100-* - split: FreeForm_101 path: data/FreeForm_101-* - split: FreeForm_102 path: data/FreeForm_102-* - split: FreeForm_103 path: data/FreeForm_103-* - split: FreeForm_104 path: data/FreeForm_104-* - split: FreeForm_105 path: data/FreeForm_105-* - split: FreeForm_106 path: data/FreeForm_106-* - split: FreeForm_107 path: data/FreeForm_107-* - split: FreeForm_108 path: data/FreeForm_108-* - split: FreeForm_109 path: data/FreeForm_109-* - split: FreeForm_110 path: data/FreeForm_110-* - split: FreeForm_111 path: data/FreeForm_111-* - split: FreeForm_112 path: data/FreeForm_112-* - split: FreeForm_113 path: data/FreeForm_113-* - split: FreeForm_114 path: data/FreeForm_114-* - split: FreeForm_115 path: data/FreeForm_115-* - split: FreeForm_116 path: data/FreeForm_116-* - split: FreeForm_117 path: data/FreeForm_117-* - split: FreeForm_118 path: data/FreeForm_118-* - split: FreeForm_119 path: data/FreeForm_119-* - split: FreeForm_120 path: data/FreeForm_120-* - split: FreeForm_121 path: data/FreeForm_121-* - split: FreeForm_122 path: data/FreeForm_122-* - split: FreeForm_123 path: data/FreeForm_123-* - split: FreeForm_124 path: data/FreeForm_124-* - split: FreeForm_125 path: data/FreeForm_125-* - split: FreeForm_126 path: data/FreeForm_126-* - split: FreeForm_127 path: data/FreeForm_127-* - split: FreeForm_128 path: data/FreeForm_128-* - split: FreeForm_129 path: data/FreeForm_129-* - split: FreeForm_130 path: data/FreeForm_130-* - split: FreeForm_131 path: data/FreeForm_131-* - split: FreeForm_132 path: data/FreeForm_132-* - split: FreeForm_133 path: data/FreeForm_133-* - split: FreeForm_134 path: data/FreeForm_134-* - split: FreeForm_135 path: data/FreeForm_135-* - split: FreeForm_136 path: data/FreeForm_136-* - split: FreeForm_137 path: data/FreeForm_137-* - split: FreeForm_138 path: data/FreeForm_138-* - split: FreeForm_139 path: data/FreeForm_139-* - split: FreeForm_140 path: data/FreeForm_140-* - split: FreeForm_141 path: data/FreeForm_141-* - split: FreeForm_142 path: data/FreeForm_142-* - split: FreeForm_143 path: data/FreeForm_143-* - split: FreeForm_144 path: data/FreeForm_144-* - split: FreeForm_145 path: data/FreeForm_145-* - split: FreeForm_146 path: data/FreeForm_146-* - split: FreeForm_147 path: data/FreeForm_147-* - split: FreeForm_148 path: data/FreeForm_148-* - split: FreeForm_149 path: data/FreeForm_149-* - split: FreeForm_150 path: data/FreeForm_150-* - split: FreeForm_151 path: data/FreeForm_151-* - split: FreeForm_152 path: data/FreeForm_152-* - split: FreeForm_153 path: data/FreeForm_153-* - split: FreeForm_154 path: data/FreeForm_154-* - split: FreeForm_155 path: data/FreeForm_155-* - split: FreeForm_156 path: data/FreeForm_156-* - split: FreeForm_157 path: data/FreeForm_157-* - split: FreeForm_158 path: data/FreeForm_158-* - split: FreeForm_159 path: data/FreeForm_159-* - split: FreeForm_160 path: data/FreeForm_160-* - split: FreeForm_161 path: data/FreeForm_161-* - split: FreeForm_162 path: data/FreeForm_162-* - split: FreeForm_163 path: data/FreeForm_163-* - split: FreeForm_164 path: data/FreeForm_164-* - split: FreeForm_165 path: data/FreeForm_165-* - split: FreeForm_166 path: data/FreeForm_166-* - split: FreeForm_167 path: data/FreeForm_167-* - split: FreeForm_168 path: data/FreeForm_168-* - split: FreeForm_169 path: data/FreeForm_169-* - split: FreeForm_170 path: data/FreeForm_170-* - split: FreeForm_171 path: data/FreeForm_171-* - split: FreeForm_172 path: data/FreeForm_172-* - split: FreeForm_173 path: data/FreeForm_173-* - split: FreeForm_174 path: data/FreeForm_174-* - split: FreeForm_175 path: data/FreeForm_175-* - split: FreeForm_176 path: data/FreeForm_176-* - split: FreeForm_177 path: data/FreeForm_177-* - split: FreeForm_178 path: data/FreeForm_178-* - split: FreeForm_179 path: data/FreeForm_179-* - split: FreeForm_180 path: data/FreeForm_180-* - split: FreeForm_181 path: data/FreeForm_181-* - split: FreeForm_182 path: data/FreeForm_182-* - split: FreeForm_183 path: data/FreeForm_183-* - split: FreeForm_184 path: data/FreeForm_184-* - split: FreeForm_185 path: data/FreeForm_185-* - split: FreeForm_186 path: data/FreeForm_186-* - split: FreeForm_187 path: data/FreeForm_187-* - split: FreeForm_188 path: data/FreeForm_188-* - split: FreeForm_189 path: data/FreeForm_189-* - split: FreeForm_190 path: data/FreeForm_190-* - split: FreeForm_191 path: data/FreeForm_191-* - split: FreeForm_192 path: data/FreeForm_192-* - split: FreeForm_193 path: data/FreeForm_193-* - split: FreeForm_194 path: data/FreeForm_194-* - split: FreeForm_195 path: data/FreeForm_195-* - split: FreeForm_196 path: data/FreeForm_196-* - split: FreeForm_197 path: data/FreeForm_197-* - split: FreeForm_198 path: data/FreeForm_198-* - split: FreeForm_199 path: data/FreeForm_199-* - split: FreeForm_200 path: data/FreeForm_200-* - split: FreeForm_201 path: data/FreeForm_201-* - split: FreeForm_202 path: data/FreeForm_202-* - split: FreeForm_203 path: data/FreeForm_203-* - split: FreeForm_204 path: data/FreeForm_204-* - split: FreeForm_205 path: data/FreeForm_205-* - split: FreeForm_206 path: data/FreeForm_206-* - split: FreeForm_207 path: data/FreeForm_207-* - split: FreeForm_208 path: data/FreeForm_208-* - split: FreeForm_209 path: data/FreeForm_209-* - split: FreeForm_210 path: data/FreeForm_210-* - split: FreeForm_211 path: data/FreeForm_211-* - split: FreeForm_212 path: data/FreeForm_212-* - split: FreeForm_213 path: data/FreeForm_213-* - split: FreeForm_214 path: data/FreeForm_214-* - split: FreeForm_215 path: data/FreeForm_215-* - split: FreeForm_216 path: data/FreeForm_216-* - split: FreeForm_217 path: data/FreeForm_217-* - split: FreeForm_218 path: data/FreeForm_218-* - split: FreeForm_219 path: data/FreeForm_219-* - split: FreeForm_220 path: data/FreeForm_220-* - split: FreeForm_221 path: data/FreeForm_221-* - split: FreeForm_222 path: data/FreeForm_222-* - split: FreeForm_223 path: data/FreeForm_223-* - split: FreeForm_224 path: data/FreeForm_224-* - split: FreeForm_225 path: data/FreeForm_225-* - split: FreeForm_226 path: data/FreeForm_226-* - split: FreeForm_227 path: data/FreeForm_227-* - split: FreeForm_228 path: data/FreeForm_228-* - split: FreeForm_229 path: data/FreeForm_229-* - split: FreeForm_230 path: data/FreeForm_230-* - split: FreeForm_231 path: data/FreeForm_231-* - split: FreeForm_232 path: data/FreeForm_232-* - split: FreeForm_233 path: data/FreeForm_233-* - split: FreeForm_234 path: data/FreeForm_234-* - split: FreeForm_235 path: data/FreeForm_235-* - split: FreeForm_236 path: data/FreeForm_236-* - split: FreeForm_237 path: data/FreeForm_237-* - split: FreeForm_238 path: data/FreeForm_238-* - split: FreeForm_239 path: data/FreeForm_239-* - split: FreeForm_240 path: data/FreeForm_240-* - split: FreeForm_241 path: data/FreeForm_241-* - split: FreeForm_242 path: data/FreeForm_242-* - split: FreeForm_243 path: data/FreeForm_243-* - split: FreeForm_244 path: data/FreeForm_244-* - split: FreeForm_245 path: data/FreeForm_245-* - split: FreeForm_246 path: data/FreeForm_246-* - split: FreeForm_247 path: data/FreeForm_247-* - split: FreeForm_248 path: data/FreeForm_248-* - split: FreeForm_249 path: data/FreeForm_249-* - split: FreeForm_250 path: data/FreeForm_250-* - split: FreeForm_251 path: data/FreeForm_251-* - split: FreeForm_252 path: data/FreeForm_252-* - split: FreeForm_253 path: data/FreeForm_253-* - split: FreeForm_254 path: data/FreeForm_254-* - split: FreeForm_255 path: data/FreeForm_255-* - split: FreeForm_256 path: data/FreeForm_256-* - split: FreeForm_257 path: data/FreeForm_257-* - split: FreeForm_258 path: data/FreeForm_258-* - split: FreeForm_259 path: data/FreeForm_259-* - split: FreeForm_260 path: data/FreeForm_260-* - split: FreeForm_261 path: data/FreeForm_261-* - split: FreeForm_262 path: data/FreeForm_262-* - split: FreeForm_263 path: data/FreeForm_263-* - split: FreeForm_264 path: data/FreeForm_264-* - split: FreeForm_265 path: data/FreeForm_265-* - split: FreeForm_266 path: data/FreeForm_266-* - split: FreeForm_267 path: data/FreeForm_267-* - split: FreeForm_268 path: data/FreeForm_268-* - split: FreeForm_269 path: data/FreeForm_269-* - split: FreeForm_270 path: data/FreeForm_270-* - split: FreeForm_271 path: data/FreeForm_271-* - split: FreeForm_272 path: data/FreeForm_272-* - split: FreeForm_273 path: data/FreeForm_273-* - split: FreeForm_274 path: data/FreeForm_274-* - split: FreeForm_275 path: data/FreeForm_275-* - split: FreeForm_276 path: data/FreeForm_276-* - split: FreeForm_277 path: data/FreeForm_277-* - split: FreeForm_278 path: data/FreeForm_278-* - split: FreeForm_279 path: data/FreeForm_279-* - split: FreeForm_280 path: data/FreeForm_280-* - split: FreeForm_281 path: data/FreeForm_281-* - split: FreeForm_282 path: data/FreeForm_282-* - split: FreeForm_283 path: data/FreeForm_283-* - split: FreeForm_284 path: data/FreeForm_284-* - split: FreeForm_285 path: data/FreeForm_285-* - split: FreeForm_286 path: data/FreeForm_286-* - split: FreeForm_287 path: data/FreeForm_287-* - split: FreeForm_288 path: data/FreeForm_288-* - split: FreeForm_289 path: data/FreeForm_289-* - split: FreeForm_290 path: data/FreeForm_290-* - split: FreeForm_291 path: data/FreeForm_291-* - split: FreeForm_292 path: data/FreeForm_292-* - split: FreeForm_293 path: data/FreeForm_293-* - split: FreeForm_294 path: data/FreeForm_294-* - split: FreeForm_295 path: data/FreeForm_295-* - split: FreeForm_296 path: data/FreeForm_296-* - split: FreeForm_297 path: data/FreeForm_297-* - split: FreeForm_298 path: data/FreeForm_298-* - split: FreeForm_299 path: data/FreeForm_299-* - split: FreeForm_300 path: data/FreeForm_300-* - split: FreeForm_301 path: data/FreeForm_301-* - split: FreeForm_302 path: data/FreeForm_302-* - split: FreeForm_303 path: data/FreeForm_303-* - split: FreeForm_304 path: data/FreeForm_304-* - split: FreeForm_305 path: data/FreeForm_305-* - split: FreeForm_306 path: data/FreeForm_306-* - split: FreeForm_307 path: data/FreeForm_307-* - split: FreeForm_308 path: data/FreeForm_308-* - split: FreeForm_309 path: data/FreeForm_309-* - split: FreeForm_310 path: data/FreeForm_310-* - split: FreeForm_311 path: data/FreeForm_311-* - split: FreeForm_312 path: data/FreeForm_312-* - split: FreeForm_313 path: data/FreeForm_313-* - split: FreeForm_314 path: data/FreeForm_314-* - split: FreeForm_315 path: data/FreeForm_315-* - split: FreeForm_316 path: data/FreeForm_316-* - split: FreeForm_317 path: data/FreeForm_317-* - split: FreeForm_318 path: data/FreeForm_318-* - split: FreeForm_319 path: data/FreeForm_319-* - split: FreeForm_320 path: data/FreeForm_320-* - split: FreeForm_321 path: data/FreeForm_321-* - split: FreeForm_322 path: data/FreeForm_322-* - split: FreeForm_323 path: data/FreeForm_323-* - split: FreeForm_324 path: data/FreeForm_324-* - split: FreeForm_325 path: data/FreeForm_325-* - split: FreeForm_326 path: data/FreeForm_326-* - split: FreeForm_327 path: data/FreeForm_327-* - split: FreeForm_328 path: data/FreeForm_328-* - split: FreeForm_329 path: data/FreeForm_329-* - split: FreeForm_330 path: data/FreeForm_330-* - split: FreeForm_331 path: data/FreeForm_331-* - split: FreeForm_332 path: data/FreeForm_332-* - split: FreeForm_333 path: data/FreeForm_333-* - split: FreeForm_334 path: data/FreeForm_334-* - split: FreeForm_335 path: data/FreeForm_335-* - split: FreeForm_336 path: data/FreeForm_336-* - split: FreeForm_337 path: data/FreeForm_337-* - split: FreeForm_338 path: data/FreeForm_338-* - split: FreeForm_339 path: data/FreeForm_339-* - split: FreeForm_340 path: data/FreeForm_340-* - split: FreeForm_341 path: data/FreeForm_341-* - split: FreeForm_342 path: data/FreeForm_342-* - split: FreeForm_343 path: data/FreeForm_343-* - split: FreeForm_344 path: data/FreeForm_344-* - split: FreeForm_345 path: data/FreeForm_345-* - split: FreeForm_346 path: data/FreeForm_346-* - split: FreeForm_347 path: data/FreeForm_347-* - split: FreeForm_348 path: data/FreeForm_348-* - split: FreeForm_349 path: data/FreeForm_349-* - split: FreeForm_350 path: data/FreeForm_350-* - split: FreeForm_351 path: data/FreeForm_351-* - split: FreeForm_352 path: data/FreeForm_352-* - split: FreeForm_353 path: data/FreeForm_353-* - split: FreeForm_354 path: data/FreeForm_354-* - split: FreeForm_355 path: data/FreeForm_355-* - split: FreeForm_356 path: data/FreeForm_356-* - split: FreeForm_357 path: data/FreeForm_357-* - split: FreeForm_358 path: data/FreeForm_358-* - split: FreeForm_359 path: data/FreeForm_359-* - split: FreeForm_360 path: data/FreeForm_360-* - split: FreeForm_361 path: data/FreeForm_361-* - split: FreeForm_362 path: data/FreeForm_362-* - split: FreeForm_363 path: data/FreeForm_363-* - split: FreeForm_364 path: data/FreeForm_364-* - split: FreeForm_365 path: data/FreeForm_365-* - split: FreeForm_366 path: data/FreeForm_366-* - split: FreeForm_367 path: data/FreeForm_367-* - split: FreeForm_368 path: data/FreeForm_368-* - split: FreeForm_369 path: data/FreeForm_369-* - split: FreeForm_370 path: data/FreeForm_370-* - split: FreeForm_371 path: data/FreeForm_371-* - split: FreeForm_372 path: data/FreeForm_372-* - split: FreeForm_373 path: data/FreeForm_373-* - split: FreeForm_374 path: data/FreeForm_374-* - split: FreeForm_375 path: data/FreeForm_375-* - split: FreeForm_376 path: data/FreeForm_376-* - split: FreeForm_377 path: data/FreeForm_377-* - split: FreeForm_378 path: data/FreeForm_378-* - split: FreeForm_379 path: data/FreeForm_379-* - split: FreeForm_380 path: data/FreeForm_380-* - split: FreeForm_381 path: data/FreeForm_381-* - split: FreeForm_382 path: data/FreeForm_382-* - split: FreeForm_383 path: data/FreeForm_383-* - split: FreeForm_384 path: data/FreeForm_384-* - split: FreeForm_385 path: data/FreeForm_385-* - split: FreeForm_386 path: data/FreeForm_386-* - split: FreeForm_387 path: data/FreeForm_387-* - split: FreeForm_388 path: data/FreeForm_388-* - split: FreeForm_389 path: data/FreeForm_389-* - split: FreeForm_390 path: data/FreeForm_390-* - split: FreeForm_391 path: data/FreeForm_391-* - split: FreeForm_392 path: data/FreeForm_392-* - split: FreeForm_393 path: data/FreeForm_393-* - split: FreeForm_394 path: data/FreeForm_394-* - split: FreeForm_395 path: data/FreeForm_395-* - split: FreeForm_396 path: data/FreeForm_396-* - split: FreeForm_397 path: data/FreeForm_397-* - split: FreeForm_398 path: data/FreeForm_398-* - split: FreeForm_399 path: data/FreeForm_399-* - split: FreeForm_400 path: data/FreeForm_400-* - split: FreeForm_401 path: data/FreeForm_401-* - split: FreeForm_402 path: data/FreeForm_402-* - split: FreeForm_403 path: data/FreeForm_403-* - split: FreeForm_404 path: data/FreeForm_404-* - split: FreeForm_405 path: data/FreeForm_405-* - split: FreeForm_406 path: data/FreeForm_406-* - split: FreeForm_407 path: data/FreeForm_407-* - split: FreeForm_408 path: data/FreeForm_408-* - split: FreeForm_409 path: data/FreeForm_409-* - split: FreeForm_410 path: data/FreeForm_410-* - split: FreeForm_411 path: data/FreeForm_411-* - split: FreeForm_412 path: data/FreeForm_412-* - split: FreeForm_413 path: data/FreeForm_413-* - split: FreeForm_414 path: data/FreeForm_414-* - split: FreeForm_415 path: data/FreeForm_415-* - split: FreeForm_416 path: data/FreeForm_416-* - split: FreeForm_417 path: data/FreeForm_417-* - split: FreeForm_418 path: data/FreeForm_418-* - split: FreeForm_419 path: data/FreeForm_419-* - split: FreeForm_420 path: data/FreeForm_420-* - split: FreeForm_421 path: data/FreeForm_421-* - split: FreeForm_422 path: data/FreeForm_422-* - split: FreeForm_423 path: data/FreeForm_423-* - split: FreeForm_424 path: data/FreeForm_424-* - 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split: FreeForm_1975 path: data/FreeForm_1975-* - split: FreeForm_1699 path: data/FreeForm_1699-* - split: FreeForm_1755 path: data/FreeForm_1755-* - split: FreeForm_1757 path: data/FreeForm_1757-* - split: FreeForm_1763 path: data/FreeForm_1763-* - split: FreeForm_1814 path: data/FreeForm_1814-* - split: FreeForm_1816 path: data/FreeForm_1816-* - split: FreeForm_1821 path: data/FreeForm_1821-* - split: FreeForm_1856 path: data/FreeForm_1856-* - split: FreeForm_1862 path: data/FreeForm_1862-* - split: FreeForm_1873 path: data/FreeForm_1873-* - split: FreeForm_1875 path: data/FreeForm_1875-* - split: FreeForm_1877 path: data/FreeForm_1877-* - split: FreeForm_1935 path: data/FreeForm_1935-* - split: FreeForm_1936 path: data/FreeForm_1936-* - split: FreeForm_1938 path: data/FreeForm_1938-* - split: FreeForm_1939 path: data/FreeForm_1939-* - split: FreeForm_1941 path: data/FreeForm_1941-* - split: FreeForm_1977 path: data/FreeForm_1977-* - split: FreeForm_1981 path: data/FreeForm_1981-* - split: FreeForm_1984 path: data/FreeForm_1984-* - split: FreeForm_1985 path: data/FreeForm_1985-* - split: FreeForm_1987 path: data/FreeForm_1987-* - split: FreeForm_1989 path: data/FreeForm_1989-* - split: FreeForm_1990 path: data/FreeForm_1990-* - split: FreeForm_1993 path: data/FreeForm_1993-* - split: FreeForm_1996 path: data/FreeForm_1996-* - split: FreeForm_2000 path: data/FreeForm_2000-* tags: - art --- # Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed] ## Bibtex citation ```bibtex @misc{zhao2024ultraeditinstructionbasedfinegrainedimage, title={UltraEdit: Instruction-based Fine-Grained Image Editing at Scale}, author={Haozhe Zhao and Xiaojian Ma and Liang Chen and Shuzheng Si and Rujie Wu and Kaikai An and Peiyu Yu and Minjia Zhang and Qing Li and Baobao Chang}, year={2024}, eprint={2407.05282}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2407.05282}, } ```
amithm3/shrutilipi	amithm3	2024-08-31T05:27:21Z	15,355	3	[ "task_categories:automatic-speech-recognition", "language:kn", "language:sa", "language:bn", "language:pa", "language:ml", "language:gu", "language:ta", "language:te", "language:hi", "language:mr", "license:apache-2.0", "size_categories:1M<n<10M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "audio", "transcription", "AI4Bharat", "shrutilipi" ]	[ "automatic-speech-recognition" ]	2024-04-08T17:53:27Z	null	--- language: - kn - sa - bn - pa - ml - gu - ta - te - hi - mr license: apache-2.0 size_categories: - 1M<n<10M task_categories: - automatic-speech-recognition pretty_name: AI4Bharat Shrutilipi ASR Dataset dataset_info: - config_name: bn features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 59658532357.726 num_examples: 302349 - name: validation num_bytes: 6723169844.11 num_examples: 37602 - name: test num_bytes: 7660623563.6 num_examples: 38740 download_size: 74278694994 dataset_size: 74042325765.436 - config_name: gu features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 55793674372.628 num_examples: 329931 - name: validation num_bytes: 6293796356.189 num_examples: 40773 - name: test num_bytes: 7165218289.408 num_examples: 40853 download_size: 78346523702 dataset_size: 69252689018.225 - config_name: hi features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 213699256456.296 num_examples: 877604 - name: validation num_bytes: 27583551082.248 num_examples: 110692 - name: test num_bytes: 25110580660.236 num_examples: 108492 download_size: 269912939092 dataset_size: 266393388198.78 - config_name: kn features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 54770494386.876 num_examples: 278766 - name: validation num_bytes: 7864058142.98 num_examples: 34726 - name: test num_bytes: 7572538417.28 num_examples: 35166 download_size: 74257809304 dataset_size: 70207090947.136 - config_name: ml features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 71262913087.942 num_examples: 467414 - name: validation num_bytes: 7751159979.48 num_examples: 58985 - name: test num_bytes: 8930337765.4 num_examples: 59230 download_size: 99439381074 dataset_size: 87944410832.82199 - config_name: mr features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 125894833883.753 num_examples: 505639 - name: validation num_bytes: 14280421505.308 num_examples: 63407 - name: test num_bytes: 15230198579.815 num_examples: 63397 download_size: 147608513634 dataset_size: 155405453968.876 - config_name: pa features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 11549437955.164 num_examples: 41874 - name: validation num_bytes: 1317876276.359 num_examples: 5311 - name: test num_bytes: 1453641173.132 num_examples: 5139 download_size: 13966090670 dataset_size: 14320955404.654999 - config_name: sa features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 6219394271.104 num_examples: 11532 - name: validation num_bytes: 718650848.144 num_examples: 1408 - name: test num_bytes: 752806235.026 num_examples: 1474 download_size: 7321556791 dataset_size: 7690851354.274 - config_name: ta features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 101739123587.681 num_examples: 429417 - name: validation num_bytes: 12903430948.456 num_examples: 54012 - name: test num_bytes: 12724306851.984 num_examples: 53827 download_size: 126128595816 dataset_size: 127366861388.12099 - config_name: te features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 33158344172.292 num_examples: 155322 - name: validation num_bytes: 4085414503.579 num_examples: 19501 - name: test num_bytes: 4173443926.076 num_examples: 19189 download_size: 43278403108 dataset_size: 41417202601.94701 configs: - config_name: bn data_files: - split: train path: data/bn/train-* - split: validation path: data/bn/validation-* - split: test path: data/bn/test-* - config_name: gu data_files: - split: train path: data/gu/train-* - split: validation path: data/gu/validation-* - split: test path: data/gu/test-* - config_name: hi data_files: - split: train path: data/hi/train-* - split: validation path: data/hi/validation-* - split: test path: data/hi/test-* - config_name: kn data_files: - split: train path: data/kn/train-* - split: validation path: data/kn/validation-* - split: test path: data/kn/test-* - config_name: ml data_files: - split: train path: data/ml/train-* - split: validation path: data/ml/validation-* - split: test path: data/ml/test-* - config_name: mr data_files: - split: train path: data/mr/train-* - split: validation path: data/mr/validation-* - split: test path: data/mr/test-* - config_name: pa data_files: - split: train path: data/pa/train-* - split: validation path: data/pa/validation-* - split: test path: data/pa/test-* - config_name: sa data_files: - split: train path: data/sa/train-* - split: validation path: data/sa/validation-* - split: test path: data/sa/test-* - config_name: ta data_files: - split: train path: data/ta/train-* - split: validation path: data/ta/validation-* - split: test path: data/ta/test-* - config_name: te data_files: - split: train path: data/te/train-* - split: validation path: data/te/validation-* - split: test path: data/te/test-* tags: - audio - transcription - AI4Bharat - shrutilipi ---
NousResearch/hermes-function-calling-v1	NousResearch	2024-08-30T06:07:08Z	2,284	290	[ "task_categories:text-generation", "task_categories:question-answering", "task_categories:feature-extraction", "language:en", "license:apache-2.0", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[ "text-generation", "question-answering", "feature-extraction" ]	2024-08-14T01:22:36Z	null	--- license: apache-2.0 task_categories: - text-generation - question-answering - feature-extraction language: - en configs: - config_name: func_calling_singleturn data_files: "func-calling-singleturn.json" default: true - config_name: func_calling data_files: "func-calling.json" - config_name: glaive_func_calling data_files: "glaive-function-calling-5k.json" - config_name: json_mode_agentic data_files: "json-mode-agentic.json" - config_name: json_mode_singleturn data_files: "json-mode-singleturn.json" --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/nNQdNcHw9tvQw0AIPGW_i.png) # Hermes Function-Calling V1 This dataset is the compilation of structured output and function calling data used in the Hermes 2 Pro series of models. This repository contains a structured output dataset with function-calling conversations, json-mode, agentic json-mode and structured extraction samples, designed to train LLM models in performing function calls and returning structured output based on natural language instructions. The dataset features various conversational scenarios where AI agents are required to interpret queries and execute appropriate single or multiple function calls. The synthetic data generation was led by @interstellarninja in collaboration with @NousResearch, @teknium, @THEODOROS and many others who provided guidance. ## Hermes Function Calling Standard Hermes Function-calling Standard enables creation of LLM agents that are capable of executing API calls directly from user instructions. For instance, when asked to "find a flight from New York to Los Angeles for next Friday," a function-calling agent can interpret the request, generate the necessary function call (e.g., `search_flights`), and return the results. These agents significantly enhance the utility of AI by enabling direct interactions with APIs, making them invaluable in digital assistants across various domains. For a complete useage guide of models trained on this data, see our github repo: https://github.com/NousResearch/Hermes-Function-Calling ## Repository Structure There are 5 datasets that comprise the datamix, func-calling-singleturn.json - Single turn function calls func-calling.json - Multi-turn conversation function calls glaive-function-calling-5k.json - Updated and cleaned Glaive Function Calling 5k dataset by Glaive AI json-mode-agentic.json - Advanced JSON structured output samples json-mode-singleturn.json - Single turn JSON structured output samples The dataset has the following configs, used to load each file: - ```func_calling_singleturn``` - ```func_calling``` - ```glaive_func_calling``` - ```json_mode_agentic``` - ```json_mode_singleturn``` ### Inference Example Output Here's an example of the inference output: ``` <\|im_start\|>user Fetch the stock fundamentals data for Tesla (TSLA)<\|im_end\|> <\|im_start\|>assistant <tool_call> {'arguments': {'symbol': 'TSLA'}, 'name': 'get_stock_fundamentals'} </tool_call><\|im_end\|> <\|im_start\|>tool <tool_response> {"name": "get_stock_fundamentals", "content": {'symbol': 'TSLA', 'company_name': 'Tesla, Inc.', 'sector': 'Consumer Cyclical', 'industry': 'Auto Manufacturers', 'market_cap': 611384164352, 'pe_ratio': 49.604652, 'pb_ratio': 9.762013, 'dividend_yield': None, 'eps': 4.3, 'beta': 2.427, '52_week_high': 299.29, '52_week_low': 152.37}} </tool_response> <\|im_end\|> <\|im_start\|>assistant The stock fundamentals data for Tesla (TSLA) are as follows: - Symbol: TSLA - Company Name: Tesla, Inc. - Sector: Consumer Cyclical - Industry: Auto Manufacturers - Market Capitalization: $566,160,130,480 - Forward Price-to-Earnings Ratio (PE Ratio): 42.73 - Price-to-Book Ratio (PB Ratio): 9.04 - Dividend Yield: N/A - Trailing Earnings Per Share (EPS): $4.3 - Beta Value of the Stock: 2.42 - 52-Week High Price of the Stock: $299.29 - 52-Week Low Price of the Stock: $152.37 This information provides a snapshot of Tesla's financial position and performance based on the fundamental data obtained from the yfinance API. It shows that Tesla has a substantial market capitalization and a relatively high P/E and P/B ratio compared to other stocks in its industry. The company does not pay a dividend at the moment, which is reflected by a 'Dividend Yield' of 'None'. The Beta value indicates that Tesla's stock has a moderate level of volatility relative to the market. The 52-week high and low prices give an idea of the stock's range over the past year. This data can be useful when assessing investment opportunities and making investment decisions. ``` ### Hermes-2 Pro Prompt Format for Function Calling Our model was trained on specific system prompts and structures for Function Calling. You should use the system role with this message, followed by a function signature json as this example shows here. ``` <\|im_start\|>system You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: <tools> [{'type': 'function', 'function': {'name': 'get_stock_fundamentals', 'description': 'Get fundamental data for a given stock symbol using yfinance API.', 'parameters': {'type': 'object', 'properties': {'symbol': {'type': 'string'}}, 'required': ['symbol']}}}] </tools> Use the following pydantic model json schema for each tool call you will make: {'title': 'FunctionCall', 'type': 'object', 'properties': {'arguments': {'title': 'Arguments', 'type': 'object'}, 'name': {'title': 'Name', 'type': 'string'}}, 'required': ['arguments', 'name']} For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call> {'arguments': <args-dict>, 'name': <function-name>} </tool_call><\|im_end\|> ``` To complete the function call, create a user prompt that follows the above system prompt, like so: ``` <\|im_start\|>user Fetch the stock fundamentals data for Tesla (TSLA)<\|im_end\|> ``` The model will then generate a tool call, which your inference code must parse, and plug into a function ``` <\|im_start\|>assistant <tool_call> {'arguments': {'symbol': 'TSLA'}, 'name': 'get_stock_fundamentals'} </tool_call><\|im_end\|> ``` Once you parse the tool call, call the api and get the returned values for the call, and pass it back in as a new role, tool like so: ``` <\|im_start\|>tool <tool_response> {"name": "get_stock_fundamentals", "content": {'symbol': 'TSLA', 'company_name': 'Tesla, Inc.', 'sector': 'Consumer Cyclical', 'industry': 'Auto Manufacturers', 'market_cap': 611384164352, 'pe_ratio': 49.604652, 'pb_ratio': 9.762013, 'dividend_yield': None, 'eps': 4.3, 'beta': 2.427, '52_week_high': 299.29, '52_week_low': 152.37}} </tool_response> <\|im_end\|> ``` The assistant will then read in that data from the function's response, and generate a natural language response: ``` <\|im_start\|>assistant The stock fundamentals data for Tesla (TSLA) are as follows: - Symbol: TSLA - Company Name: Tesla, Inc. - Sector: Consumer Cyclical - Industry: Auto Manufacturers - Market Capitalization: $566,160,130,480 - Forward Price-to-Earnings Ratio (PE Ratio): 42.73 - Price-to-Book Ratio (PB Ratio): 9.04 - Dividend Yield: N/A - Trailing Earnings Per Share (EPS): $4.3 - Beta Value of the Stock: 2.42 - 52-Week High Price of the Stock: $299.29 - 52-Week Low Price of the Stock: $152.37 This information provides a snapshot of Tesla's financial position and performance based on the fundamental data obtained from the yfinance API. It shows that Tesla has a substantial market capitalization and a relatively high P/E and P/B ratio compared to other stocks in its industry. The company does not pay a dividend at the moment, which is reflected by a 'Dividend Yield' of 'None'. The Beta value indicates that Tesla's stock has a moderate level of volatility relative to the market. The 52-week high and low prices give an idea of the stock's range over the past year. This data can be useful when assessing investment opportunities and making investment decisions.<\|im_end\|> ``` ### Prompt Format for JSON Mode / Structured Outputs Our model was also trained on a specific system prompt for Structured Outputs, which should respond with only a json object response, in a specific json schema. Your schema can be made from a pydantic object using our codebase, with the standalone script jsonmode.py available here: https://github.com/NousResearch/Hermes-Function-Calling/tree/main ``` <\|im_start\|>system You are a helpful assistant that answers in JSON. Here's the json schema you must adhere to:\n<schema>\n{schema}\n</schema><\|im_end\|> ``` Given the {schema} that you provide, it should follow the format of that json to create it's response, all you have to do is give a typical user prompt, and it will respond in JSON. ## Dataset Structure The dataset follows a sharegpt structure. This means it is a list of dictionaries, with each dictionary containing a new list of dicts called conversations. Each turn in a conversation has two dictionaries, a "from" field, which denotes the role of that turn, and a "value" field which contains the actual text. ### Tool-use with API response: ```json { "id": "753d8365-0e54-43b1-9514-3f9b819fd31c", "conversations": [ { "from": "system", "value": "You are a function calling AI model. You are provided with function signatures within <tools> </tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions.\n<tools>\n[{'type': 'function', 'function': {'name': 'search_flights', 'description': 'Searches for flights based on departure and destination cities, dates, class, and other preferences.', 'parameters': {'type': 'object', 'properties': {'departure_city': {'type': 'string', 'description': 'The city from which the flight will depart.'}, 'destination_city': {'type': 'string', 'description': 'The destination city for the flight.'}, 'departure_date': {'type': 'string', 'description': 'The departure date for the flight.', 'format': 'date'}, 'return_date': {'type': 'string', 'description': 'The return date for the flight.', 'format': 'date'}, 'class': {'type': 'string', 'description': 'The class of the flight ticket.', 'enum': ['economy', 'business', 'first']}, 'flexible_cancellation': {'type': 'boolean', 'description': 'Indicates if the search should filter for flights with flexible cancellation policies.'}}, 'required': ['departure_city', 'destination_city', 'departure_date', 'return_date', 'class']}}}]\n</tools>\nFor each function call return a json object with function name and arguments within <tool_call> </tool_call> tags with the following schema:\n<tool_call>\n{'arguments': <args-dict>, 'name': <function-name>}\n</tool_call>\n" }, { "from": "human", "value": "I'm planning a kayaking trip and looking to book flights from Los Angeles to Auckland. My departure is scheduled for July 10th, 2023, and I intend to return on July 24th, 2023. I would prefer to travel in economy class and would also like the option to have flexible cancellation policies for the tickets due to the uncertain nature of outdoor activities. Could you please search for flights that meet these criteria and provide me with the available options?" }, { "from": "gpt", "value": "<tool_call>\n{'arguments': {'departure_city': 'Los Angeles', 'destination_city': 'Auckland', 'departure_date': '2023-07-10', 'return_date': '2023-07-24', 'class': 'economy', 'flexible_cancellation': True}, 'name': 'search_flights'}\n</tool_call>\n" }, { "from": "tool", "value": "<tool_response>\n{\"name\": \"search_flights\", \"content\": {\"flights\": [{\"flight_number\": \"AA118\", \"airline\": \"American Airlines\", \"departure_airport\": \"LAX\", \"departure_city\": \"Los Angeles\", \"departure_date\": \"2023-07-10T08:00:00Z\", \"departure_terminal\": \"4\", \"arrival_airport\": \"AKL\", \"arrival_city\": \"Auckland\", \"arrival_date\": \"2023-07-11T14:25:00Z\", \"arrival_terminal\": \"I\", \"duration\": \"17h 25m\", \"class\": \"Economy\", \"price\": \"$950\", \"flexible_cancellation\": true}, {\"flight_number\": \"UA796\", \"airline\": \"United Airlines\", \"departure_airport\": \"LAX\", \"departure_city\": \"Los Angeles\", \"departure_date\": \"2023-07-10T11:30:00Z\", \"departure_terminal\": \"7\", \"arrival_airport\": \"AKL\", \"arrival_city\": \"Auckland\", \"arrival_date\": \"2023-07-11T18:30:00Z\", \"arrival_terminal\": \"I\", \"duration\": \"16h 0m\", \"class\": \"Economy\", \"price\": \"$975\", \"flexible_cancellation\": true}, {\"flight_number\": \"NZ6\", \"airline\": \"Air New Zealand\", \"departure_airport\": \"LAX\", \"departure_city\": \"Los Angeles\", \"departure_date\": \"2023-07-10T21:00:00Z\", \"departure_terminal\": \"B\", \"arrival_airport\": \"AKL\", \"arrival_city\": \"Auckland\", \"arrival_date\": \"2023-07-12T05:20:00Z\", \"arrival_terminal\": \"I\", \"duration\": \"13h 20m\", \"class\": \"Economy\", \"price\": \"$1020\", \"flexible_cancellation\": true}], \"return_flights\": [{\"flight_number\": \"AA117\", \"airline\": \"American Airlines\", \"departure_airport\": \"AKL\", \"departure_city\": \"Auckland\", \"departure_date\": \"2023-07-24T14:10:00Z\", \"departure_terminal\": \"I\", \"arrival_airport\": \"LAX\", \"arrival_city\": \"Los Angeles\", \"arrival_date\": \"2023-07-24T06:00:00Z\", \"arrival_terminal\": \"4\", \"duration\": \"12h 50m\", \"class\": \"Economy\", \"price\": \"$950\", \"flexible_cancellation\": true}, {\"flight_number\": \"UA797\", \"airline\": \"United Airlines\", \"departure_airport\": \"AKL\", \"departure_city\": \"Auckland\", \"departure_date\": \"2023-07-24T19:15:00Z\", \"departure_terminal\": \"I\", \"arrival_airport\": \"LAX\", \"arrival_city\": \"Los Angeles\", \"arrival_date\": \"2023-07-24T10:15:00Z\", \"arrival_terminal\": \"7\", \"duration\": \"11h 0m\", \"class\": \"Economy\", \"price\": \"$975\", \"flexible_cancellation\": true}, {\"flight_number\": \"NZ5\", \"airline\": \"Air New Zealand\", \"departure_airport\": \"AKL\", \"departure_city\": \"Auckland\", \"departure_date\": \"2023-07-24T19:30:00Z\", \"departure_terminal\": \"I\", \"arrival_airport\": \"LAX\", \"arrival_city\": \"Los Angeles\", \"arrival_date\": \"2023-07-24T10:30:00Z\", \"arrival_terminal\": \"B\", \"duration\": \"12h 0m\", \"class\": \"Economy\", \"price\": \"$1020\", \"flexible_cancellation\": true}]}}\n</tool_response>\n" }, { "from": "gpt", "value": "Here are the available flight options with flexible cancellation policies for your kayaking trip from Los Angeles to Auckland:\n\nOutbound Flights (Los Angeles to Auckland):\n1. American Airlines AA118\n - Departure: July 10, 2023, 8:00 AM from LAX Terminal 4\n - Arrival: July 11, 2023, 2:25 PM at AKL Terminal I\n - Duration: 17h 25m\n - Price: $950\n\n2. United Airlines UA796\n - Departure: July 10, 2023, 11:30 AM from LAX Terminal 7\n - Arrival: July 11, 2023, 6:30 PM at AKL Terminal I\n - Duration: 16h 0m\n - Price: $975\n\n3. Air New Zealand NZ6\n - Departure: July 10, 2023, 9:00 PM from LAX Terminal B\n - Arrival: July 12, 2023, 5:20 AM at AKL Terminal I\n - Duration: 13h 20m\n - Price: $1020\n\nReturn Flights (Auckland to Los Angeles):\n1. American Airlines AA117\n - Departure: July 24, 2023, 2:10 PM from AKL Terminal I\n - Arrival: July 24, 2023, 6:00 AM at LAX Terminal 4\n - Duration: 12h 50m\n - Price: $950\n\n2. United Airlines UA797\n - Departure: July 24, 2023, 7:15 PM from AKL Terminal I\n - Arrival: July 24, 2023, 10:15 AM at LAX Terminal 7\n - Duration: 11h 0m\n - Price: $975\n\n3. Air New Zealand NZ5\n - Departure: July 24, 2023, 7:30 PM from AKL Terminal I\n - Arrival: July 24, 2023, 10:30 AM at LAX Terminal B\n - Duration: 12h 0m\n - Price: $1020\n\nAll the listed prices are in economy class with flexible cancellation policies. Please review the options and let me know if you would like to proceed with booking any of these flights or if you need further assistance." } ], "category": "E-commerce Platforms", "subcategory": "Kayak", "task": "Flight Search" } ``` ### Structured extraction with function-calling: ```json { "id": "afba1f0c-3212-467d-8594-d72a06da4d05", "conversations": [ { "from": "system", "value": "You are an expert structured information extraction AI model. You will be provided with documents to extract information from. You are also provided with the json schema to output extracted information in the function signatures within XML tags <tools></tools>. Don't make assumptions about what values to plug into json schema. \n<tools>\n[{\"type\": \"function\", \"function\": {\"name\": \"ExpertQAExtractor\", \"description\": \"Extracts a list of questions that ask how a concept or information from the document can be applied to a real-world situation. These assess ability to apply knowledge.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"application_questions\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"required\": [\"application_questions\"]}}}]\n</tools>\nFor each extraction function call return a json object with function name and arguments followed by a <tool_call> tag with the following schema:\n<tool_call>\n{'arguments': <args-dict>, 'name': <function-name>}\n</tool_call>" }, { "from": "human", "value": "Can you help me extract queries from the following passage <passage> : A directed graph. \n weighted, by the way. If a pair of vertices in such a graph is attached \"both ways,\" then each of the two edges will have its own weight. \n Washington, DC \n Fredericksburg \n Richmond \n Virginia Beach \n 50 \n 60 100 \n 150 \n Figure 5.3: A weighted (and undirected) graph. \nadjacent. If two vertices have an edge between them, they are said to be adjacent. \nconnected. The word connected has two meanings: it applies both to pairs of vertices and to entire graphs. We say that two vertices are connected if there is at least one path between them. Each vertex is therefore \"reachable\" from the other. In Figure 5.1, President and actor are connected, but Ford's Theatre and Civil War are not. \"Connected\" is also used to describe entire graphs, if _every_ node can be reached from all others. It's easy to see that Fig\n90 CHAPTER 5. STRUCTURES \n ure 5.3 is a connected graph, whereas Figure 5.1 is not (because Civil War and Gettysburg are isolated from the other nodes). It's not always trivial to determine whether a graph is connected, however: imagine a tangled morass of a million vertices, with ten million edges, and having to figure out whether or not every vertex is reachable from every other. (And if that seems unrealistically large, consider Facebook, which has over a billion nodes.) \ndegree. A vertex's degree is simply the number of edges that connect to it. Virginia Beach has degree 2, and Fredericksburg \n3. In the case of a directed graph, we sometimes distinguish between the number of incoming arrows a vertex has (called its in-degree ) and the number of outgoing arrows (the out- degree ). Muhammad Ali had a higher out-degree (3) than in-degree (1) since he won most of the time. \ncycle. A cycle is a path that begins and ends at the same vertex.^2 In Figure 5.3, Richmond-to-Virginia Beach-to-Fredericksburgto-Richmond is a cycle. Any loop is a cycle all by itself. For directed graphs, the entire loop must comprise edges in the \"forward\" direction: no fair going backwards. In Figure 5.2, Frazier-to-Ali-to-Foreman-to-Frazier is a cycle, as is the simpler Ali-to-Frazier-to-Ali. \nDAG (directed, acyclic graph). One common use of graphs is to represent flows of dependencies, for instance the prerequisites that different college courses have for one another. Another example is project management workflows: the tasks needed to complete a project become vertices, and then the dependencies they have on one another become edges. The graph in Figure 5.4 shows the steps in making a batch of brownies, and how these steps depend on each other. The eggs have to be cracked before the ingredients can be mixed, \n(^2) We'll also say that a cycle can't repeat any edges or vertices along the way, so that it can't go back and forth repeatedly and pointlessly between two adjacent nodes. Some mathematicians call this a simple cycle to distinguish it from the more general cycle , but we'll just say that no cycles can repeat like this. \n5.1. GRAPHS 91 \n and the oven has to be preheated before baking, but the pan can be greased any old time, provided that it's done before pouring the brown goop into it. \n mix ingredients \n pour brown stuff in bowl \n crack two eggs measure 2 tbsp oil \n preheat oven \n bake for 30 mins \n grease pan \n pour into pan \n cool \n enjoy! \n Figure 5.4: A DAG. \n A graph of dependencies like this must be both directed and acyclic , or it wouldn't make sense. Directed, of course, means that task X can require task Y to be completed before it, without the reverse also being true. If they both depended on each other, we'd have an infinite loop, and no brownies could ever get baked! Acyclic means that no kind of cycle can exist in the graph, even one that goes through multiple vertices. Such a cycle would again result in an infinite loop, making the project hopeless. Imagine if there were an arrow from bake for 30 mins back to grease pan in Figure 5.4. Then, we'd have to grease the pan before pouring the goop into it, and we'd have to pour the goop before baking, but we'd also have to bake before greasing the pan! We'd be stuck right off the bat: there'd be no way to complete any of those tasks since they'd all indirectly depend on each other. A graph that is both directed and acyclic (and therefore free of these problems) is sometimes called a DAG for short. \n92 CHAPTER 5. STRUCTURES \nSpatial positioning \nOne important thing to understand about graphs is which aspects of a diagram are relevant. Specifically, _the spatial positioning of the vertices doesn't matter._ In Figure 5.2 we drew Muhammad Ali in the mid-upper left, and Sonny Liston in the extreme upper right. But this was an arbitrary choice, and irrelevant. More specifically, this isn't part of the information the diagram claims to represent. We could have positioned the vertices differently, as in Figure 5.5, and had _the same graph_. In both diagrams, there are the same vertices, and the same edges between them (check me). Therefore, these are mathematically the same graph. \nGeorge Foreman Sonny Liston (^) Muhammad Ali Joe Frazier Figure 5.5: A different look to the same graph as Figure 5.2. This might not seem surprising for the prize fighter graph, but for graphs like the MapQuest graph, which actually represent physical locations, it can seem jarring. In Figure 5.3 we could have drawn Richmond north of Fredericksburg, and Virginia Beach on the far west side of the diagram, and still had the same graph, provided that all the nodes and links were the same. Just remember that the spatial positioning is designed for human convenience, and isn't part of the mathematical information. It's similar to how there's no order to the elements of a set, even though when we specify a set extensionally, we have to list them in _some_ order to avoid writing all the element names on top of each other. On a graph diagram, we have to draw each vertex _somewhere_ , but where we put it is simply aesthetic. \n5.1. GRAPHS 93 \nRelationship to sets \nWe seem to have strayed far afield from sets with all this graph stuff. But actually, there are some important connections to be made to those original concepts. Recall the wizards set A from chapter 3 that we extended to contain { Harry, Ron, Hermione, Neville }. Now consider the following endorelation on A: \n (Harry, Ron) (Ron, Harry) (Ron, Hermione) (Ron, Neville) (Hermione, Hermione) (Neville, Harry) \nThis relation, and all it contains, is represented faithfully by the graph in Figure 5.6. The elements of A are the vertices of course, and each ordered pair of the relation is reflected in an edge of the graph. Can you see how _exactly_ the same information is represented by both forms? \n Hermione \n Ron Neville \n Harry \n Figure 5.6: A graph depicting a endorelation. \nFigure 5.6 is a directed graph, of course. What if it were an undirected graph? The answer is that the corresponding relation would be _symmetric_. An undirected graph implies that if there's an edge between two vertices, it goes \"both ways.\" This is really identical to saying a relation is symmetric: if an (x, y) is in the relation, then the corresponding (y, x) must also be. An example is Figure 5.7, which depicts the following symmetric relation: \n94 CHAPTER 5. STRUCTURES \n (Harry, Ron) (Ron, Harry) (Ron, Hermione) (Hermione, Ron) (Harry, Harry) (Neville, Neville) \n Harry Ron \n Hermione Neville \n Figure 5.7: A graph depicting a symmetric endorelation. \nNotice how the loops (edges from a node back to itself) in these diagrams represent ordered pairs in which both elements are the same. \nAnother connection between graphs and sets has to do with partitions. Figure 5.7 was not a connected graph: Neville couldn't be reached from any of the other nodes. Now consider: isn't a graph like this similar in some ways to a _partition_ of A -- namely, this one? \n { Harry, Ron, Hermione } and { Neville }. \nWe've simply partitioned the elements of A into the groups that are connected. If you remove the edge between Harry and Ron in that graph, you have: \n { Harry }, { Ron, Hermione }, and { Neville }. \nThen add one between Hermione and Neville, and now you have: \n5.1. GRAPHS 95 \n { Harry } and { Ron, Hermione, Neville }. \nIn other words, the \"connectedness\" of a graph can be represented precisely as a partition of the set of vertices. Each connected subset is in its own group, and every vertex is in one and only one group: therefore, these isolated groups are mutually exclusive and collectively exhaustive. Cool. \nGraph traversal \nIf you had a long list -- perhaps of phone numbers, names, or purchase orders -- and you needed to go through and do something to each element of the list -- dial all the numbers, scan the list for a certain name, add up all the orders -- it'd be pretty obvious how to do it. You just start at the top and work your way down. It might be tedious, but it's not confusing. \nIterating through the elements like this is called traversing the data structure. You want to make sure you encounter each element once (and only once) so you can do whatever needs to be done with it. It's clear how to traverse a list. But how to traverse a graph? There is no obvious \"first\" or \"last\" node, and each one is linked to potentially many others. And as we've seen, the vertices might not even _be_ fully connected, so a traversal path through all the nodes might not even exist. \nThere are two different ways of traversing a graph: breadth-first, and depth-first. They provide different ways of exploring the nodes, and as a side effect, each is able to discover whether the graph is connected or not. Let's look at each in turn. \nBreadth-first traversal \nWith breadth-first traversal , we begin at a starting vertex (it doesn't matter which one) and explore the graph cautiously and delicately. We probe equally deep in all directions, making sure we've looked a little ways down each possible path before exploring each of those paths a little further. \n96 CHAPTER 5. STRUCTURES \nTo do this, we use a very simple data structure called a queue. A queue is simply a list of nodes that are waiting in line. (In Britain, I'm told, instead of saying \"line up\" at the sandwich shop, they say \"queue up.\") When we enter a node into the queue at the tail end, we call it enqueueing the node, and when we remove one from the front, we call it dequeueing the node. The nodes in the middle patiently wait their turn to be dealt with, getting closer to the front every time the front node is dequeued. \nAn example of this data structure in action is shown in Figure 5.8. Note carefully that we always insert nodes at one end (on the right) and remove them from the _other_ end (the left). This means that the first item to be enqueued (in this case, the triangle) will be the first to be dequeued. \"Calls will be answered in the order they were received.\" This fact has given rise to another name for a queue: a \" FIFO ,\" which stands for \"first-in-first-out.\" \n Start with an empty queue: \| Enqueue a triangle, and we have: \|^ Enqueue a star, and we have: \|^ Enqueue a heart, and we have: \|^ Dequeue the triangle, and we have: \| Enqueue a club, and we have: \| Dequeue the star, and we have: \| Dequeue the heart, and we have: \| Dequeue the club. We're empty again: \| \nFigure 5.8: A queue in action. The vertical bar marks the \"front of the line,\" and the elements are waiting to be dequeued in order from left to right. \nNow here's how we use a queue to traverse a graph breadth-first. We're going to start at a particular node, and put all of its adjacent nodes into a queue. This makes them all safely \"wait in line\" until we get around to exploring them. Then, we repeatedly take the first node in line, do whatever we need to do with it, and then put all of _its_ adjacent nodes in line. We keep doing this until the queue is empty. \n5.1. GRAPHS 97 \nNow it might have occurred to you that we can run into trouble if we encounter the same node multiple times while we're traversing. This can happen if the graph has a cycle: there will be more than one path to reach some nodes, and we could get stuck in an infinite loop if we're not careful. For this reason, we introduce the concept of marking nodes. This is kind of like leaving a trail of breadcrumbs: if we're ever about to explore a node, but find out it's marked, then we know we've already been there, and it's pointless to search it again. \nSo there </passage>" }, { "from": "gpt", "value": "<tool_call>\\n{\"arguments\": {\"queries\": ['How can the concept of graph traversal be applied to real-world situations, such as project management workflows or social networks?', 'Can you think of any other real-world scenarios where understanding and effectively using spatial positioning in a graph would be important?', 'In what ways can the concept of marking nodes be useful in problem-solving or decision-making processes?'], \"name\": \"ExpertQAExtractor\"}}\\n</tool_call>" } ], "category": "Information Extraction", "subcategory": "Json Schema", "task": "Structured json schema extaction with function calling" } ``` ### JSON-mode with pydantic JSON schema ```json { "id": "57aa5f1b-6af3-42b8-9598-d48540277354", "conversations": [ { "from": "system", "value": "You are a helpful assistant that answers in JSON. Here's the json schema you must adhere to:\n<schema>\n{'title': 'MovieDatabaseEntry', 'type': 'object', 'properties': {'movieId': {'title': 'Movie ID', 'type': 'string'}, 'title': {'title': 'Title', 'type': 'string'}, 'genre': {'title': 'Genre', 'type': 'string'}, 'director': {'title': 'Director', 'type': 'string'}, 'cast': {'title': 'Cast', 'type': 'array', 'items': {'type': 'string'}}}, 'required': ['movieId', 'title', 'genre', 'director', 'cast']}\n</schema>\n" }, { "from": "human", "value": "I'm organizing a movie database and need to create a structured JSON object for each film entry. The next movie I want to add is 'Interstellar', which has a unique movie ID 'INTSTL_2014'. The genre is 'Science Fiction', directed by Christopher Nolan. The main cast includes Matthew McConaughey as 'Cooper', Anne Hathaway as 'Brand', Jessica Chastain as 'Murph', and Michael Caine as 'Professor Brand'." }, { "from": "gpt", "value": "{\"movieId\": \"INTSTL_2014\", \"title\": \"Interstellar\", \"genre\": \"Science Fiction\", \"director\": \"Christopher Nolan\", \"cast\": [\"Matthew McConaughey as Cooper\", \"Anne Hathaway as Brand\", \"Jessica Chastain as Murph\", \"Michael Caine as Professor Brand\"]}" } ], "category": "Media & Entertainment", "subcategory": "Media", "schema": "{\"title\": \"MovieDatabaseEntry\", \"type\": \"object\", \"properties\": {\"movieId\": {\"title\": \"Movie ID\", \"type\": \"string\"}, \"title\": {\"title\": \"Title\", \"type\": \"string\"}, \"genre\": {\"title\": \"Genre\", \"type\": \"string\"}, \"director\": {\"title\": \"Director\", \"type\": \"string\"}, \"cast\": {\"title\": \"Cast\", \"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"required\": [\"movieId\", \"title\", \"genre\", \"director\", \"cast\"]}", "__index_level_0__": 671 } ``` ### JSON-mode with Agentic Schemas: ```json { "id": "c0618e9a-d832-4e2f-b9fe-28c8d8ca8007", "conversations": [ { "from": "system", "value": "You are a helpful assistant that answers in JSON. Here's the json schema you must adhere to:\n<schema>\n{'type': 'object', 'properties': {'agent_execution': {'type': 'object', 'properties': {'task_decomposition': {'type': 'string'}, 'action_retrieval': {'type': 'string'}, 'code_execution': {'type': 'string'}, 'error_analysis': {'type': 'string'}, 'code_amendment': {'type': 'string'}, 'execution_critique': {'type': 'string'}, 'task_replanning': {'type': 'string'}}, 'required': ['task_decomposition', 'action_retrieval', 'code_execution', 'error_analysis', 'code_amendment', 'execution_critique', 'task_replanning']}}}\n</schema>\n" }, { "from": "human", "value": "As a software engineer, I'm currently working on integrating an agent execution framework into our system to automate complex tasks. I need to generate a JSON object that represents the process of executing a task where the agent decomposes the task into subtasks, retrieves the necessary actions from an action library, and executes code to fulfill the task requirements. The task at hand is to automate the process of data migration from one database to another, ensuring data integrity and handling any errors that may arise. The agent should also provide a critique of the task execution and replan the task if necessary. Specifically, the agent decomposes the data migration task into subtasks such as data extraction, data transformation, and data loading. It then retrieves actions like 'extract_data', 'transform_data', and 'load_data' from the action library. Following this, the agent executes the retrieved code snippets for each subtask, ensuring data integrity throughout the migration process. During this process, the agent analyzes any errors encountered during execution, identifying issues such as data format mismatches or connectivity problems. Based on the error analysis, the agent amends the code to handle exceptions and retry the subtask. After the execution, the agent provides a critique of the execution, highlighting successful completions and areas that required amendments. If a subtask cannot be completed successfully after amendments, the agent replans the task by decomposing it into alternative subtasks, thus ensuring a robust and adaptive approach to task automation." }, { "from": "gpt", "value": "{\"agent_execution\": {\"task_decomposition\": \"The agent decomposes the data migration task into subtasks such as data extraction, data transformation, and data loading.\", \"action_retrieval\": \"The agent retrieves actions like 'extract_data', 'transform_data', and 'load_data' from the action library.\", \"code_execution\": \"The agent executes the retrieved code snippets for each subtask, ensuring data integrity throughout the migration process.\", \"error_analysis\": \"The agent analyzes any errors encountered during execution, identifying issues such as data format mismatches or connectivity problems.\", \"code_amendment\": \"The agent amends the code based on the error analysis to handle exceptions and retry the subtask.\", \"execution_critique\": \"The agent provides a critique of the execution, highlighting successful completions and areas that required amendments.\", \"task_replanning\": \"If a subtask cannot be completed successfully after amendments, the agent replans the task by decomposing it into alternative subtasks.\"}}" } ], "category": "Copilot Frameworks", "subcategory": "Agent Execution", "schema": "{\"type\": \"object\", \"properties\": {\"agent_execution\": {\"type\": \"object\", \"properties\": {\"task_decomposition\": {\"type\": \"string\"}, \"action_retrieval\": {\"type\": \"string\"}, \"code_execution\": {\"type\": \"string\"}, \"error_analysis\": {\"type\": \"string\"}, \"code_amendment\": {\"type\": \"string\"}, \"execution_critique\": {\"type\": \"string\"}, \"task_replanning\": {\"type\": \"string\"}}, \"required\": [\"task_decomposition\", \"action_retrieval\", \"code_execution\", \"error_analysis\", \"code_amendment\", \"execution_critique\", \"task_replanning\"]}}}" } ``` # How to cite: ```bibtext @misc{Hermes-Function-Calling-Dataset-V1, url={https://huggingface.co/NousResearch/hermes-function-calling-v1}, c title={Hermes-Function-Calling-Dataset-V1}, author={"interstellarninja", "Teknium"} } ```
Major-TOM/Core-S1RTC	Major-TOM	2024-08-29T16:17:53Z	27,095	12	[ "license:cc-by-sa-4.0", "size_categories:1M<n<10M", "format:parquet", "modality:image", "modality:tabular", "modality:text", "modality:geospatial", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2402.12095", "region:us", "earth-observation", "remote-sensing", "sentinel-1", "sar", "synthethic-aperture-radar", "satellite", "geospatial" ]	[]	2024-03-15T14:13:22Z	null	--- license: cc-by-sa-4.0 tags: - earth-observation - remote-sensing - sentinel-1 - sar - synthethic-aperture-radar - satellite - geospatial size_categories: - 1M<n<10M dataset_info: - config_name: default features: - name: product_id dtype: string - name: grid_cell dtype: string - name: product_datetime dtype: string - name: thumbnail dtype: image - name: vv dtype: binary - name: vh dtype: binary configs: - config_name: default data_files: images/*.parquet - config_name: metadata data_files: metadata.parquet --- # Core-S1RTC Contains a global coverage of Sentinel-1 (RTC) patches, each of size 1,068 x 1,068 pixels. \| Source \| Sensing Type \| Number of Patches \| Patch Size \| Total Pixels \| \|--------\|--------------\|-------------------\|------------\|--------------\| \|Sentinel-1 RTC \| Synthetic Aperture Radar \|1,469,955\|1,068 x 1,068 (10 m) \| > 1.676 Trillion \| ## Content \| Column \| Details \| Resolution \| \|--------\|---------\|------------\| \| VV \| Received Linear Power in the VV Polarization \| 10m \| \| VH \| Received Linear Power in the VV Polarization \| 10m \| \| thumbnail \| Rescaled false colour<sup>1</sup> saved as png \| 10m \| <sup>1</sup> False colour composites are made with decibel-scale values with red green and blue defined as ```R:VV G:VV+VH B:VH```. For each channel, a contrast-stretch is applied, transforming minimum-maximum to 0-255. This means bluer areas have relatively higher VH values, whilst brightness is a function of overall intensity. This is relative within each thumbnail because of the normalisation, and so cannot be compared across different samples. ## Spatial Coverage This is a global monotemporal dataset. Nearly every piece of Earth captured by Sentinel-1 is contained at least once in this dataset (and only once, excluding some marginal overlaps). The coverage is about 35% lower than for Core Sentinel-2 dataset due to the sensor coverage limitations. The following figure demonstrates the spatial coverage (only black pixels are absent): ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6304c06eeb6d777a838eab63/hFf98OyO25mL9Q962d5NL.png) ## Example Use Interface scripts are available at https://github.com/ESA-PhiLab/Major-TOM Here's a sneak peek with a thumbnail image: ```python from fsspec.parquet import open_parquet_file import pyarrow.parquet as pq from io import BytesIO from PIL import Image PARQUET_FILE = 'part_03900' # parquet number ROW_INDEX = 42 # row number (about 500 per parquet) url = "https://huggingface.co/datasets/Major-TOM/Core-S1RTC/resolve/main/images/{}.parquet".format(PARQUET_FILE) with open_parquet_file(url,columns = ["thumbnail"]) as f: with pq.ParquetFile(f) as pf: first_row_group = pf.read_row_group(ROW_INDEX, columns=['thumbnail']) stream = BytesIO(first_row_group['thumbnail'][0].as_py()) image = Image.open(stream) ``` ## Cite [![arxiv](https://img.shields.io/badge/Open_Access-arxiv:2402.12095-b31b1b)](https://arxiv.org/abs/2402.12095/) ```latex @inproceedings{Major_TOM, title={Major TOM: Expandable Datasets for Earth Observation}, author={Alistair Francis and Mikolaj Czerkawski}, year={2024}, booktitle={IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium}, eprint={2402.12095}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` Powered by [Φ-lab, European Space Agency (ESA) 🛰️](https://huggingface.co/ESA-philab)
Omartificial-Intelligence-Space/Arab3M-Triplets	Omartificial-Intelligence-Space	2024-08-29T12:10:58Z	45	4	[ "task_categories:sentence-similarity", "language:ar", "license:apache-2.0", "size_categories:1M<n<10M", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "STS", "Embeddings", "Arabic" ]	[ "sentence-similarity" ]	2024-07-11T10:24:46Z	3	--- license: apache-2.0 extra_gated_fields: Name: text Affilation: text Company: text Country: country Specific date: date_picker I want to use this dataset for: type: select options: - Research - Education - label: Other value: other I agree to use this dataset for non-commercial use ONLY: checkbox task_categories: - sentence-similarity language: - ar tags: - STS - Embeddings - Arabic pretty_name: Arab3M-Triplets size_categories: - 1M<n<10M --- # Arab3M-Triplets This dataset is designed for training and evaluating models using contrastive learning techniques, particularly in the context of natural language understanding. The dataset consists of triplets: an anchor sentence, a positive sentence, and a negative sentence. The goal is to encourage models to learn meaningful representations by distinguishing between semantically similar and dissimilar sentences. ## Dataset Overview - Format: Parquet - Number of rows: 3.03 million - Columns: - `anchor`: A sentence serving as the reference point. - `positive`: A sentence that is semantically similar to the `anchor`. - `negative`: A sentence that is semantically dissimilar to the `anchor`. ## Usage This dataset can be used to train models for various NLP tasks, including: - Sentence Similarity: Training models to identify sentences with similar meanings. - Contrastive Learning: Teaching models to differentiate between semantically related and unrelated sentences. - Representation Learning: Developing models that learn robust sentence embeddings. ### Loading the Dataset You can load the dataset using the Hugging Face `datasets` library: ```python from datasets import load_dataset dataset = load_dataset('Omartificial-Intelligence-Space/Arab3M-Triplets') ```
fixie-ai/covost2	fixie-ai	2024-08-27T20:58:08Z	17,492	2	[ "size_categories:1M<n<10M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-07-16T23:40:52Z	null	--- dataset_info: - config_name: ar_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 61607709.192 num_examples: 2283 - name: validation num_bytes: 56223234.024 num_examples: 1758 - name: test num_bytes: 54650910.41 num_examples: 1695 download_size: 160468333 dataset_size: 172481853.626 - config_name: ca_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 4397026262.322 num_examples: 95854 - name: validation num_bytes: 544108371.96 num_examples: 12730 - name: test num_bytes: 604755238.63 num_examples: 12730 download_size: 4957773433 dataset_size: 5545889872.912 - config_name: cy_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 51478765.087 num_examples: 1241 - name: validation num_bytes: 26992697.0 num_examples: 690 - name: test num_bytes: 28772216.0 num_examples: 690 download_size: 102604972 dataset_size: 107243678.087 - config_name: de_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 5680326209.222 num_examples: 127834 - name: validation num_bytes: 631442490.202 num_examples: 13511 - name: test num_bytes: 637042944.685 num_examples: 13511 download_size: 6490850158 dataset_size: 6948811644.108999 - 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name: id dtype: string splits: - name: train num_bytes: 36136135.768 num_examples: 1243 - name: validation num_bytes: 25058845.0 num_examples: 792 - name: test num_bytes: 26577467.0 num_examples: 844 download_size: 86110062 dataset_size: 87772447.768 - config_name: it_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 1517510665.568 num_examples: 31698 - name: validation num_bytes: 422409218.1 num_examples: 8940 - name: test num_bytes: 454569171.595 num_examples: 8951 download_size: 2125529183 dataset_size: 2394489055.2630005 - config_name: ja_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 38181610.624 num_examples: 1119 - name: validation num_bytes: 24623052.0 num_examples: 635 - name: test num_bytes: 25558787.0 num_examples: 684 download_size: 88228548 dataset_size: 88363449.624 - config_name: lv_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 66152116.328 num_examples: 2337 - name: validation num_bytes: 32655276.0 num_examples: 1125 - name: test num_bytes: 50997551.638 num_examples: 1629 download_size: 137700207 dataset_size: 149804943.96600002 - config_name: mn_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 87891433.547 num_examples: 2067 - name: validation num_bytes: 77519039.943 num_examples: 1761 - name: test num_bytes: 83667460.167 num_examples: 1759 download_size: 242638800 dataset_size: 249077933.657 - config_name: nl_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 216102081.4 num_examples: 7108 - name: validation num_bytes: 55386349.319 num_examples: 1699 - name: test num_bytes: 60219179.711 num_examples: 1699 download_size: 320267264 dataset_size: 331707610.43 - config_name: pt_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 274723273.528 num_examples: 9158 - name: validation num_bytes: 118345891.704 num_examples: 3318 - name: test num_bytes: 166247624.001 num_examples: 4023 download_size: 540891735 dataset_size: 559316789.233 - config_name: ru_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 557219472.672 num_examples: 12112 - name: validation num_bytes: 290218427.6 num_examples: 6110 - name: test num_bytes: 312622838.0 num_examples: 6300 download_size: 1112848246 dataset_size: 1160060738.272 - config_name: sl_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 55992153.0 num_examples: 1843 - name: validation num_bytes: 15074155.0 num_examples: 509 - name: test num_bytes: 10209711.0 num_examples: 360 download_size: 83863293 dataset_size: 81276019.0 - config_name: sv-SE_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 48298330.64 num_examples: 2160 - name: validation num_bytes: 32544646.416 num_examples: 1349 - name: test num_bytes: 46894324.615 num_examples: 1595 download_size: 121860373 dataset_size: 127737301.671 - config_name: ta_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 47757197.616 num_examples: 1358 - name: validation num_bytes: 13670695.0 num_examples: 384 - name: test num_bytes: 29891516.0 num_examples: 786 download_size: 87791516 dataset_size: 91319408.616 - config_name: tr_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 119299427.798 num_examples: 3966 - name: validation num_bytes: 52552534.232 num_examples: 1624 - name: test num_bytes: 59106253.862 num_examples: 1629 download_size: 224018260 dataset_size: 230958215.89200002 - config_name: zh-CN_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: validation num_bytes: 231018998.33 num_examples: 4843 - name: test num_bytes: 243850956.45 num_examples: 4898 - name: train num_bytes: 341425113.6 num_examples: 7085 download_size: 766660661 dataset_size: 816295068.38 configs: - config_name: ar_en data_files: - split: train path: ar_en/train-* - split: validation path: ar_en/validation-* - split: test path: ar_en/test-* - config_name: ca_en data_files: - split: train path: ca_en/train-* - split: validation path: ca_en/validation-* - split: test path: ca_en/test-* - config_name: cy_en data_files: - split: train path: cy_en/train-* - split: validation path: cy_en/validation-* - split: test path: cy_en/test-* - config_name: de_en data_files: - split: train path: de_en/train-* - split: validation path: de_en/validation-* - split: test path: de_en/test-* - config_name: en_ar data_files: - split: train path: en_ar/train-* - split: validation path: en_ar/validation-* - split: test path: en_ar/test-* - config_name: en_ca data_files: - split: train path: en_ca/train-* - split: validation path: en_ca/validation-* - split: test path: en_ca/test-* - config_name: en_cy data_files: - split: train path: en_cy/train-* - split: validation path: en_cy/validation-* - split: test path: en_cy/test-* - config_name: en_de data_files: - split: train path: en_de/train-* - split: validation path: en_de/validation-* - split: test path: en_de/test-* - config_name: en_et data_files: - split: train path: en_et/train-* - split: validation path: en_et/validation-* - split: test path: en_et/test-* - config_name: en_fa data_files: - split: train path: en_fa/train-* - split: validation path: en_fa/validation-* - split: test path: en_fa/test-* - config_name: en_id data_files: - split: train path: en_id/train-* - split: validation path: en_id/validation-* - split: test path: en_id/test-* - config_name: en_ja data_files: - split: train path: en_ja/train-* - split: validation path: en_ja/validation-* - split: test path: en_ja/test-* - config_name: en_lv data_files: - split: train path: en_lv/train-* - split: validation path: en_lv/validation-* - split: test path: en_lv/test-* - config_name: en_mn data_files: - split: train path: en_mn/train-* - split: validation path: en_mn/validation-* - split: test path: en_mn/test-* - config_name: en_sl data_files: - split: train path: en_sl/train-* - split: validation path: en_sl/validation-* - split: test path: en_sl/test-* - config_name: en_sv-SE data_files: - split: train path: en_sv-SE/train-* - split: validation path: en_sv-SE/validation-* - split: test path: en_sv-SE/test-* - config_name: en_ta data_files: - split: train path: en_ta/train-* - split: validation path: en_ta/validation-* - split: test path: en_ta/test-* - config_name: en_tr data_files: - split: train path: en_tr/train-* - split: validation path: en_tr/validation-* - split: test path: en_tr/test-* - config_name: en_zh-CN data_files: - split: train path: en_zh-CN/train-* - split: validation path: en_zh-CN/validation-* - split: test path: en_zh-CN/test-* - config_name: es_en data_files: - split: validation path: es_en/validation-* - split: test path: es_en/test-* - split: train path: es_en/train-* - config_name: et_en data_files: - split: train path: et_en/train-* - split: validation path: et_en/validation-* - split: test path: et_en/test-* - config_name: fa_en data_files: - split: train path: fa_en/train-* - split: validation path: fa_en/validation-* - split: test path: fa_en/test-* - config_name: fr_en data_files: - split: validation path: fr_en/validation-* - split: test path: fr_en/test-* - split: train path: fr_en/train-* - config_name: id_en data_files: - split: train path: id_en/train-* - split: validation path: id_en/validation-* - split: test path: id_en/test-* - config_name: it_en data_files: - split: train path: it_en/train-* - split: validation path: it_en/validation-* - split: test path: it_en/test-* - config_name: ja_en data_files: - split: train path: ja_en/train-* - split: validation path: ja_en/validation-* - split: test path: ja_en/test-* - config_name: lv_en data_files: - split: train path: lv_en/train-* - split: validation path: lv_en/validation-* - split: test path: lv_en/test-* - config_name: mn_en data_files: - split: train path: mn_en/train-* - split: validation path: mn_en/validation-* - split: test path: mn_en/test-* - config_name: nl_en data_files: - split: train path: nl_en/train-* - split: validation path: nl_en/validation-* - split: test path: nl_en/test-* - config_name: pt_en data_files: - split: train path: pt_en/train-* - split: validation path: pt_en/validation-* - split: test path: pt_en/test-* - config_name: ru_en data_files: - split: train path: ru_en/train-* - split: validation path: ru_en/validation-* - split: test path: ru_en/test-* - config_name: sl_en data_files: - split: train path: sl_en/train-* - split: validation path: sl_en/validation-* - split: test path: sl_en/test-* - config_name: sv-SE_en data_files: - split: train path: sv-SE_en/train-* - split: validation path: sv-SE_en/validation-* - split: test path: sv-SE_en/test-* - config_name: ta_en data_files: - split: train path: ta_en/train-* - split: validation path: ta_en/validation-* - split: test path: ta_en/test-* - config_name: tr_en data_files: - split: train path: tr_en/train-* - split: validation path: tr_en/validation-* - split: test path: tr_en/test-* - config_name: zh-CN_en data_files: - split: validation path: zh-CN_en/validation-* - split: test path: zh-CN_en/test-* - split: train path: zh-CN_en/train-* --- This is a partial copy of [CoVoST2](https://huggingface.co/datasets/facebook/covost2) dataset. The main difference is that the audio data is included in the dataset, which makes usage easier and allows browsing the samples using HF Dataset Viewer. The limitation of this method is that all audio samples of the `EN_XX` subsets are duplicated, as such the size of the dataset is larger. As such, not all the data is included: Only the `validation` and `test` subsets are available. From the `XX_EN` subsets, only `fr`, `es`, and `zh-CN` are included.
google/fleurs	google	2024-08-25T05:03:32Z	27,911	287	[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "annotations_creators:crowdsourced", "annotations_creators:machine-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "language:afr", "language:amh", "language:ara", "language:asm", "language:ast", "language:azj", "language:bel", "language:ben", "language:bos", "language:cat", "language:ceb", "language:cmn", "language:ces", "language:cym", "language:dan", "language:deu", "language:ell", "language:eng", "language:spa", "language:est", "language:fas", "language:ful", "language:fin", "language:tgl", "language:fra", "language:gle", "language:glg", "language:guj", "language:hau", "language:heb", "language:hin", "language:hrv", "language:hun", "language:hye", "language:ind", "language:ibo", "language:isl", "language:ita", "language:jpn", "language:jav", "language:kat", "language:kam", "language:kea", "language:kaz", "language:khm", "language:kan", "language:kor", "language:ckb", "language:kir", "language:ltz", "language:lug", "language:lin", "language:lao", "language:lit", "language:luo", "language:lav", "language:mri", "language:mkd", "language:mal", "language:mon", "language:mar", "language:msa", "language:mlt", "language:mya", "language:nob", "language:npi", "language:nld", "language:nso", "language:nya", "language:oci", "language:orm", "language:ory", "language:pan", "language:pol", "language:pus", "language:por", "language:ron", "language:rus", "language:bul", "language:snd", "language:slk", "language:slv", "language:sna", "language:som", "language:srp", "language:swe", "language:swh", "language:tam", "language:tel", "language:tgk", "language:tha", "language:tur", "language:ukr", "language:umb", "language:urd", "language:uzb", "language:vie", "language:wol", "language:xho", "language:yor", "language:yue", "language:zul", "license:cc-by-4.0", "size_categories:10K<n<100K", "arxiv:2205.12446", "arxiv:2106.03193", "region:us", "speech-recognition" ]	[ "automatic-speech-recognition" ]	2022-04-19T10:25:58Z	null	--- annotations_creators: - expert-generated - crowdsourced - machine-generated language_creators: - crowdsourced - expert-generated language: - afr - amh - ara - asm - ast - azj - bel - ben - bos - cat - ceb - cmn - ces - cym - dan - deu - ell - eng - spa - est - fas - ful - fin - tgl - fra - gle - glg - guj - hau - heb - hin - hrv - hun - hye - ind - ibo - isl - ita - jpn - jav - kat - kam - kea - kaz - khm - kan - kor - ckb - kir - ltz - lug - lin - lao - lit - luo - lav - mri - mkd - mal - mon - mar - msa - mlt - mya - nob - npi - nld - nso - nya - oci - orm - ory - pan - pol - pus - por - ron - rus - bul - snd - slk - slv - sna - som - srp - swe - swh - tam - tel - tgk - tha - tur - ukr - umb - urd - uzb - vie - wol - xho - yor - yue - zul license: - cc-by-4.0 multilinguality: - multilingual size_categories: - 10K<n<100K task_categories: - automatic-speech-recognition task_ids: [] pretty_name: 'The Cross-lingual TRansfer Evaluation of Multilingual Encoders for Speech (XTREME-S) benchmark is a benchmark designed to evaluate speech representations across languages, tasks, domains and data regimes. It covers 102 languages from 10+ language families, 3 different domains and 4 task families: speech recognition, translation, classification and retrieval.' tags: - speech-recognition --- # FLEURS ## Dataset Description - Fine-Tuning script: [pytorch/speech-recognition](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition) - Paper: [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://arxiv.org/abs/2205.12446) - Total amount of disk used: ca. 350 GB Fleurs is the speech version of the [FLoRes machine translation benchmark](https://arxiv.org/abs/2106.03193). We use 2009 n-way parallel sentences from the FLoRes dev and devtest publicly available sets, in 102 languages. Training sets have around 10 hours of supervision. Speakers of the train sets are different than speakers from the dev/test sets. Multilingual fine-tuning is used and ”unit error rate” (characters, signs) of all languages is averaged. Languages and results are also grouped into seven geographical areas: - Western Europe: Asturian, Bosnian, Catalan, Croatian, Danish, Dutch, English, Finnish, French, Galician, German, Greek, Hungarian, Icelandic, Irish, Italian, Kabuverdianu, Luxembourgish, Maltese, Norwegian, Occitan, Portuguese, Spanish, Swedish, Welsh - Eastern Europe: Armenian, Belarusian, Bulgarian, Czech, Estonian, Georgian, Latvian, Lithuanian, Macedonian, Polish, Romanian, Russian, Serbian, Slovak, Slovenian, Ukrainian - Central-Asia/Middle-East/North-Africa: Arabic, Azerbaijani, Hebrew, Kazakh, Kyrgyz, Mongolian, Pashto, Persian, Sorani-Kurdish, Tajik, Turkish, Uzbek - Sub-Saharan Africa: Afrikaans, Amharic, Fula, Ganda, Hausa, Igbo, Kamba, Lingala, Luo, Northern-Sotho, Nyanja, Oromo, Shona, Somali, Swahili, Umbundu, Wolof, Xhosa, Yoruba, Zulu - South-Asia: Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali, Oriya, Punjabi, Sindhi, Tamil, Telugu, Urdu - South-East Asia: Burmese, Cebuano, Filipino, Indonesian, Javanese, Khmer, Lao, Malay, Maori, Thai, Vietnamese - CJK languages: Cantonese and Mandarin Chinese, Japanese, Korean ## How to use & Supported Tasks ### How to use The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. For example, to download the Hindi config, simply specify the corresponding language config name (i.e., "hi_in" for Hindi): ```python from datasets import load_dataset fleurs = load_dataset("google/fleurs", "hi_in", split="train") ``` Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk. ```python from datasets import load_dataset fleurs = load_dataset("google/fleurs", "hi_in", split="train", streaming=True) print(next(iter(fleurs))) ``` Bonus: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed). Local: ```python from datasets import load_dataset from torch.utils.data.sampler import BatchSampler, RandomSampler fleurs = load_dataset("google/fleurs", "hi_in", split="train") batch_sampler = BatchSampler(RandomSampler(fleurs), batch_size=32, drop_last=False) dataloader = DataLoader(fleurs, batch_sampler=batch_sampler) ``` Streaming: ```python from datasets import load_dataset from torch.utils.data import DataLoader fleurs = load_dataset("google/fleurs", "hi_in", split="train") dataloader = DataLoader(fleurs, batch_size=32) ``` To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets). ### Example scripts Train your own CTC or Seq2Seq Automatic Speech Recognition models on FLEURS with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition). Fine-tune your own Language Identification models on FLEURS with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/audio-classification) ### 1. Speech Recognition (ASR) ```py from datasets import load_dataset fleurs_asr = load_dataset("google/fleurs", "af_za") # for Afrikaans # to download all data for multi-lingual fine-tuning uncomment following line # fleurs_asr = load_dataset("google/fleurs", "all") # see structure print(fleurs_asr) # load audio sample on the fly audio_input = fleurs_asr["train"][0]["audio"] # first decoded audio sample transcription = fleurs_asr["train"][0]["transcription"] # first transcription # use `audio_input` and `transcription` to fine-tune your model for ASR # for analyses see language groups all_language_groups = fleurs_asr["train"].features["lang_group_id"].names lang_group_id = fleurs_asr["train"][0]["lang_group_id"] all_language_groups[lang_group_id] ``` ### 2. Language Identification LangID can often be a domain classification, but in the case of FLEURS-LangID, recordings are done in a similar setting across languages and the utterances correspond to n-way parallel sentences, in the exact same domain, making this task particularly relevant for evaluating LangID. The setting is simple, FLEURS-LangID is splitted in train/valid/test for each language. We simply create a single train/valid/test for LangID by merging all. ```py from datasets import load_dataset fleurs_langID = load_dataset("google/fleurs", "all") # to download all data # see structure print(fleurs_langID) # load audio sample on the fly audio_input = fleurs_langID["train"][0]["audio"] # first decoded audio sample language_class = fleurs_langID["train"][0]["lang_id"] # first id class language = fleurs_langID["train"].features["lang_id"].names[language_class] # use audio_input and language_class to fine-tune your model for audio classification ``` ### 3. Retrieval Retrieval provides n-way parallel speech and text data. Similar to how XTREME for text leverages Tatoeba to evaluate bitext mining a.k.a sentence translation retrieval, we use Retrieval to evaluate the quality of fixed-size representations of speech utterances. Our goal is to incentivize the creation of fixed-size speech encoder for speech retrieval. The system has to retrieve the English "key" utterance corresponding to the speech translation of "queries" in 15 languages. Results have to be reported on the test sets of Retrieval whose utterances are used as queries (and keys for English). We augment the English keys with a large number of utterances to make the task more difficult. ```py from datasets import load_dataset fleurs_retrieval = load_dataset("google/fleurs", "af_za") # for Afrikaans # to download all data for multi-lingual fine-tuning uncomment following line # fleurs_retrieval = load_dataset("google/fleurs", "all") # see structure print(fleurs_retrieval) # load audio sample on the fly audio_input = fleurs_retrieval["train"][0]["audio"] # decoded audio sample text_sample_pos = fleurs_retrieval["train"][0]["transcription"] # positive text sample text_sample_neg = fleurs_retrieval["train"][1:20]["transcription"] # negative text samples # use `audio_input`, `text_sample_pos`, and `text_sample_neg` to fine-tune your model for retrieval ``` Users can leverage the training (and dev) sets of FLEURS-Retrieval with a ranking loss to build better cross-lingual fixed-size representations of speech. ## Dataset Structure We show detailed information the example configurations `af_za` of the dataset. All other configurations have the same structure. ### Data Instances af_za - Size of downloaded dataset files: 1.47 GB - Size of the generated dataset: 1 MB - Total amount of disk used: 1.47 GB An example of a data instance of the config `af_za` looks as follows: ``` {'id': 91, 'num_samples': 385920, 'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/310a663d52322700b3d3473cbc5af429bd92a23f9bc683594e70bc31232db39e/home/vaxelrod/FLEURS/oss2_obfuscated/af_za/audio/train/17797742076841560615.wav', 'audio': {'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/310a663d52322700b3d3473cbc5af429bd92a23f9bc683594e70bc31232db39e/home/vaxelrod/FLEURS/oss2_obfuscated/af_za/audio/train/17797742076841560615.wav', 'array': array([ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., -1.1205673e-04, -8.4638596e-05, -1.2731552e-04], dtype=float32), 'sampling_rate': 16000}, 'raw_transcription': 'Dit is nog nie huidiglik bekend watter aantygings gemaak sal word of wat owerhede na die seun gelei het nie maar jeugmisdaad-verrigtinge het in die federale hof begin', 'transcription': 'dit is nog nie huidiglik bekend watter aantygings gemaak sal word of wat owerhede na die seun gelei het nie maar jeugmisdaad-verrigtinge het in die federale hof begin', 'gender': 0, 'lang_id': 0, 'language': 'Afrikaans', 'lang_group_id': 3} ``` ### Data Fields The data fields are the same among all splits. - id (int): ID of audio sample - num_samples (int): Number of float values - path (str): Path to the audio file - audio (dict): Audio object including loaded audio array, sampling rate and path ot audio - raw_transcription (str): The non-normalized transcription of the audio file - transcription (str): Transcription of the audio file - gender (int): Class id of gender - lang_id (int): Class id of language - lang_group_id (int): Class id of language group ### Data Splits Every config only has the `"train"` split containing of ca. 1000 examples, and a `"validation"` and `"test"` split each containing of ca. 400 examples. ## Dataset Creation We collect between one and three recordings for each sentence (2.3 on average), and buildnew train-dev-test splits with 1509, 150 and 350 sentences for train, dev and test respectively. ## Considerations for Using the Data ### Social Impact of Dataset This dataset is meant to encourage the development of speech technology in a lot more languages of the world. One of the goal is to give equal access to technologies like speech recognition or speech translation to everyone, meaning better dubbing or better access to content from the internet (like podcasts, streaming or videos). ### Discussion of Biases Most datasets have a fair distribution of gender utterances (e.g. the newly introduced FLEURS dataset). While many languages are covered from various regions of the world, the benchmark misses many languages that are all equally important. We believe technology built through FLEURS should generalize to all languages. ### Other Known Limitations The dataset has a particular focus on read-speech because common evaluation benchmarks like CoVoST-2 or LibriSpeech evaluate on this type of speech. There is sometimes a known mismatch between performance obtained in a read-speech setting and a more noisy setting (in production for instance). Given the big progress that remains to be made on many languages, we believe better performance on FLEURS should still correlate well with actual progress made for speech understanding. ## Additional Information All datasets are licensed under the [Creative Commons license (CC-BY)](https://creativecommons.org/licenses/). ### Citation Information You can access the FLEURS paper at https://arxiv.org/abs/2205.12446. Please cite the paper when referencing the FLEURS corpus as: ``` @article{fleurs2022arxiv, title = {FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech}, author = {Conneau, Alexis and Ma, Min and Khanuja, Simran and Zhang, Yu and Axelrod, Vera and Dalmia, Siddharth and Riesa, Jason and Rivera, Clara and Bapna, Ankur}, journal={arXiv preprint arXiv:2205.12446}, url = {https://arxiv.org/abs/2205.12446}, year = {2022}, ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) and [@aconneau](https://github.com/aconneau) for adding this dataset.
gksriharsha/chitralekha	gksriharsha	2024-08-23T23:00:03Z	160,664	4	[ "task_categories:image-to-text", "language:te", "license:mit", "size_categories:10M<n<100M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "doi:10.57967/hf/3403", "region:us" ]	[ "image-to-text" ]	2023-11-29T14:31:24Z	null	--- dataset_info: - config_name: Dhurjati features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1298445060.3780885 num_examples: 475834 - name: validation num_bytes: 432816839.3109558 num_examples: 158612 - name: test num_bytes: 432816839.3109558 num_examples: 158612 download_size: 2214924048 dataset_size: 2164078739 - config_name: Gidugu features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1282865192.8855712 num_examples: 476265 - name: validation num_bytes: 427624424.55721444 num_examples: 158756 - name: test num_bytes: 427624424.55721444 num_examples: 158756 download_size: 2189311335 dataset_size: 2138114042.0000002 - 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config_name: TLOTSwamiB features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 2425012348.9576674 num_examples: 477330 - name: validation num_bytes: 808342530.0211664 num_examples: 159111 - name: test num_bytes: 808342530.0211664 num_examples: 159111 download_size: 4038041582 dataset_size: 4041697409 - config_name: TLOTSwamiBI_Ship features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 2850358898.466789 num_examples: 478777 - name: validation num_bytes: 950123601.7666057 num_examples: 159593 - name: test num_bytes: 950123601.7666057 num_examples: 159593 download_size: 4756940495 dataset_size: 4750606102 - config_name: TLOTSwamiB_Ship features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 2597770710.722685 num_examples: 475800 - name: validation num_bytes: 865923570.240895 num_examples: 158600 - name: test num_bytes: 865929030.0364199 num_examples: 158601 download_size: 4330358867 dataset_size: 4329623311 - config_name: TLOTVennela1B_Ship features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1858266228.4038165 num_examples: 476703 - name: validation num_bytes: 619425974.2980918 num_examples: 158902 - name: test num_bytes: 619425974.2980918 num_examples: 158902 download_size: 9264631387 dataset_size: 3097118177 - config_name: TLOTVennelaBI_Ship features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 2075214563.274462 num_examples: 475737 - name: validation num_bytes: 691742549.862769 num_examples: 158580 - name: test num_bytes: 691742549.862769 num_examples: 158580 download_size: 3449852145 dataset_size: 3458699663 - config_name: TLOTVennelaB_Ship features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1853628708.5342138 num_examples: 475764 - name: validation num_bytes: 617876236.1780713 num_examples: 158588 - name: test num_bytes: 617880132.287715 num_examples: 158589 download_size: 3076196686 dataset_size: 3089385077 - config_name: TLOTVennelaI_Ship features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 2220159958.2 num_examples: 477489 - name: validation num_bytes: 740053319.4 num_examples: 159163 - name: test num_bytes: 740053319.4 num_examples: 159163 download_size: 3692812769 dataset_size: 3700266597 - config_name: TenaliRamakrishna-Regular features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1412098107.6 num_examples: 479922 - name: validation num_bytes: 470699369.2 num_examples: 159974 - name: test num_bytes: 470699369.2 num_examples: 159974 download_size: 2373061510 dataset_size: 2353496846 - config_name: Tikkana features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 237760800.6 num_examples: 476520 - name: validation num_bytes: 79253600.2 num_examples: 158840 - name: test num_bytes: 79253600.2 num_examples: 158840 download_size: 266272383 dataset_size: 396268001 - config_name: TimmanaRegular features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1476790008.6 num_examples: 478059 - name: validation num_bytes: 492263336.2 num_examples: 159353 - name: test num_bytes: 492263336.2 num_examples: 159353 download_size: 2461309068 dataset_size: 2461316681 - config_name: Vajram features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1522698226.9404452 num_examples: 480837 - name: validation num_bytes: 507566075.64681506 num_examples: 160279 - name: test num_bytes: 507569242.41273975 num_examples: 160280 download_size: 2548130724 dataset_size: 2537833545 - config_name: Vani features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1457020940.7032518 num_examples: 476385 - name: validation num_bytes: 485673646.9010839 num_examples: 158795 - name: test num_bytes: 485676705.39566433 num_examples: 158796 download_size: 2434817917 dataset_size: 2428371293 - config_name: Vanib features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1522290417.6 num_examples: 474951 - name: validation num_bytes: 507430139.2 num_examples: 158317 - name: test num_bytes: 507430139.2 num_examples: 158317 download_size: 2529233521 dataset_size: 2537150696 - config_name: Vemana features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1699154826.4604304 num_examples: 476205 - name: validation num_bytes: 566388510.2697848 num_examples: 158736 - name: test num_bytes: 566388510.2697848 num_examples: 158736 download_size: 2814457802 dataset_size: 2831931847 - config_name: akshar features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1339177104.1214905 num_examples: 476169 - name: validation num_bytes: 446395180.4392547 num_examples: 158724 - name: test num_bytes: 446395180.4392547 num_examples: 158724 download_size: 2284376294 dataset_size: 2231967465 - config_name: gautami features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1459193859.1610594 num_examples: 476425 - name: validation num_bytes: 486399994.91947037 num_examples: 158809 - name: test num_bytes: 486399994.91947037 num_examples: 158809 download_size: 2447315957 dataset_size: 2431993849 - config_name: gautamib features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1464740409.2608879 num_examples: 477459 - name: validation num_bytes: 488249870.869556 num_examples: 159154 - name: test num_bytes: 488249870.869556 num_examples: 159154 download_size: 2454242590 dataset_size: 2441240151 - config_name: lohit_te features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1566900366.462158 num_examples: 477809 - name: validation num_bytes: 522301215.268921 num_examples: 159270 - name: test num_bytes: 522301215.268921 num_examples: 159270 download_size: 2611413315 dataset_size: 2611502797 configs: - config_name: Dhurjati data_files: - split: train path: Dhurjati/train-* - split: validation path: Dhurjati/validation-* - split: test path: Dhurjati/test-* - config_name: Gidugu data_files: - split: train path: Gidugu/train-* - split: validation path: Gidugu/validation-* - split: test path: Gidugu/test-* - config_name: Gurajada data_files: - split: train path: Gurajada/train-* - split: validation path: Gurajada/validation-* - split: test path: Gurajada/test-* - config_name: Mallanna data_files: - split: train path: Mallanna/train-* - split: validation path: Mallanna/validation-* - split: test path: Mallanna/test-* - config_name: Mandali-Regular data_files: - split: train path: Mandali-Regular/train-* - split: validation path: Mandali-Regular/validation-* - split: test path: Mandali-Regular/test-* - config_name: NATS data_files: - split: train path: NATS/train-* - split: validation path: NATS/validation-* - split: test path: NATS/test-* - config_name: NTR data_files: - split: train path: NTR/train-* - split: validation path: NTR/validation-* - split: test path: NTR/test-* - config_name: NotoSansTelugu-Bold data_files: - split: train path: NotoSansTelugu-Bold/train-* - split: validation path: NotoSansTelugu-Bold/validation-* - split: test path: NotoSansTelugu-Bold/test-* - config_name: NotoSansTelugu-Regular data_files: - split: train path: NotoSansTelugu-Regular/train-* - split: validation path: NotoSansTelugu-Regular/validation-* - split: test path: NotoSansTelugu-Regular/test-* - config_name: NotoSansTeluguUI-Bold data_files: - split: train path: NotoSansTeluguUI-Bold/train-* - split: validation path: NotoSansTeluguUI-Bold/validation-* - split: test path: NotoSansTeluguUI-Bold/test-* - config_name: NotoSansTeluguUI-Regular data_files: - split: train path: NotoSansTeluguUI-Regular/train-* - split: validation path: NotoSansTeluguUI-Regular/validation-* - split: test path: NotoSansTeluguUI-Regular/test-* - config_name: NotoSerifTelugu-VariableFont_wght data_files: - split: train path: NotoSerifTelugu-VariableFont_wght/train-* - split: validation path: NotoSerifTelugu-VariableFont_wght/validation-* - split: test path: NotoSerifTelugu-VariableFont_wght/test-* - config_name: Pothana2000 data_files: - split: train path: Pothana2000/train-* - split: validation path: Pothana2000/validation-* - split: test path: Pothana2000/test-* - config_name: Ramabhadra data_files: - split: train path: Ramabhadra/train-* - split: validation path: Ramabhadra/validation-* - split: test path: Ramabhadra/test-* - config_name: Ramabhadra1 data_files: - split: train path: Ramabhadra1/train-* - split: validation path: Ramabhadra1/validation-* - split: test path: Ramabhadra1/test-* - config_name: RamaneeyaWin data_files: - split: train path: RamaneeyaWin/train-* - split: validation path: RamaneeyaWin/validation-* - split: test path: RamaneeyaWin/test-* - config_name: Ramaraja-Regular data_files: - split: train path: Ramaraja-Regular/train-* - split: validation path: Ramaraja-Regular/validation-* - split: test path: Ramaraja-Regular/test-* - config_name: Suguna data_files: - split: train path: Suguna/train-* - split: validation path: Suguna/validation-* - split: test path: Suguna/test-* - config_name: Suranna data_files: - split: train path: Suranna/train-* - split: validation path: Suranna/validation-* - split: test path: Suranna/test-* - config_name: Suravara_Samhita data_files: - split: train path: Suravara_Samhita/train-* - split: validation path: Suravara_Samhita/validation-* - split: test path: Suravara_Samhita/test-* - config_name: Suravara_Swarna data_files: - split: train path: Suravara_Swarna/train-* - split: validation path: Suravara_Swarna/validation-* - split: test path: Suravara_Swarna/test-* - config_name: Suravara_Swarna_bold data_files: - split: train path: Suravara_Swarna_bold/train-* - split: validation path: Suravara_Swarna_bold/validation-* - split: test path: Suravara_Swarna_bold/test-* - config_name: Suravara_Swarna_italic data_files: - split: train path: Suravara_Swarna_italic/train-* - split: validation path: Suravara_Swarna_italic/validation-* - split: test path: Suravara_Swarna_italic/test-* - config_name: Suravaram data_files: - split: train path: Suravaram/train-* - split: validation path: Suravaram/validation-* - split: test path: Suravaram/test-* - config_name: TLOTAmmaBI_ship data_files: - split: train path: TLOTAmmaBI_ship/train-* - split: validation path: TLOTAmmaBI_ship/validation-* - split: test path: TLOTAmmaBI_ship/test-* - config_name: TLOTAmmaB_ship data_files: - split: train path: TLOTAmmaB_ship/train-* - split: validation path: TLOTAmmaB_ship/validation-* - split: test path: TLOTAmmaB_ship/test-* - config_name: TLOTAmmaI_ship data_files: - split: train path: TLOTAmmaI_ship/train-* - split: validation path: TLOTAmmaI_ship/validation-* - split: test path: TLOTAmmaI_ship/test-* - config_name: TLOTAmmaN_ship data_files: - split: train path: TLOTAmmaN_ship/train-* - split: validation path: TLOTAmmaN_ship/validation-* - split: test path: TLOTAmmaN_ship/test-* - config_name: TLOTAmrutaBI_Ship data_files: - split: train path: TLOTAmrutaBI_Ship/train-* - split: validation path: TLOTAmrutaBI_Ship/validation-* - split: test path: TLOTAmrutaBI_Ship/test-* - config_name: TLOTAmrutaB_Ship data_files: - split: train path: TLOTAmrutaB_Ship/train-* - split: validation path: TLOTAmrutaB_Ship/validation-* - split: test path: TLOTAmrutaB_Ship/test-* - config_name: TLOTAtreyaBI_Ship data_files: - split: train path: TLOTAtreyaBI_Ship/train-* - split: validation path: TLOTAtreyaBI_Ship/validation-* - split: test path: TLOTAtreyaBI_Ship/test-* - config_name: TLOTAtreyaB_Ship data_files: - split: train path: TLOTAtreyaB_Ship/train-* - split: validation path: TLOTAtreyaB_Ship/validation-* - split: test path: TLOTAtreyaB_Ship/test-* - config_name: TLOTAtreyaI_Ship data_files: - split: train path: TLOTAtreyaI_Ship/train-* - split: validation path: TLOTAtreyaI_Ship/validation-* - split: test path: TLOTAtreyaI_Ship/test-* - config_name: TLOTAtreyaN_Ship data_files: - split: train path: TLOTAtreyaN_Ship/train-* - split: validation path: TLOTAtreyaN_Ship/validation-* - split: test path: TLOTAtreyaN_Ship/test-* - config_name: TLOTChandanaBI_Ship data_files: - split: train path: TLOTChandanaBI_Ship/train-* - split: validation path: TLOTChandanaBI_Ship/validation-* - split: test path: TLOTChandanaBI_Ship/test-* - config_name: TLOTChandanaB_Ship data_files: - split: train path: TLOTChandanaB_Ship/train-* - split: validation path: TLOTChandanaB_Ship/validation-* - split: test path: TLOTChandanaB_Ship/test-* - config_name: TLOTDevaI_Ship data_files: - split: train path: TLOTDevaI_Ship/train-* - split: validation path: TLOTDevaI_Ship/validation-* - split: test path: TLOTDevaI_Ship/test-* - config_name: TLOTDevaN_Ship data_files: - split: train path: TLOTDevaN_Ship/train-* - split: validation path: TLOTDevaN_Ship/validation-* - split: test path: TLOTDevaN_Ship/test-* - config_name: TLOTDraupadiBI_Ship data_files: - split: train path: TLOTDraupadiBI_Ship/train-* - split: validation path: TLOTDraupadiBI_Ship/validation-* - split: test path: TLOTDraupadiBI_Ship/test-* - config_name: TLOTDraupadiB_ship data_files: - split: train path: TLOTDraupadiB_ship/train-* - split: validation path: TLOTDraupadiB_ship/validation-* - split: test path: TLOTDraupadiB_ship/test-* - config_name: TLOTDraupadiI_Ship data_files: - split: train path: TLOTDraupadiI_Ship/train-* - split: validation path: TLOTDraupadiI_Ship/validation-* - split: test path: TLOTDraupadiI_Ship/test-* - config_name: TLOTDraupadiN_Ship data_files: - split: train path: TLOTDraupadiN_Ship/train-* - split: validation path: TLOTDraupadiN_Ship/validation-* - split: test path: TLOTDraupadiN_Ship/test-* - config_name: TLOTGolkondaBI_Ship data_files: - split: train path: TLOTGolkondaBI_Ship/train-* - split: validation path: TLOTGolkondaBI_Ship/validation-* - split: test path: TLOTGolkondaBI_Ship/test-* - config_name: TLOTGolkondaB_Ship data_files: - split: train path: TLOTGolkondaB_Ship/train-* - split: validation path: TLOTGolkondaB_Ship/validation-* - split: test path: TLOTGolkondaB_Ship/test-* - config_name: TLOTKrishnaB_Ship data_files: - split: train path: TLOTKrishnaB_Ship/train-* - split: validation path: TLOTKrishnaB_Ship/validation-* - split: test path: TLOTKrishnaB_Ship/test-* - config_name: TLOTKrishnaI_Ship data_files: - split: train path: TLOTKrishnaI_Ship/train-* - split: validation path: TLOTKrishnaI_Ship/validation-* - split: test path: TLOTKrishnaI_Ship/test-* - config_name: TLOTKrishnaN_Ship data_files: - split: train path: TLOTKrishnaN_Ship/train-* - split: validation path: TLOTKrishnaN_Ship/validation-* - split: test path: TLOTKrishnaN_Ship/test-* - config_name: TLOTManuBI_Ship data_files: - split: train path: TLOTManuBI_Ship/train-* - split: validation path: TLOTManuBI_Ship/validation-* - split: test path: TLOTManuBI_Ship/test-* - config_name: TLOTManuB_Ship data_files: - split: train path: TLOTManuB_Ship/train-* - split: validation path: TLOTManuB_Ship/validation-* - split: test path: TLOTManuB_Ship/test-* - config_name: TLOTManuI_Ship data_files: - split: train path: TLOTManuI_Ship/train-* - split: validation path: TLOTManuI_Ship/validation-* - split: test path: TLOTManuI_Ship/test-* - config_name: TLOTManuN_Ship data_files: - split: train path: TLOTManuN_Ship/train-* - split: validation path: TLOTManuN_Ship/validation-* - split: test path: TLOTManuN_Ship/test-* - config_name: TLOTMenakaBI_Ship data_files: - split: train path: TLOTMenakaBI_Ship/train-* - split: validation path: TLOTMenakaBI_Ship/validation-* - split: test path: TLOTMenakaBI_Ship/test-* - config_name: TLOTMenakaB_Ship data_files: - split: train path: TLOTMenakaB_Ship/train-* - split: validation path: TLOTMenakaB_Ship/validation-* - split: test path: TLOTMenakaB_Ship/test-* - config_name: TLOTMenakaI_Ship data_files: - split: train path: TLOTMenakaI_Ship/train-* - split: validation path: TLOTMenakaI_Ship/validation-* - split: test path: TLOTMenakaI_Ship/test-* - config_name: TLOTMenakaN_Ship data_files: - split: train path: TLOTMenakaN_Ship/train-* - split: validation path: TLOTMenakaN_Ship/validation-* - split: test path: TLOTMenakaN_Ship/test-* - config_name: TLOTPavaniBI_Ship data_files: - split: train path: TLOTPavaniBI_Ship/train-* - split: validation path: TLOTPavaniBI_Ship/validation-* - split: test path: TLOTPavaniBI_Ship/test-* - config_name: TLOTPavaniB_Ship data_files: - split: train path: TLOTPavaniB_Ship/train-* - split: validation path: TLOTPavaniB_Ship/validation-* - split: test path: TLOTPavaniB_Ship/test-* - config_name: TLOTPriyaB_Ship data_files: - split: train path: TLOTPriyaB_Ship/train-* - split: validation path: TLOTPriyaB_Ship/validation-* - split: test path: TLOTPriyaB_Ship/test-* - config_name: TLOTRajanBI_Ship data_files: - split: train path: TLOTRajanBI_Ship/train-* - split: validation path: TLOTRajanBI_Ship/validation-* - split: test path: TLOTRajanBI_Ship/test-* - config_name: TLOTRajanB_Ship data_files: - split: train path: TLOTRajanB_Ship/train-* - split: validation path: TLOTRajanB_Ship/validation-* - split: test path: TLOTRajanB_Ship/test-* - config_name: TLOTRajaniBI_Ship data_files: - split: train path: TLOTRajaniBI_Ship/train-* - split: validation path: TLOTRajaniBI_Ship/validation-* - split: test path: TLOTRajaniBI_Ship/test-* - config_name: TLOTRajaniB_Ship data_files: - split: train path: TLOTRajaniB_Ship/train-* - split: validation path: TLOTRajaniB_Ship/validation-* - split: test path: TLOTRajaniB_Ship/test-* - config_name: TLOTSanjanaBI_Ship data_files: - split: train path: TLOTSanjanaBI_Ship/train-* - split: validation path: TLOTSanjanaBI_Ship/validation-* - split: test path: TLOTSanjanaBI_Ship/test-* - config_name: TLOTSanjanaB_Ship data_files: - split: train path: TLOTSanjanaB_Ship/train-* - split: validation path: TLOTSanjanaB_Ship/validation-* - split: test path: TLOTSanjanaB_Ship/test-* - config_name: TLOTSitaraBI_Ship data_files: - split: train path: TLOTSitaraBI_Ship/train-* - split: validation path: TLOTSitaraBI_Ship/validation-* - split: test path: TLOTSitaraBI_Ship/test-* - config_name: TLOTSitaraB_Ship data_files: - split: train path: TLOTSitaraB_Ship/train-* - split: validation path: TLOTSitaraB_Ship/validation-* - split: test path: TLOTSitaraB_Ship/test-* - config_name: TLOTSwamiBI_Ship data_files: - split: train path: TLOTSwamiBI_Ship/train-* - split: validation path: TLOTSwamiBI_Ship/validation-* - split: test path: TLOTSwamiBI_Ship/test-* - config_name: TLOTSwamiB_Ship data_files: - split: train path: TLOTSwamiB_Ship/train-* - split: validation path: TLOTSwamiB_Ship/validation-* - split: test path: TLOTSwamiB_Ship/test-* - config_name: TLOTVennela1B_Ship data_files: - split: train path: TLOTVennela1B_Ship/train-* - split: validation path: TLOTVennela1B_Ship/validation-* - split: test path: TLOTVennela1B_Ship/test-* - config_name: TLOTVennelaBI_Ship data_files: - split: train path: TLOTVennelaBI_Ship/train-* - split: validation path: TLOTVennelaBI_Ship/validation-* - split: test path: TLOTVennelaBI_Ship/test-* - config_name: TLOTVennelaI_Ship data_files: - split: train path: TLOTVennelaI_Ship/train-* - split: validation path: TLOTVennelaI_Ship/validation-* - split: test path: TLOTVennelaI_Ship/test-* - config_name: TenaliRamakrishna-Regular data_files: - split: train path: TenaliRamakrishna-Regular/train-* - split: validation path: TenaliRamakrishna-Regular/validation-* - split: test path: TenaliRamakrishna-Regular/test-* - config_name: TimmanaRegular data_files: - split: train path: TimmanaRegular/train-* - split: validation path: TimmanaRegular/validation-* - split: test path: TimmanaRegular/test-* - config_name: Vanib data_files: - split: train path: Vanib/train-* - split: validation path: Vanib/validation-* - split: test path: Vanib/test-* - config_name: Vemana data_files: - split: train path: Vemana/train-* - split: validation path: Vemana/validation-* - split: test path: Vemana/test-* - config_name: akshar data_files: - split: train path: akshar/train-* - split: validation path: akshar/validation-* - split: test path: akshar/test-* - config_name: gautami data_files: - split: train path: gautami/train-* - split: validation path: gautami/validation-* - split: test path: gautami/test-* - config_name: gautamib data_files: - split: train path: gautamib/train-* - split: validation path: gautamib/validation-* - split: test path: gautamib/test-* license: mit task_categories: - image-to-text language: - te size_categories: - 1M<n<10M --- # Chitralekha ## Dataset Details ### Dataset Version Some of the fonts do not have proper letters/rendering of different telugu letter combinations. Those have been removed as much as I can find them. If there are any other mistakes that you notice, please raise an issue and I will try my best to look into it ### Dataset Description This extensive dataset, hosted on Huggingface, is a comprehensive resource for Optical Character Recognition (OCR) in the Telugu language, featuring an impressive array of 80+ configurations. Each configuration in this dataset corresponds to a unique font, meticulously curated by Dr. Rakesh Achanta and sourced from his GitHub repository (https://github.com/TeluguOCR/banti_telugu_ocr). The dataset is specifically designed to support and enhance the development of OCR models, ranging from simple Convolutional Recurrent Neural Network (CRNN) architectures to more advanced systems like trOCR. The versatility of this dataset lies in its large volume and diversity, making it an ideal choice for researchers and developers aiming to build robust OCR systems for the Telugu script. Key Features: - Font Diversity: Over 80 unique fonts, each forming a separate configuration, providing a rich variety in text styles and nuances. - Large Volume: Each configuration contains approximately 800,000 examples, summing up to a vast pool of data for comprehensive training and evaluation. - Data Split: The dataset is pre-split into training, validation, and test sets, following a 60/20/20 ratio, to facilitate efficient model training and benchmarking. - Use Cases: Ideal for developing a wide range of OCR models - from basic CRNNs to sophisticated models like trOCR. - Accessibility: Hosted on Huggingface, ensuring easy access and integration with various machine learning frameworks and tools. This dataset stands as a testament to Dr. Rakesh Achanta's dedication to enhancing Telugu language processing technologies. It is not just a tool for model development but also a gateway to preserving and digitizing the rich literary heritage of the Telugu language. Researchers and developers leveraging this dataset are encouraged to adhere to the ethical guidelines of AI research and development, ensuring that the applications developed are for the benefit of language preservation, accessibility, and technological advancement in a responsible manner. - Fonts Curated by: Dr. Rakesh Achanta - Shared by: Krishna Sriharsha Gundu - Data Curated by: Anusha Motamarri - Language(s) (NLP): Telugu ### Ethical Considerations: Researchers and developers leveraging this dataset are encouraged to adhere to the ethical guidelines of AI research and development. Applications developed using this dataset should prioritize: - Language preservation and cultural heritage protection - Improving accessibility of Telugu text for diverse user groups - Responsible technological advancement in language processing ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [Original Books Dataset](https://github.com/AnushaMotamarri/Telugu-Books-Dataset)
legacy-datasets/common_voice	legacy-datasets	2024-08-22T08:27:23Z	4,611	137	[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:multilingual", "source_datasets:extended\|common_voice", "language:ab", "language:ar", "language:as", "language:br", "language:ca", "language:cnh", "language:cs", "language:cv", "language:cy", "language:de", "language:dv", "language:el", "language:en", "language:eo", "language:es", "language:et", "language:eu", "language:fa", "language:fi", "language:fr", "language:fy", "language:ga", "language:hi", "language:hsb", "language:hu", "language:ia", "language:id", "language:it", "language:ja", "language:ka", "language:kab", "language:ky", "language:lg", "language:lt", "language:lv", "language:mn", "language:mt", "language:nl", "language:or", "language:pa", "language:pl", "language:pt", "language:rm", "language:ro", "language:ru", "language:rw", "language:sah", "language:sl", "language:sv", "language:ta", "language:th", "language:tr", "language:tt", "language:uk", "language:vi", "language:vot", "language:zh", "license:cc0-1.0", "size_categories:100K<n<1M", "region:us" ]	[ "automatic-speech-recognition" ]	2022-03-02T23:29:22Z	null	--- pretty_name: Common Voice annotations_creators: - crowdsourced language_creators: - crowdsourced language: - ab - ar - as - br - ca - cnh - cs - cv - cy - de - dv - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - hi - hsb - hu - ia - id - it - ja - ka - kab - ky - lg - lt - lv - mn - mt - nl - or - pa - pl - pt - rm - ro - ru - rw - sah - sl - sv - ta - th - tr - tt - uk - vi - vot - zh language_bcp47: - fy-NL - ga-IE - pa-IN - rm-sursilv - rm-vallader - sv-SE - zh-CN - zh-HK - zh-TW license: - cc0-1.0 multilinguality: - multilingual size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K - n<1K source_datasets: - extended\|common_voice task_categories: - automatic-speech-recognition task_ids: [] paperswithcode_id: common-voice viewer: false dataset_info: - config_name: ab features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 1295622 num_examples: 22 - name: test num_bytes: 411844 num_examples: 9 - name: validation - name: other num_bytes: 40023390 num_examples: 752 - name: validated num_bytes: 1707426 num_examples: 31 - name: invalidated num_bytes: 361626 num_examples: 8 download_size: 41038412 dataset_size: 43799908 - config_name: ar features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 359335168 num_examples: 14227 - name: test num_bytes: 237546641 num_examples: 7622 - name: validation num_bytes: 209606861 num_examples: 7517 - name: other num_bytes: 515822404 num_examples: 18283 - name: validated num_bytes: 1182522872 num_examples: 43291 - name: invalidated num_bytes: 194805036 num_examples: 6333 download_size: 1756264615 dataset_size: 2699638982 - config_name: as features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 11442279 num_examples: 270 - name: test num_bytes: 5071343 num_examples: 110 - name: validation num_bytes: 5480156 num_examples: 124 - name: other - name: validated num_bytes: 21993698 num_examples: 504 - name: invalidated num_bytes: 886145 num_examples: 31 download_size: 22226465 dataset_size: 44873621 - config_name: br features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 62238289 num_examples: 2780 - name: test num_bytes: 54461339 num_examples: 2087 - name: validation num_bytes: 46995570 num_examples: 1997 - name: other num_bytes: 269858143 num_examples: 10912 - name: validated num_bytes: 203503622 num_examples: 8560 - name: invalidated num_bytes: 20861017 num_examples: 623 download_size: 465276982 dataset_size: 657917980 - config_name: ca features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - 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name: invalidated num_bytes: 13642724 num_examples: 433 download_size: 161331331 dataset_size: 233556145 - config_name: cs features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 215205282 num_examples: 5655 - name: test num_bytes: 148499476 num_examples: 4144 - name: validation num_bytes: 148312130 num_examples: 4118 - name: other num_bytes: 282225475 num_examples: 7475 - name: validated num_bytes: 1019817024 num_examples: 30431 - name: invalidated num_bytes: 24717823 num_examples: 685 download_size: 1271909933 dataset_size: 1838777210 - config_name: cv features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 31649510 num_examples: 931 - name: test num_bytes: 32513061 num_examples: 788 - name: validation num_bytes: 28429779 num_examples: 818 - name: other num_bytes: 288294623 num_examples: 6927 - name: validated num_bytes: 126717875 num_examples: 3496 - name: invalidated num_bytes: 57923138 num_examples: 1282 download_size: 439329081 dataset_size: 565527986 - config_name: cy features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - 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config_name: id features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 63515863 num_examples: 2130 - name: test num_bytes: 60711104 num_examples: 1844 - name: validation num_bytes: 56963520 num_examples: 1835 - name: other num_bytes: 206578628 num_examples: 6782 - name: validated num_bytes: 272570942 num_examples: 8696 - name: invalidated num_bytes: 16566129 num_examples: 470 download_size: 475918233 dataset_size: 676906186 - config_name: it features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 2555546829 num_examples: 58015 - name: test num_bytes: 656285877 num_examples: 12928 - name: validation num_bytes: 621955330 num_examples: 12928 - name: other num_bytes: 671213467 num_examples: 14549 - name: validated num_bytes: 4552252754 num_examples: 102579 - name: invalidated num_bytes: 564610354 num_examples: 12189 download_size: 5585781573 dataset_size: 9621864611 - config_name: ja features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 27600264 num_examples: 722 - name: test num_bytes: 26475556 num_examples: 632 - name: validation num_bytes: 22098940 num_examples: 586 - name: other num_bytes: 34588931 num_examples: 885 - name: validated num_bytes: 106916400 num_examples: 3072 - name: invalidated num_bytes: 17819020 num_examples: 504 download_size: 152879796 dataset_size: 235499111 - config_name: ka features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 47790695 num_examples: 1058 - name: test num_bytes: 30301524 num_examples: 656 - name: validation num_bytes: 24951079 num_examples: 527 - name: other num_bytes: 2144603 num_examples: 44 - name: validated num_bytes: 104135978 num_examples: 2275 - name: invalidated num_bytes: 7004160 num_examples: 139 download_size: 104280554 dataset_size: 216328039 - config_name: kab features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 3219289101 num_examples: 120530 - name: test num_bytes: 446453041 num_examples: 14622 - name: validation num_bytes: 414159937 num_examples: 14622 - name: other num_bytes: 2282481767 num_examples: 88021 - name: validated num_bytes: 15310455176 num_examples: 573718 - name: invalidated num_bytes: 581587104 num_examples: 18134 download_size: 17171606918 dataset_size: 22254426126 - config_name: ky features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 75460488 num_examples: 1955 - name: test num_bytes: 57116561 num_examples: 1503 - name: validation num_bytes: 61393867 num_examples: 1511 - name: other num_bytes: 258081579 num_examples: 7223 - name: validated num_bytes: 355742823 num_examples: 9236 - name: invalidated num_bytes: 41007711 num_examples: 926 download_size: 579440853 dataset_size: 848803029 - config_name: lg features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 46910479 num_examples: 1250 - name: test num_bytes: 26951803 num_examples: 584 - name: validation num_bytes: 16709367 num_examples: 384 - name: other num_bytes: 111180838 num_examples: 3110 - name: validated num_bytes: 90606863 num_examples: 2220 - name: invalidated num_bytes: 14069959 num_examples: 290 download_size: 208197149 dataset_size: 306429309 - config_name: lt features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 34605356 num_examples: 931 - name: test num_bytes: 19940391 num_examples: 466 - name: validation num_bytes: 10462851 num_examples: 244 - name: other num_bytes: 71150206 num_examples: 1629 - name: validated num_bytes: 65138550 num_examples: 1644 - name: invalidated num_bytes: 4414780 num_examples: 102 download_size: 135299706 dataset_size: 205712134 - config_name: lv features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 67269173 num_examples: 2552 - name: test num_bytes: 56937435 num_examples: 1882 - name: validation num_bytes: 55289058 num_examples: 2002 - name: other num_bytes: 40259801 num_examples: 1560 - name: validated num_bytes: 179726893 num_examples: 6444 - name: invalidated num_bytes: 4383319 num_examples: 143 download_size: 208307691 dataset_size: 403865679 - config_name: mn features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 89913910 num_examples: 2183 - name: test num_bytes: 86737041 num_examples: 1862 - name: validation num_bytes: 82343275 num_examples: 1837 - name: other num_bytes: 146365394 num_examples: 3272 - name: validated num_bytes: 327264827 num_examples: 7487 - name: invalidated num_bytes: 31764232 num_examples: 667 download_size: 486369317 dataset_size: 764388679 - config_name: mt features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 73850815 num_examples: 2036 - name: test num_bytes: 66520195 num_examples: 1617 - name: validation num_bytes: 56412066 num_examples: 1516 - name: other num_bytes: 220666971 num_examples: 5714 - name: validated num_bytes: 218212969 num_examples: 5747 - name: invalidated num_bytes: 12328068 num_examples: 314 download_size: 425114242 dataset_size: 647991084 - config_name: nl features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 321946148 num_examples: 9460 - name: test num_bytes: 205287443 num_examples: 5708 - name: validation num_bytes: 186095353 num_examples: 4938 - name: other num_bytes: 801418 num_examples: 27 - name: validated num_bytes: 1710636990 num_examples: 52488 - name: invalidated num_bytes: 115133112 num_examples: 3308 download_size: 1741827548 dataset_size: 2539900464 - config_name: or features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 16067910 num_examples: 388 - name: test num_bytes: 4270651 num_examples: 98 - name: validation num_bytes: 5485937 num_examples: 129 - name: other num_bytes: 177775963 num_examples: 4302 - name: validated num_bytes: 25824418 num_examples: 615 - name: invalidated num_bytes: 2701922 num_examples: 62 download_size: 199077358 dataset_size: 232126801 - config_name: pa-IN features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 7572499 num_examples: 211 - name: test num_bytes: 4375532 num_examples: 116 - name: validation num_bytes: 1702492 num_examples: 44 - name: other num_bytes: 56683312 num_examples: 1411 - name: validated num_bytes: 13650443 num_examples: 371 - name: invalidated num_bytes: 1690766 num_examples: 43 download_size: 69748265 dataset_size: 85675044 - config_name: pl features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 273394509 num_examples: 7468 - name: test num_bytes: 205047541 num_examples: 5153 - name: validation num_bytes: 195917307 num_examples: 5153 - name: other num_bytes: 442144781 num_examples: 12848 - name: validated num_bytes: 3150860197 num_examples: 90791 - name: invalidated num_bytes: 180801918 num_examples: 4601 download_size: 3537012341 dataset_size: 4448166253 - config_name: pt features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 231451724 num_examples: 6514 - name: test num_bytes: 180108694 num_examples: 4641 - name: validation num_bytes: 165966139 num_examples: 4592 - name: other num_bytes: 283497435 num_examples: 8390 - name: validated num_bytes: 1480529669 num_examples: 41584 - name: invalidated num_bytes: 67948392 num_examples: 1740 download_size: 1704252567 dataset_size: 2409502053 - config_name: rm-sursilv features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 62396326 num_examples: 1384 - name: test num_bytes: 51707733 num_examples: 1194 - name: validation num_bytes: 52114252 num_examples: 1205 - name: other num_bytes: 93351293 num_examples: 2102 - name: validated num_bytes: 166218231 num_examples: 3783 - name: invalidated num_bytes: 30593270 num_examples: 639 download_size: 275950479 dataset_size: 456381105 - config_name: rm-vallader features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 29528457 num_examples: 574 - name: test num_bytes: 18805466 num_examples: 378 - name: validation num_bytes: 17012341 num_examples: 357 - name: other num_bytes: 36890435 num_examples: 727 - name: validated num_bytes: 65711922 num_examples: 1316 - name: invalidated num_bytes: 9356204 num_examples: 374 download_size: 108113989 dataset_size: 177304825 - config_name: ro features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 107235430 num_examples: 3399 - name: test num_bytes: 60106568 num_examples: 1778 - name: validation num_bytes: 30358457 num_examples: 858 - name: other num_bytes: 65805210 num_examples: 1945 - name: validated num_bytes: 197820619 num_examples: 6039 - name: invalidated num_bytes: 11108104 num_examples: 485 download_size: 261978702 dataset_size: 472434388 - config_name: ru features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 686168722 num_examples: 15481 - name: test num_bytes: 385349488 num_examples: 8007 - name: validation num_bytes: 361164462 num_examples: 7963 - name: other num_bytes: 450644862 num_examples: 10247 - name: validated num_bytes: 3212213931 num_examples: 74256 - name: invalidated num_bytes: 145739451 num_examples: 3056 download_size: 3655676916 dataset_size: 5241280916 - config_name: rw features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 21645788973 num_examples: 515197 - name: test num_bytes: 707959382 num_examples: 15724 - name: validation num_bytes: 698662384 num_examples: 15032 - name: other num_bytes: 923146896 num_examples: 22923 - name: validated num_bytes: 35011249432 num_examples: 832929 - name: invalidated num_bytes: 7969286423 num_examples: 206790 download_size: 42545189583 dataset_size: 66956093490 - config_name: sah features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 68286985 num_examples: 1442 - name: test num_bytes: 38534020 num_examples: 757 - name: validation num_bytes: 17900397 num_examples: 405 - name: other num_bytes: 62594222 num_examples: 1275 - name: validated num_bytes: 124800352 num_examples: 2606 - name: invalidated num_bytes: 3594160 num_examples: 66 download_size: 181245626 dataset_size: 315710136 - config_name: sl features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 66122967 num_examples: 2038 - name: test num_bytes: 26872195 num_examples: 881 - name: validation num_bytes: 16353097 num_examples: 556 - name: other num_bytes: 79268518 num_examples: 2502 - name: validated num_bytes: 148371273 num_examples: 4669 - name: invalidated num_bytes: 3048301 num_examples: 92 download_size: 222751292 dataset_size: 340036351 - config_name: sv-SE features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 62727263 num_examples: 2331 - name: test num_bytes: 59127381 num_examples: 2027 - name: validation num_bytes: 53846355 num_examples: 2019 - name: other num_bytes: 109970049 num_examples: 3043 - name: validated num_bytes: 327049001 num_examples: 12552 - name: invalidated num_bytes: 13462567 num_examples: 462 download_size: 421434184 dataset_size: 626182616 - config_name: ta features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 69052658 num_examples: 2009 - name: test num_bytes: 67616865 num_examples: 1781 - name: validation num_bytes: 63248009 num_examples: 1779 - name: other num_bytes: 246650792 num_examples: 7428 - name: validated num_bytes: 438961956 num_examples: 12652 - name: invalidated num_bytes: 23587453 num_examples: 594 download_size: 679766097 dataset_size: 909117733 - config_name: th features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 100435725 num_examples: 2917 - name: test num_bytes: 82030679 num_examples: 2188 - name: validation num_bytes: 63237632 num_examples: 1922 - name: other num_bytes: 95235301 num_examples: 2671 - name: validated num_bytes: 245734783 num_examples: 7028 - name: invalidated num_bytes: 18247080 num_examples: 467 download_size: 341305736 dataset_size: 604921200 - config_name: tr features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 57879052 num_examples: 1831 - name: test num_bytes: 60268059 num_examples: 1647 - name: validation num_bytes: 54914798 num_examples: 1647 - name: other num_bytes: 10954154 num_examples: 325 - name: validated num_bytes: 585777527 num_examples: 18685 - name: invalidated num_bytes: 59288266 num_examples: 1726 download_size: 620848700 dataset_size: 829081856 - config_name: tt features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 348132697 num_examples: 11211 - name: test num_bytes: 135120057 num_examples: 4485 - name: validation num_bytes: 61690964 num_examples: 2127 - name: other num_bytes: 62158038 num_examples: 1798 - name: validated num_bytes: 767791517 num_examples: 25781 - name: invalidated num_bytes: 10403128 num_examples: 287 download_size: 777153207 dataset_size: 1385296401 - config_name: uk features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 161925063 num_examples: 4035 - name: test num_bytes: 138422211 num_examples: 3235 - name: validation num_bytes: 135483169 num_examples: 3236 - name: other num_bytes: 327979131 num_examples: 8161 - name: validated num_bytes: 889863965 num_examples: 22337 - name: invalidated num_bytes: 55745301 num_examples: 1255 download_size: 1218559031 dataset_size: 1709418840 - config_name: vi features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 6244454 num_examples: 221 - name: test num_bytes: 6656365 num_examples: 198 - name: validation num_bytes: 6531856 num_examples: 200 - name: other num_bytes: 31315434 num_examples: 870 - name: validated num_bytes: 19432595 num_examples: 619 - name: invalidated num_bytes: 2981661 num_examples: 78 download_size: 51929480 dataset_size: 73162365 - config_name: vot features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 146467 num_examples: 3 - name: test - name: validation - name: other num_bytes: 7963322 num_examples: 411 - name: validated num_bytes: 146467 num_examples: 3 - name: invalidated num_bytes: 107949 num_examples: 6 download_size: 7792602 dataset_size: 8364205 - config_name: zh-CN features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 793667379 num_examples: 18541 - name: test num_bytes: 420202544 num_examples: 8760 - name: validation num_bytes: 396096323 num_examples: 8743 - name: other num_bytes: 381264783 num_examples: 8948 - name: validated num_bytes: 1618113625 num_examples: 36405 - name: invalidated num_bytes: 266234479 num_examples: 5305 download_size: 2184602350 dataset_size: 3875579133 - config_name: zh-HK features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 221459521 num_examples: 7506 - name: test num_bytes: 217627041 num_examples: 5172 - name: validation num_bytes: 196071110 num_examples: 5172 - name: other num_bytes: 1319233252 num_examples: 38830 - name: validated num_bytes: 1482087591 num_examples: 41835 - name: invalidated num_bytes: 124170969 num_examples: 2999 download_size: 2774145806 dataset_size: 3560649484 - config_name: zh-TW features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 97323787 num_examples: 3507 - name: test num_bytes: 85512325 num_examples: 2895 - name: validation num_bytes: 80402637 num_examples: 2895 - name: other num_bytes: 623801957 num_examples: 22477 - name: validated num_bytes: 1568842090 num_examples: 61232 - name: invalidated num_bytes: 100241443 num_examples: 3584 download_size: 2182836295 dataset_size: 2556124239 config_names: - ab - ar - as - br - ca - cnh - cs - cv - cy - de - dv - el - en - eo - es - et - eu - fa - fi - fr - fy-NL - ga-IE - hi - hsb - hu - ia - id - it - ja - ka - kab - ky - lg - lt - lv - mn - mt - nl - or - pa-IN - pl - pt - rm-sursilv - rm-vallader - ro - ru - rw - sah - sl - sv-SE - ta - th - tr - tt - uk - vi - vot - zh-CN - zh-HK - zh-TW --- # Dataset Card for common_voice <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Deprecated:</b> Dataset "common_voice" is deprecated and will soon be deleted. Use datasets under <a href="https://huggingface.co/mozilla-foundation">mozilla-foundation</a> organisation instead. For example, you can load <a href="https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0">Common Voice 13</a> dataset via <code>load_dataset("mozilla-foundation/common_voice_13_0", "en")</code></p> </div> ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://commonvoice.mozilla.org/en/datasets - Repository: https://github.com/common-voice/common-voice - Paper: https://commonvoice.mozilla.org/en/datasets - Leaderboard: [Needs More Information] - Point of Contact: [Needs More Information] ### Dataset Summary The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 9,283 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help train the accuracy of speech recognition engines. The dataset currently consists of 7,335 validated hours in 60 languages, but were always adding more voices and languages. Take a look at our Languages page to request a language or start contributing. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages English ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file, called path and its sentence. Additional fields include accent, age, client_id, up_votes down_votes, gender, locale and segment. ` {'accent': 'netherlands', 'age': 'fourties', 'client_id': 'bbbcb732e0f422150c30ff3654bbab572e2a617da107bca22ff8b89ab2e4f124d03b6a92c48322862f60bd0179ae07baf0f9b4f9c4e11d581e0cec70f703ba54', 'down_votes': 0, 'gender': 'male', 'locale': 'nl', 'path': 'nl/clips/common_voice_nl_23522441.mp3', 'segment': "''", 'sentence': 'Ik vind dat een dubieuze procedure.', 'up_votes': 2, 'audio': {'path': `nl/clips/common_voice_nl_23522441.mp3', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 48000} ` ### Data Fields client_id: An id for which client (voice) made the recording path: The path to the audio file audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, i.e. `dataset[0]["audio"]` should always be preferred over `dataset["audio"][0]`. sentence: The sentence the user was prompted to speak up_votes: How many upvotes the audio file has received from reviewers down_votes: How many downvotes the audio file has received from reviewers age: The age of the speaker. gender: The gender of the speaker accent: Accent of the speaker locale: The locale of the speaker segment: Usually empty field ### Data Splits The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other. The validated data is data that has been validated with reviewers and recieved upvotes that the data is of high quality. The invalidated data is data has been invalidated by reviewers and recieved downvotes that the data is of low quality. The reported data is data that has been reported, for different reasons. The other data is data that has not yet been reviewed. The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train. ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ## Considerations for Using the Data ### Social Impact of Dataset The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/) ### Citation Information ``` @inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 } ``` ### Contributions Thanks to [@BirgerMoell](https://github.com/BirgerMoell) for adding this dataset.
AI4Sec/cti-bench	AI4Sec	2024-08-17T00:58:57Z	2,536	6	[ "task_categories:zero-shot-classification", "task_categories:question-answering", "task_categories:text-classification", "language:en", "license:cc-by-nc-sa-4.0", "size_categories:1K<n<10K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2406.07599", "doi:10.57967/hf/2506", "region:us", "cti", "cyber threat intelligence", "llm" ]	[ "zero-shot-classification", "question-answering", "text-classification" ]	2024-06-12T10:24:23Z	2	--- license: cc-by-nc-sa-4.0 task_categories: - zero-shot-classification - question-answering - text-classification language: - en tags: - cti - cyber threat intelligence - llm pretty_name: CTIBench size_categories: - 1K<n<10K configs: - config_name: cti-mcq data_files: - split: test path: "cti-mcq.tsv" sep: "\t" - config_name: cti-rcm data_files: - split: test path: "cti-rcm.tsv" sep: "\t" - config_name: cti-vsp data_files: - split: test path: "cti-vsp.tsv" sep: "\t" - config_name: cti-taa data_files: - split: test path: "cti-taa.tsv" sep: "\t" - config_name: cti-ate data_files: - split: test path: "cti-ate.tsv" sep: "\t" - config_name: cti-rcm-2021 data_files: - split: test path: "cti-rcm-2021.tsv" sep: "\t" --- # Dataset Card for CTIBench <!-- Provide a quick summary of the dataset. --> A set of benchmark tasks designed to evaluate large language models (LLMs) on cyber threat intelligence (CTI) tasks. ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> CTIBench is a comprehensive suite of benchmark tasks and datasets designed to evaluate LLMs in the field of CTI. Components: - CTI-MCQ: A knowledge evaluation dataset with multiple-choice questions to assess the LLMs' understanding of CTI standards, threats, detection strategies, mitigation plans, and best practices. This dataset is built using authoritative sources and standards within the CTI domain, including NIST, MITRE, and GDPR. - CTI-RCM: A practical task that involves mapping Common Vulnerabilities and Exposures (CVE) descriptions to Common Weakness Enumeration (CWE) categories. This task evaluates the LLMs' ability to understand and classify cyber threats. - CTI-VSP: Another practical task that requires calculating the Common Vulnerability Scoring System (CVSS) scores. This task assesses the LLMs' ability to evaluate the severity of cyber vulnerabilities. - CTI-TAA: A task that involves analyzing publicly available threat reports and attributing them to specific threat actors or malware families. This task tests the LLMs' capability to understand historical cyber threat behavior and identify meaningful correlations. - Curated by: Md Tanvirul Alam & Dipkamal Bhusal (RIT) <!-- - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] --> ### Dataset Sources <!-- Provide the basic links for the dataset. --> Repository: https://github.com/xashru/cti-bench <!-- - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] --> ## Uses <!-- Address questions around how the dataset is intended to be used. --> CTIBench is designed to provide a comprehensive evaluation framework for large language models (LLMs) within the domain of cyber threat intelligence (CTI). Dataset designed in CTIBench assess the understanding of CTI standards, threats, detection strategies, mitigation plans, and best practices by LLMs, and evaluates the LLMs' ability to understand, and analyze about cyber threats and vulnerabilities. <!-- ### Direct Use This section describes suitable use cases for the dataset. [More Information Needed] --> <!-- ### Out-of-Scope Use --> <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> <!-- [More Information Needed] --> ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> The dataset consists of 5 TSV files, each corresponding to a different task. Each TSV file contains a "Prompt" column used to pose questions to the LLM. Most files also include a "GT" column that contains the ground truth for the questions, except for "cti-taa.tsv". The evaluation scripts for the different tasks are available in the associated GitHub repository. ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> This dataset was curated to evaluate the ability of LLMs to understand and analyze various aspects of open-source CTI. ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> The dataset includes URLs indicating the sources from which the data was collected. <!-- #### Data Collection and Processing This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. [More Information Needed] --> <!-- #### Who are the source data producers? This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. [More Information Needed] --> #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> The dataset does not contain any personal or sensitive information. <!-- ## Bias, Risks, and Limitations This section is meant to convey both technical and sociotechnical limitations. [More Information Needed] --> <!-- ### Recommendations This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. --> ## Citation The paper can be found at: https://arxiv.org/abs/2406.07599 BibTeX: ```bibtex @misc{alam2024ctibench, title={CTIBench: A Benchmark for Evaluating LLMs in Cyber Threat Intelligence}, author={Md Tanvirul Alam and Dipkamal Bhushal and Le Nguyen and Nidhi Rastogi}, year={2024}, eprint={2406.07599}, archivePrefix={arXiv}, primaryClass={cs.CR} } ``` <!-- APA: [More Information Needed] --> <!-- ## Glossary [optional] If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. [More Information Needed] --> <!-- ## More Information [optional] [More Information Needed] --> <!-- ## Dataset Card Authors [optional] [More Information Needed] --> ## Dataset Card Contact Md Tanvirul Alam (ma8235 @ rit . edu)
arcee-ai/The-Tome	arcee-ai	2024-08-15T16:08:27Z	267	90	[ "license:mit", "size_categories:1M<n<10M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-07-22T01:51:38Z	null	--- license: mit --- <div align="center"> <img src="https://i.ibb.co/0jqCGH6/LEW5-CGBKRv-CWKNf-KYkf-k-Q.jpg" alt="The Tome" style="border-radius: 10px; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19); max-width: 100%; height: auto;"> </div> The Tome is a curated dataset designed for training large language models with a focus on instruction following. It was used in the training of our Arcee-Nova/Spark models, which was later merged with Qwen2-72B-Instruct (or 7B with the Spark model). ## Dataset Composition - Total Samples: 1.75 million - Source: Compiled from 9 publicly available datasets The Tome is comprised of the following datasets: ```markdown arcee-ai/infini-instruct-top-500k (BAAI/Infinity-Instruct) TIGER-Lab/WebInstructSub (top-500k) jondurbin/airoboros-3.2 gardner/glaive-function-calling-v2-sharegpt arcee-ai/reasoning-sharegpt (SkunkworksAI/reasoning-0.01) arcee-ai/self-instruct-sharegpt (bigcode/self-oss-instruct-sc2-exec-filter-50k) cognitivecomputations/ultrainteract_trajectories_sharegpt cognitivecomputations/SystemChat-2.0 arcee-ai/qwen2-72b-magpie-en ``` ## Curation Process The dataset underwent a curation process to ensure high-quality content: 1. Reranker: Applied for instruction following on Infini-Instruct and WebInstruct. 2. Educational Value Scoring: Used the fineweb-edu classifier on Infini-Instruct and WebInstruct 3. Composite Scoring: Scores from the custom reranker and fineweb-edu classifier were averaged. ## Usage in Model Training The Tome was instrumental in the development of the Nova model, which was subsequently merged with Qwen2-72B-Instruct: - Merge Process: - Lower layers primarily from Qwen2-72B-Instruct - Higher layers primarily from Nova-Premerge
NbAiLab/NPSC	NbAiLab	2024-08-14T13:57:39Z	557	8	[ "task_categories:automatic-speech-recognition", "task_categories:audio-classification", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:no", "language:nb", "language:nn", "license:cc0-1.0", "region:us", "speech-modeling" ]	[ "automatic-speech-recognition", "audio-classification" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - no-annotation language_creators: - found language: - 'no' - nb - nn license: - cc0-1.0 multilinguality: - monolingual size_categories: - 2G<n<1B source_datasets: - original task_categories: - automatic-speech-recognition - audio-classification pretty_name: NPSC tags: - speech-modeling --- # Dataset Card for NbAiLab/NPSC ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Data Fields](#data-fiels) - [Dataset Creation](#dataset-creation) - [Statistics](#statistics) - [Document Types](#document-types) - [Languages](#languages) - [Publish Periode](#publish-periode) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - Homepage: https://www.nb.no/sprakbanken/ - Repository: https://www.nb.no/sprakbanken/ressurskatalog/oai-nb-no-sbr-58/ - Paper: https://www.nb.no/sprakbanken/ - Point of Contact: [Per Erik Solberg](mailto:[email protected]) The Norwegian Parliamentary Speech Corpus (NPSC) is a speech corpus made by the Norwegian Language Bank at the National Library of Norway in 2019-2021. The NPSC consists of recordings of speech from Stortinget, the Norwegian parliament, and corresponding orthographic transcriptions to Norwegian Bokmål and Norwegian Nynorsk. All transcriptions are done manually by trained linguists or philologists, and the manual transcriptions are subsequently proofread to ensure consistency and accuracy. Entire days of Parliamentary meetings are transcribed in the dataset. This repository contains a version of the NPSC in the 🤗 Dataset Format. Note that the official release of the dataset, which can be found in [the repository of the Norwegian Language Bank](https://www.nb.no/sprakbanken/ressurskatalog/oai-nb-no-sbr-58/), contains more information than the version found here, including word-level metadata, metadata about the speakers, and detailed documentation. ## How to Use ```python # Loads the 16K Bokmål corpus in streaming mode from datasets import load_dataset data = load_dataset("NbAiLab/NPSC", config="16K_mp3_bokmaal", streaming=True) ``` ## Dataset Summary The NPSC dataset contains JSON lines with language training data. The data loader will add audio data to this structure. Here is an example json object: ```json { "sentence_id": 49853, "sentence_order": 0, "speaker_id": 32, "meeting_date": "20170110", "speaker_name": "Olemic Thommessen", "sentence_text": "Stortingets møte er lovlig satt", "sentence_language_code": "nb-NO", "text": "Stortingets møte er lovlig satt", "start_time": 320246, "end_time": 323590, "normsentence_text": "Stortingets møte er lovlig satt", "transsentence_text": "Stortingets møte er lovleg sett", "translated": 1, "audio": {"path": "audio/20170110-095504_320246_323590.wav","array": [.......]} } ``` ## Data Fields \|Key \| Type \| Description \| \|:-----------\|:------------\|:------------\| \|sentence_id: \| Integer \| Unique identifier of the sentence \| \|sentence_order \| Integer \| A number indicating the order of the sentences in the meeting \| \|speaker_id \| Integer \| The ID of the speaker. This can be linked to the original dataset containing thorough demographic and dialectal information about the speaker. \| \|meeting_date \| String \| The date for the meeting in the format __yyyymmdd__ \| \| speaker_name \| String \| Name of the speaker. All speakers were members of the Norwegian Parliament or members of the Norwegian Government at the meeting date \| \| sentence_text \| String \| The sentence text. The transcribed text string of the sentence in non-normalized form. This is the text of the manual transcriptions, without any postprocessing (apart from corrections of known errors). It may contain interrupted words, non-standard words and function words with a pronunciation deviating from the written form. Detailed metadata about the words in the sentence can be found in the word-tokenized version of the corpus in the official release of the dataset. \| \| sentence_language_code \| String \| The language code of the sentence. The following alternatives exists in the file: ['nb-NO'. 'nn-NO', 'en-US']\| \| text \| String \| sentence text. This is a copy of "sentence_text". It is included here to make it more convenient to interleave with other datasets.\| \| start_time \| Integer \| The start time of the sentence in milliseconds. This time is relative to the start of audiofile of the entire meeting, which can be accessed in the official release \| \| end_time \| Integer \| End time. See comment above. \| \| normsentence_text \| String \| Normalized sentence text. In this version of the transcription, numbers and dates are written in digits on standardized formats, and common abbreviations are used. These modifications to the original transcriptions are produced automatically using normalization grammars \| \| transsentence_text \| String \| Translated sentence text. Whenever the original transcription is in Bokmål (nb-NO), this field contains a machine-translated version in Nynorsk (nn-NO), and vice versa \| \| translated \| Integer \| A flag indicating whether a machine-translated version has been produced or not. Sentences in en-US have not been translated \| \| audio \| Array \| The dataloader will encode the accociated audio files and provide them as an array containing 'path', 'sound array','sampling_rate' \| #### Initial Data Collection The procedure for the dataset creation is described in detail in our paper. ## Statistics \| Feature \| Value \| \|:---------\|-----------:\| \| Duration, pauses included \| 140,3 hours\| \| Duration, pauses not included \| 125,7 hours \| \| Word count \| 1,2 million \| \| Sentence count \| 64.531 \| \| Language distribution \| Nynorsk: 12,8%\| \| \| Bokmål: 87,2%\| \| Gender distribution \| Female: 38,3% \| \| \| Male: 61.7% \| ## Considerations for Using the Data This corpus contains speech data. All recordings are of members of Parliament in a public setting, and can be distributed without any restrains. ### Dataset Creators and Curators The content of the dataset was created by the Norwegian Language Bank (Språkbanken) at the National Library of Norway. [Javier de la Rosa](mailto:[email protected]), [Freddy Wetjen](mailto:[email protected]), [Per Egil Kummervold](mailto:[email protected]), and [Andre Kaasen](mailto:[email protected]) all contributed in making this into a HuggingFace Dataset. Thanks to the HuggingFace team for assistance. ## License The sound and the transcriptions are released under the [CC-ZERO-license](https://creativecommons.org/publicdomain/zero/1.0/). The curation of the HuggingFace Dataset is released under [CC-BY-SA-3-license](https://creativecommons.org/licenses/by-sa/3.0/). ### Citation Information The following article gives detailed information about the corpus. Please refer to the article and this page if you are using this dataset: ``` @inproceedings{solberg2022norwegian, title={The Norwegian Parliamentary Speech Corpus}, author={Solberg, Per Erik and Ortiz, Pablo}, booktitle={Proceedings of the 13th Language Resources and Evaluation Conference}, url={http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.106.pdf}, year={2022} } ```
keithito/lj_speech	keithito	2024-08-14T11:13:15Z	1,034	54	[ "task_categories:automatic-speech-recognition", "task_categories:text-to-speech", "task_categories:text-to-audio", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:unlicense", "size_categories:10K<n<100K", "region:us" ]	[ "automatic-speech-recognition", "text-to-speech", "text-to-audio" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - expert-generated language_creators: - found language: - en license: - unlicense multilinguality: - monolingual paperswithcode_id: ljspeech pretty_name: LJ Speech size_categories: - 10K<n<100K source_datasets: - original task_categories: - automatic-speech-recognition - text-to-speech - text-to-audio task_ids: [] train-eval-index: - config: main task: automatic-speech-recognition task_id: speech_recognition splits: train_split: train col_mapping: file: path text: text metrics: - type: wer name: WER - type: cer name: CER dataset_info: features: - name: id dtype: string - name: audio dtype: audio: sampling_rate: 22050 - name: file dtype: string - name: text dtype: string - name: normalized_text dtype: string config_name: main splits: - name: train num_bytes: 4667022 num_examples: 13100 download_size: 2748572632 dataset_size: 4667022 --- # Dataset Card for lj_speech ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [The LJ Speech Dataset](https://keithito.com/LJ-Speech-Dataset/) - Repository: [N/A] - Paper: [N/A] - Leaderboard: [Paperswithcode Leaderboard](https://paperswithcode.com/sota/text-to-speech-synthesis-on-ljspeech) - Point of Contact: [Keith Ito](mailto:[email protected]) ### Dataset Summary This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books in English. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours. The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded in 2016-17 by the LibriVox project and is also in the public domain. ### Supported Tasks and Leaderboards The dataset can be used to train a model for Automatic Speech Recognition (ASR) or Text-to-Speech (TTS). - `automatic-speech-recognition`: An ASR model is presented with an audio file and asked to transcribe the audio file to written text. The most common ASR evaluation metric is the word error rate (WER). - `text-to-speech`, `text-to-audio`: A TTS model is given a written text in natural language and asked to generate a speech audio file. A reasonable evaluation metric is the mean opinion score (MOS) of audio quality. The dataset has an active leaderboard which can be found at https://paperswithcode.com/sota/text-to-speech-synthesis-on-ljspeech ### Languages The transcriptions and audio are in English. ## Dataset Structure ### Data Instances A data point comprises the path to the audio file, called `file` and its transcription, called `text`. A normalized version of the text is also provided. ``` { 'id': 'LJ002-0026', 'file': '/datasets/downloads/extracted/05bfe561f096e4c52667e3639af495226afe4e5d08763f2d76d069e7a453c543/LJSpeech-1.1/wavs/LJ002-0026.wav', 'audio': {'path': '/datasets/downloads/extracted/05bfe561f096e4c52667e3639af495226afe4e5d08763f2d76d069e7a453c543/LJSpeech-1.1/wavs/LJ002-0026.wav', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 22050}, 'text': 'in the three years between 1813 and 1816,' 'normalized_text': 'in the three years between eighteen thirteen and eighteen sixteen,', } ``` Each audio file is a single-channel 16-bit PCM WAV with a sample rate of 22050 Hz. ### Data Fields - id: unique id of the data sample. - file: a path to the downloaded audio file in .wav format. - audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, i.e. `dataset[0]["audio"]` should always be preferred over `dataset["audio"][0]`. - text: the transcription of the audio file. - normalized_text: the transcription with numbers, ordinals, and monetary units expanded into full words. ### Data Splits The dataset is not pre-split. Some statistics: - Total Clips: 13,100 - Total Words: 225,715 - Total Characters: 1,308,678 - Total Duration: 23:55:17 - Mean Clip Duration: 6.57 sec - Min Clip Duration: 1.11 sec - Max Clip Duration: 10.10 sec - Mean Words per Clip: 17.23 - Distinct Words: 13,821 ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization This dataset consists of excerpts from the following works: - Morris, William, et al. Arts and Crafts Essays. 1893. - Griffiths, Arthur. The Chronicles of Newgate, Vol. 2. 1884. - Roosevelt, Franklin D. The Fireside Chats of Franklin Delano Roosevelt. 1933-42. - Harland, Marion. Marion Harland's Cookery for Beginners. 1893. - Rolt-Wheeler, Francis. The Science - History of the Universe, Vol. 5: Biology. 1910. - Banks, Edgar J. The Seven Wonders of the Ancient World. 1916. - President's Commission on the Assassination of President Kennedy. Report of the President's Commission on the Assassination of President Kennedy. 1964. Some details about normalization: - The normalized transcription has the numbers, ordinals, and monetary units expanded into full words (UTF-8) - 19 of the transcriptions contain non-ASCII characters (for example, LJ016-0257 contains "raison d'être"). - The following abbreviations appear in the text. They may be expanded as follows: \| Abbreviation \| Expansion \| \|--------------\|-----------\| \| Mr. \| Mister \| \| Mrs. \| Misess () \| \| Dr. \| Doctor \| \| No. \| Number \| \| St. \| Saint \| \| Co. \| Company \| \| Jr. \| Junior \| \| Maj. \| Major \| \| Gen. \| General \| \| Drs. \| Doctors \| \| Rev. \| Reverend \| \| Lt. \| Lieutenant \| \| Hon. \| Honorable \| \| Sgt. \| Sergeant \| \| Capt. \| Captain \| \| Esq. \| Esquire \| \| Ltd. \| Limited \| \| Col. \| Colonel \| \| Ft. \| Fort \| () there's no standard expansion for "Mrs." #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process - The audio clips range in length from approximately 1 second to 10 seconds. They were segmented automatically based on silences in the recording. Clip boundaries generally align with sentence or clause boundaries, but not always. - The text was matched to the audio manually, and a QA pass was done to ensure that the text accurately matched the words spoken in the audio. #### Who are the annotators? Recordings by Linda Johnson from LibriVox. Alignment and annotation by Keith Ito. ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations - The original LibriVox recordings were distributed as 128 kbps MP3 files. As a result, they may contain artifacts introduced by the MP3 encoding. ## Additional Information ### Dataset Curators The dataset was initially created by Keith Ito and Linda Johnson. ### Licensing Information Public Domain ([LibriVox](https://librivox.org/pages/public-domain/)) ### Citation Information ``` @misc{ljspeech17, author = {Keith Ito and Linda Johnson}, title = {The LJ Speech Dataset}, howpublished = {\url{https://keithito.com/LJ-Speech-Dataset/}}, year = 2017 } ``` ### Contributions Thanks to [@anton-l](https://github.com/anton-l) for adding this dataset.
openslr/librispeech_asr	openslr	2024-08-14T10:48:50Z	12,204	144	[ "task_categories:automatic-speech-recognition", "task_categories:audio-classification", "task_ids:speaker-identification", "annotations_creators:expert-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-4.0", "size_categories:100K<n<1M", "region:us" ]	[ "automatic-speech-recognition", "audio-classification" ]	2022-03-02T23:29:22Z	null	--- pretty_name: LibriSpeech annotations_creators: - expert-generated language_creators: - crowdsourced - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual paperswithcode_id: librispeech-1 size_categories: - 100K<n<1M source_datasets: - original task_categories: - automatic-speech-recognition - audio-classification task_ids: - speaker-identification dataset_info: - config_name: clean features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string splits: - name: train.100 num_bytes: 6619683041 num_examples: 28539 - name: train.360 num_bytes: 23898214592 num_examples: 104014 - name: validation num_bytes: 359572231 num_examples: 2703 - name: test num_bytes: 367705423 num_examples: 2620 download_size: 30121377654 dataset_size: 31245175287 - config_name: other features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string splits: - name: train.500 num_bytes: 31810256902 num_examples: 148688 - name: validation num_bytes: 337283304 num_examples: 2864 - name: test num_bytes: 352396474 num_examples: 2939 download_size: 31236565377 dataset_size: 32499936680 - config_name: all features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string splits: - name: train.clean.100 num_bytes: 6627791685 num_examples: 28539 - name: train.clean.360 num_bytes: 23927767570 num_examples: 104014 - name: train.other.500 num_bytes: 31852502880 num_examples: 148688 - name: validation.clean num_bytes: 359505691 num_examples: 2703 - name: validation.other num_bytes: 337213112 num_examples: 2864 - name: test.clean num_bytes: 368449831 num_examples: 2620 - name: test.other num_bytes: 353231518 num_examples: 2939 download_size: 61357943031 dataset_size: 63826462287 --- # Dataset Card for librispeech_asr ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [LibriSpeech ASR corpus](http://www.openslr.org/12) - Repository: [Needs More Information] - Paper: [LibriSpeech: An ASR Corpus Based On Public Domain Audio Books](https://www.danielpovey.com/files/2015_icassp_librispeech.pdf) - Leaderboard: [The 🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench) - Point of Contact: [Daniel Povey](mailto:[email protected]) ### Dataset Summary LibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned. ### Supported Tasks and Leaderboards - `automatic-speech-recognition`, `audio-speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active Hugging Face leaderboard which can be found at https://huggingface.co/spaces/huggingface/hf-speech-bench. The leaderboard ranks models uploaded to the Hub based on their WER. An external leaderboard at https://paperswithcode.com/sota/speech-recognition-on-librispeech-test-clean ranks the latest models from research and academia. ### Languages The audio is in English. There are two configurations: `clean` and `other`. The speakers in the corpus were ranked according to the WER of the transcripts of a model trained on a different dataset, and were divided roughly in the middle, with the lower-WER speakers designated as "clean" and the higher WER speakers designated as "other". ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided. ``` {'chapter_id': 141231, 'file': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac', 'audio': {'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'id': '1272-141231-0000', 'speaker_id': 1272, 'text': 'A MAN SAID TO THE UNIVERSE SIR I EXIST'} ``` ### Data Fields - file: A path to the downloaded audio file in .flac format. - audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, i.e. `dataset[0]["audio"]` should always be preferred over `dataset["audio"][0]`. - text: the transcription of the audio file. - id: unique id of the data sample. - speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples. - chapter_id: id of the audiobook chapter which includes the transcription. ### Data Splits The size of the corpus makes it impractical, or at least inconvenient for some users, to distribute it as a single large archive. Thus the training portion of the corpus is split into three subsets, with approximate size 100, 360 and 500 hours respectively. A simple automatic procedure was used to select the audio in the first two sets to be, on average, of higher recording quality and with accents closer to US English. An acoustic model was trained on WSJ’s si-84 data subset and was used to recognize the audio in the corpus, using a bigram LM estimated on the text of the respective books. We computed the Word Error Rate (WER) of this automatic transcript relative to our reference transcripts obtained from the book texts. The speakers in the corpus were ranked according to the WER of the WSJ model’s transcripts, and were divided roughly in the middle, with the lower-WER speakers designated as "clean" and the higher-WER speakers designated as "other". For "clean", the data is split into train, validation, and test set. The train set is further split into train.100 and train.360 respectively accounting for 100h and 360h of the training data. For "other", the data is split into train, validation, and test set. The train set contains approximately 500h of recorded speech. \| \| Train.500 \| Train.360 \| Train.100 \| Valid \| Test \| \| ----- \| ------ \| ----- \| ---- \| ---- \| ---- \| \| clean \| - \| 104014 \| 28539 \| 2703 \| 2620\| \| other \| 148688 \| - \| - \| 2864 \| 2939 \| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators The dataset was initially created by Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur. ### Licensing Information [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @inproceedings{panayotov2015librispeech, title={Librispeech: an ASR corpus based on public domain audio books}, author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev}, booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on}, pages={5206--5210}, year={2015}, organization={IEEE} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
HuggingFaceTB/cosmopedia	HuggingFaceTB	2024-08-12T22:05:49Z	38,739	608	[ "language:en", "license:apache-2.0", "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2309.05463", "arxiv:2306.11644", "region:us", "synthetic" ]	[]	2024-02-18T20:23:48Z	null	--- dataset_info: - config_name: auto_math_text features: - name: prompt dtype: string - name: text_token_length dtype: int64 - name: text dtype: string - name: seed_data dtype: string - name: format dtype: string - name: audience dtype: string splits: - name: train num_bytes: 8777587297.907892 num_examples: 1949895 download_size: 4461401898 dataset_size: 8777587297.907892 - config_name: khanacademy features: - name: prompt dtype: string - name: text_token_length dtype: int64 - name: text dtype: string - name: seed_data dtype: string - name: format dtype: string - name: audience dtype: string splits: - name: train num_bytes: 108591354.09210858 num_examples: 24123 download_size: 49139761 dataset_size: 108591354.09210858 - config_name: openstax features: - name: text_token_length dtype: int64 - name: prompt dtype: string - name: text dtype: string - name: seed_data dtype: string - name: format dtype: string - name: audience dtype: string splits: - name: train num_bytes: 667837450 num_examples: 126332 download_size: 346992522 dataset_size: 667837450 - config_name: stanford features: - name: text_token_length dtype: int64 - name: prompt dtype: string - name: text dtype: string - name: seed_data dtype: string - name: format dtype: string - name: audience dtype: string splits: - name: train num_bytes: 6341291506 num_examples: 1020024 download_size: 3302284560 dataset_size: 6341291506 - config_name: stories features: - name: text dtype: string - name: prompt dtype: string - name: text_token_length dtype: int64 - name: seed_data dtype: string - name: format dtype: string - name: audience dtype: string splits: - name: train num_bytes: 21314739648 num_examples: 4992964 download_size: 11902294709 dataset_size: 21314739648 - config_name: web_samples_v1 features: - name: text_token_length dtype: int64 - name: prompt dtype: string - name: text dtype: string - name: seed_data dtype: string - name: format dtype: string - name: audience dtype: string splits: - name: train num_bytes: 69075726295 num_examples: 12426348 download_size: 38978124936 dataset_size: 69075726295 - config_name: web_samples_v2 features: - name: text_token_length dtype: int64 - name: prompt dtype: string - name: text dtype: string - name: seed_data dtype: string - name: format dtype: string - name: audience dtype: string splits: - name: train num_bytes: 58711802939 num_examples: 10345867 download_size: 32658254617 dataset_size: 58711802939 - config_name: wikihow features: - name: text_token_length dtype: int64 - name: prompt dtype: string - name: text dtype: string - name: seed_data dtype: string - name: format dtype: string - name: audience dtype: string splits: - name: train num_bytes: 892720528 num_examples: 179191 download_size: 502284600 dataset_size: 892720528 configs: - config_name: auto_math_text data_files: - split: train path: data/auto_math_text/train-* - config_name: khanacademy data_files: - split: train path: data/khanacademy/train-* - config_name: openstax data_files: - split: train path: data/openstax/train-* - config_name: stanford data_files: - split: train path: data/stanford/train-* - config_name: stories data_files: - split: train path: data/stories/train-* - config_name: web_samples_v1 data_files: - split: train path: data/web_samples_v1/train-* - config_name: web_samples_v2 data_files: - split: train path: data/web_samples_v2/train-* - config_name: wikihow data_files: - split: train path: data/wikihow/train-* license: apache-2.0 language: - en tags: - synthetic --- # Cosmopedia v0.1 <center> <img src="https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/8a9ZTW8sC4utjEPIrZegN.png" alt="Cosmopedia v0.1" width="600" height="300"> <p><em>Image generated by DALL-E, the <a href="https://huggingface.co/datasets/HuggingFaceTB/miscellaneous/blob/main/cosmopedia_dalle_prompt_by_mixtral.txt">prompt</a> was generated by Mixtral-8x7B-Instruct-v0.1</em></p> </center> Note: Cosmopedia v0.2 is available at [smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) ``` User: What do you think "Cosmopedia" could mean? Hint: in our case it's not related to cosmology. Mixtral-8x7B-Instruct-v0.1: A possible meaning for "Cosmopedia" could be an encyclopedia or collection of information about different cultures, societies, and topics from around the world, emphasizing diversity and global connectedness. ``` Cosmopedia is a dataset of synthetic textbooks, blogposts, stories, posts and WikiHow articles generated by [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1).The dataset contains over 30 million files and 25 billion tokens, making it the largest open synthetic dataset to date. It covers a variety of topics; we tried to map world knowledge present in Web datasets like [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) and [RedPajama](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T), and generate synthetic content that covers them. This is the v0.1 of Cosmopedia, with ample room for improvement and topics to be more comprehensively covered. We hope this dataset will help the community's research efforts in the increasingly intriguing domain of synthetic data. You can find a clickable map by Nomic at [https://atlas.nomic.ai/map/cosmopedia](https://atlas.nomic.ai/map/cosmopedia). This work is inspired by the great work of [Phi1.5](https://huggingface.co/papers/2309.05463). You can find more details about the dataset in our blog post: https://huggingface.co/blog/cosmopedia # TL;DR This is a synthetic dataset of 30M samples generated by [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1). It contains 8 splits depending on the source of the seed samples we use in the prompts, the model is asked to generate content related to them. The splits range from web samples to educational resources like Stanford, OpenStax and KhanAcademy, we also use some instruction-tuning datasets as seed samples for stories. Here's how you can load a dataset split: ```python from datasets import load_dataset ds = load_dataset("HuggingFaceTB/cosmopedia", "stories", split="train", num_proc=12) ds[0] ``` If you want a smaller subset of the dataset check [Cosmopedia-100k](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia-100k). We also trained a 1.8B model on Cosmopedia [Cosmo-1B](https://huggingface.co/HuggingFaceTB/cosmopedian-1b). # Dataset splits The prompts are all based on the concept of using a seed sample (for example an extract from a web page) and asking the model to generate new content (textbook, story, blogpost..) related to that seed sample. The dataset consist of 8 splits depending on the source of the seed data used in the split. Some seed samples may appear more than once when we ask for a different style (e.g academic textbook vs blogpost) or audience (e.g young children vs college students). For example, each sample in `stanford` was used with 4 different prompt styles and audiences, check the `format` and `audience` columns for more details. We observed that tailoring the audience and prompt style accordingly significantly enhances diversity; the proportion of duplicates eliminated via MinHash was under 1%. The graph below shows the distribution of seed datasets, generations formats and audiences in Cosmopedia: <center> <img src="https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/V7MGV2OrCfLO5TxKPUXs4.png" alt="distributions" width="1000" height="500"> </center> Below are the 8 splits: - `web_samples_v1`: this and `web_samples_v2` are the largest splits (they make up~75% of the dataset), where we use samples from an internal web dataset similar to [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb). These samples were selected based on their topic, using a clustering method explained in the section below. - `web_samples_v2`: similar to `web_samples_v2` using different samples. We call it v2 because we refined the prompts for this split (e.g asking for more depth over breadth in the concepts explanations and requesting the model to not generate a title and introductory sentences, which might be redundant across samples). - `stanford`: we scraped course outlines from [stanford.edu](https://explorecourses.stanford.edu/search?q=all%20courses), and each time we prompt the model with one of the course units. - `stories`: we generated stories to add some commonsense and day-to-day knowledge aspect to the dataset. For this split we use samples from [UltraChat](https://huggingface.co/datasets/stingning/ultrachat) -only questions about the world [subset](https://huggingface.co/datasets/loubnabnl/ultrachat_questions_about_world)- and [OpenHermes2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5). These are synthetic instruction-tuning datasets that are already curated and cover a wide range of topics. - `wikihow`: in this split, we asked the model to generate WikiHow articles from WikiHow titles that we scraped, the list is avilable [here](https://github.com/huggingface/cosmopedia/blob/main/prompts/wikihow/wikihowcom-20231012-titles.txt). Note that you can find more WikiHow articles in the other splits by looking for it in the `format` column. - `openstax`: we scraped course outlines with unit introductions from [OpenStax](https://openstax.org/), a resource suggested by [AFAIK](https://afaik.io/) team. - `khanacademy`: we scraped the outlines for the courses on [KhanAcademy](https://www.khanacademy.org), and asked the model to genrate a textbook for each. - `automathtext`: to improve the science knowledge of the model, we use samples from [AutoMathText](https://huggingface.co/datasets/math-ai/AutoMathText/) dataset as seed samples. The dataset covers more than just math. See this clustering [plot](https://huggingface.co/datasets/HuggingFaceTB/miscellaneous/blob/main/AMT_plots/topics_distpng.png) we made. ### Dataset features The dataset has the following features: - prompt: the prompt we used to generate the content with Mixtral-8x7B-Instruct-v0.1. - text: the synthetic generated content. - seed_data: the prompts include some text fromanother dataset/an external source, `seed_data` is the name of that dataset (e.g web, Stanford courses...) - token_length: the number of tokens in `text`, computed using [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1)'s tokenizer - format: the style of `text`, this can for example be a textbook, a blogpost, a story.. It can also be inferred from the prompt. - audience: the target audience defined in the prompt # Dataset creation The "Dataset splits" section already provides an overview of the data creation pipeline. In this section, we will explain the topic clustering method for web samples and our iterative process for refining the prompts, in addition to decontamination. ### Topic clustering Our goal was to generate a vast quantity of synthetic data covering a wide range of topics (essentially, anything useful found on the web) in a cleaner format like textbooks. A natural strategy was to begin with web samples, using them as seeds for the generation. This approach, employed by Li et al. in [Phi-1.5](https://huggingface.co/papers/2309.05463), appears to be the most scalable method for synthetic data generation, given the availability of web datasets with trillions of tokens. The prompted model will use an extract from these seed samples as a reference for generation, so the topic might matter more than the actual content of the file. To filter out less relevant topics and to provide the model with context for generating content, we first clustered millions of files from a web dataset. Then we prompted Mixtral 8x7B with extracts from 10 random samples in each cluster and asked it to find the topic they have in common and to provide an educational score for that topic. The dataset with clusters and topics is available in this [demo](https://huggingface.co/spaces/HuggingFaceTB/inspect_web_clusters), the code is available in [text-clustering]( https://github.com/huggingface/text-clustering ) and a [demo](https://huggingface.co/spaces/HuggingFaceTB/inspect_web_clusters) for inspection. The educational score seems to work for "very uneducational" topics like adult content and "highly educational" topics like College Mathematics, but isn't very relevant in-between. So we manually inspect the 145 clusters we find, and discard 35 of them. The final list of topics is available [here](https://github.com/huggingface/cosmopedia/blob/dd5cd1f7fcfae255c9cfbe704ba2187965523457/prompts/web_samples/filter_and_classify_clusters.py#L8). We don't do any further filtering inside the clusters but we include the topic of the sample in the prompt 100% of the time for `web_samples_v1`, but only 50% of the time in `web_samples_v2`, where we tried to refine the prompts, in case the topic isn't accurate or the topic list isn't comprehensive. Below are the clusters found in Cosmopedia: <center> <img src="https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/jMKGaE_UnEfH3j8iZYXVN.png" alt="Cosmopedia clusters" width="1200" height="750"> <p><em>Cosmopedia clusters.</em></p> </center> ### Diversity We find that when using the same seed sample multiple times, changing the generation style and/or the audience and their target format results in different generations, covering the same topic from different angles. For example when asking the model for a children's textbook, we needed to remind it that it can't use complex concepts and that the tone should be adapted to children. The same goes when asking for textbooks for college students vs for researchers, we had to emphasize the level of depth we wanted for each, and how acadmeic the textbooks should be. By carefully iterating on the prompts using [HuggingChat](https://huggingface.co/chat/) and then generating few hundreds samples, we managed to reduce the redundancy. For example, we noticed that the model always started the stories with "Once upon a time" and the forums posts with "A few years back", asking it to explicitly avoid these sentences when starting the generation results in more diverse beginnings (don't worry "Once upon a time" still appears in stories!). Same goes for blogposts and textbooks where the introductory sentences were initially repetitive. Running MinHash deduplication on the splits detects less than 1% of the files as duplicates. ### Decontamination Given how we generate synthetic content, there is a possibility that the seed samples or the model's training data could have benchmarks contamination. Therefore, we run a decontamination piepline to make sure we don't have any samples from the test benchmarks in our dataset. We use a 10-gram overlap to retrieve potentially contaminated samples, similarly to [Phi-1](https://huggingface.co/papers/2306.11644). After retrieving the candidates, we run a diff between the dataset sample and the benchmark sample using `difflib.SequenceMatcher` and discard the sample if `len(matched_substrings)/len(benchmark_sample) > 0.5`. We run decontamination against all the benchmarks we evaluated the Cosmo-1B model on: MMLU, HellaSwag, PIQA, SIQA, Winogrande, OpenBookQA, ARC-easy, ARC-challenge. We report the number of contaminated samples removed from each dataset split, as well as the number of unique benchmark samples that they correspond to (in brackets): \| Dataset group \| ARC Easy \| ARC Challenge \| BoolQ \| HellaSwag \| MMLU \| OpenBookQA \| PIQA \| WinoGrande \| \|-----------------------------------------------\|----------\|---------------\|----------------\|-----------\|------\|------------\|------\|------------\| \| web_samples_v1 + web_samples_v2 + stanford + openstax \| 30 (13) \| 19 (3) \| 386 (41) \| 6 (5) \| 1 (1) \| 0 (0) \| 5 (3) \| 0 (0) \| \| auto_math_text + khanacademy \| 4 (4) \| 13 (2) \| 34 (7) \| 1 (1) \| 0 (0) \| 0 (0) \| 0 (0) \| 0 (0) \| \| stories \| 33 (20) \| 20 (12) \| 27 (21) \| 3 (3) \| 1 (1) \| 2 (2) \| 6 (4) \| 3 (2) \| ## Code The code for topic clustering of the web samples, building the prompts, content generation and data deduplication & decontamination can be found in the [Cosmopedia GitHub repository](https://github.com/huggingface/cosmopedia). ## Citation ``` @software{benallal2024cosmopedia, author = {Ben Allal, Loubna and Lozhkov, Anton and Penedo, Guilherme and Wolf, Thomas and von Werra, Leandro}, title = {Cosmopedia}, month = February, year = 2024, url = {https://huggingface.co/datasets/HuggingFaceTB/cosmopedia} } ```
facebook/multilingual_librispeech	facebook	2024-08-12T16:50:57Z	6,300	131	[ "task_categories:automatic-speech-recognition", "task_categories:text-to-speech", "task_categories:text-to-audio", "annotations_creators:expert-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "source_datasets:original", "language:de", "language:nl", "language:fr", "language:it", "language:es", "language:pt", "language:pl", "language:en", "license:cc-by-4.0", "size_categories:1M<n<10M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2012.03411", "region:us" ]	[ "automatic-speech-recognition", "text-to-speech", "text-to-audio" ]	2022-03-02T23:29:22Z	null	--- annotations_creators: - expert-generated language_creators: - crowdsourced - expert-generated language: - de - nl - fr - it - es - pt - pl - en license: - cc-by-4.0 multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - automatic-speech-recognition - text-to-speech - text-to-audio paperswithcode_id: multilingual-librispeech pretty_name: MultiLingual LibriSpeech dataset_info: - config_name: dutch features: - name: audio dtype: audio - name: original_path dtype: string - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: transcript dtype: string - name: audio_duration dtype: float64 - name: speaker_id dtype: string - name: chapter_id dtype: string - name: file dtype: string - name: id dtype: string splits: - name: dev num_bytes: 199959986 num_examples: 3095 - name: test num_bytes: 199298575 num_examples: 3075 - name: train num_bytes: 23931679031 num_examples: 374287 - name: 9_hours num_bytes: 139884664.668 num_examples: 2153 - name: 1_hours num_bytes: 15462181 num_examples: 234 download_size: 24376256629 dataset_size: 24486284437.668 - config_name: french features: - name: audio dtype: audio - name: original_path dtype: string - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: transcript dtype: string - name: audio_duration dtype: float64 - name: speaker_id dtype: string - name: chapter_id dtype: string - name: file dtype: string - name: id dtype: string splits: - name: dev num_bytes: 157923970.696 num_examples: 2416 - name: test num_bytes: 158352158.582 num_examples: 2426 - name: train num_bytes: 16984935842.04 num_examples: 258213 - name: 9_hours num_bytes: 142796680.609 num_examples: 2167 - name: 1_hours num_bytes: 15675831 num_examples: 241 download_size: 17381581776 dataset_size: 17459684482.927002 - config_name: german features: - name: audio dtype: audio - name: original_path dtype: string - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: transcript dtype: string - name: audio_duration dtype: float64 - name: speaker_id dtype: string - name: chapter_id dtype: string - name: file dtype: string - name: id dtype: string splits: - name: dev num_bytes: 224293581.302 num_examples: 3469 - name: test num_bytes: 225756069.096 num_examples: 3394 - name: train num_bytes: 31050881388 num_examples: 469942 - name: 9_hours num_bytes: 142777983.118 num_examples: 2194 - name: 1_hours num_bytes: 15714704 num_examples: 241 download_size: 31526161821 dataset_size: 31659423725.516 - config_name: italian features: - name: audio dtype: audio - name: original_path dtype: string - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: transcript dtype: string - name: audio_duration dtype: float64 - name: speaker_id dtype: string - name: chapter_id dtype: string - name: file dtype: string - name: id dtype: string splits: - name: dev num_bytes: 81607596.048 num_examples: 1248 - name: test num_bytes: 83216752.046 num_examples: 1262 - name: train num_bytes: 3896742625 num_examples: 59623 - name: 9_hours num_bytes: 141671904.428 num_examples: 2173 - name: 1_hours num_bytes: 15560398 num_examples: 240 download_size: 4200633596 dataset_size: 4218799275.522 - config_name: polish features: - name: audio dtype: audio - name: original_path dtype: string - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: transcript dtype: string - name: audio_duration dtype: float64 - name: speaker_id dtype: string - name: chapter_id dtype: string - name: file dtype: string - name: id dtype: string splits: - name: dev num_bytes: 32746725 num_examples: 512 - name: test num_bytes: 33735044 num_examples: 520 - name: train num_bytes: 1638889846 num_examples: 25043 - name: 9_hours num_bytes: 142005461 num_examples: 2173 - name: 1_hours num_bytes: 15681216 num_examples: 238 download_size: 1855342312 dataset_size: 1863058292 - config_name: portuguese features: - name: audio dtype: audio - name: original_path dtype: string - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: transcript dtype: string - name: audio_duration dtype: float64 - name: speaker_id dtype: string - name: chapter_id dtype: string - name: file dtype: string - name: id dtype: string splits: - name: dev num_bytes: 57533473 num_examples: 826 - name: test num_bytes: 59141979 num_examples: 871 - name: train num_bytes: 2518553713.946 num_examples: 37533 - name: 9_hours num_bytes: 141641902.42 num_examples: 2116 - name: 1_hours num_bytes: 15697139 num_examples: 236 download_size: 2780836500 dataset_size: 2792568207.366 - config_name: spanish features: - name: audio dtype: audio - name: original_path dtype: string - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: transcript dtype: string - name: audio_duration dtype: float64 - name: speaker_id dtype: string - name: chapter_id dtype: string - name: file dtype: string - name: id dtype: string splits: - name: dev num_bytes: 157804903.144 num_examples: 2408 - name: test num_bytes: 158526899.32 num_examples: 2385 - name: train num_bytes: 14562584188 num_examples: 220701 - name: 9_hours num_bytes: 142473624.48 num_examples: 2110 - name: 1_hours num_bytes: 15702048 num_examples: 233 download_size: 14971394533 dataset_size: 15037091662.944 configs: - config_name: dutch data_files: - split: dev path: dutch/dev-* - split: test path: dutch/test-* - split: train path: dutch/train-* - split: 9_hours path: dutch/9_hours-* - split: 1_hours path: dutch/1_hours-* - config_name: french data_files: - split: dev path: french/dev-* - split: test path: french/test-* - split: train path: french/train-* - split: 9_hours path: french/9_hours-* - split: 1_hours path: french/1_hours-* - config_name: german data_files: - split: dev path: german/dev-* - split: test path: german/test-* - split: train path: german/train-* - split: 9_hours path: german/9_hours-* - split: 1_hours path: german/1_hours-* - config_name: italian data_files: - split: dev path: italian/dev-* - split: test path: italian/test-* - split: train path: italian/train-* - split: 9_hours path: italian/9_hours-* - split: 1_hours path: italian/1_hours-* - config_name: polish data_files: - split: dev path: polish/dev-* - split: test path: polish/test-* - split: train path: polish/train-* - split: 9_hours path: polish/9_hours-* - split: 1_hours path: polish/1_hours-* - config_name: portuguese data_files: - split: dev path: portuguese/dev-* - split: test path: portuguese/test-* - split: train path: portuguese/train-* - split: 9_hours path: portuguese/9_hours-* - split: 1_hours path: portuguese/1_hours-* - config_name: spanish data_files: - split: dev path: spanish/dev-* - split: test path: spanish/test-* - split: train path: spanish/train-* - split: 9_hours path: spanish/9_hours-* - split: 1_hours path: spanish/1_hours-* --- # Dataset Card for MultiLingual LibriSpeech ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [MultiLingual LibriSpeech ASR corpus](http://www.openslr.org/94) - Repository: [Needs More Information] - Paper: [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://arxiv.org/abs/2012.03411) - Leaderboard: [🤗 Autoevaluate Leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=facebook%2Fmultilingual_librispeech&only_verified=0&task=automatic-speech-recognition&config=-unspecified-&split=-unspecified-&metric=wer) ### Dataset Summary This is a streamable version of the Multilingual LibriSpeech (MLS) dataset. The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94) to make it easier to stream. MLS dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish. It includes about 44.5K hours of English and a total of about 6K hours for other languages. ### Supported Tasks and Leaderboards - `automatic-speech-recognition`, `speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://paperswithcode.com/dataset/multilingual-librispeech and ranks models based on their WER. - `text-to-speech`, `text-to-audio`: The dataset can also be used to train a model for Text-To-Speech (TTS). ### Languages The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish ### How to use The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. For example, to download the German config, simply specify the corresponding language config name (i.e., "german" for German): ```python from datasets import load_dataset mls = load_dataset("facebook/multilingual_librispeech", "german", split="train") ``` Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk. ```python from datasets import load_dataset mls = load_dataset("facebook/multilingual_librispeech", "german", split="train", streaming=True) print(next(iter(mls))) ``` Bonus: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed). Local: ```python from datasets import load_dataset from torch.utils.data.sampler import BatchSampler, RandomSampler mls = load_dataset("facebook/multilingual_librispeech", "german", split="train") batch_sampler = BatchSampler(RandomSampler(mls), batch_size=32, drop_last=False) dataloader = DataLoader(mls, batch_sampler=batch_sampler) ``` Streaming: ```python from datasets import load_dataset from torch.utils.data import DataLoader mls = load_dataset("facebook/multilingual_librispeech", "german", split="train", streaming=True) dataloader = DataLoader(mls, batch_size=32) ``` To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets). ### Example scripts Train your own CTC or Seq2Seq Automatic Speech Recognition models on MultiLingual Librispeech with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition). ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided. ``` {'file': '10900_6473_000030.flac', 'audio': {'path': '10900_6473_000030.flac', 'array': array([-1.52587891e-04, 6.10351562e-05, 0.00000000e+00, ..., 4.27246094e-04, 5.49316406e-04, 4.57763672e-04]), 'sampling_rate': 16000}, 'text': 'więc czego chcecie odemnie spytałem wysłuchawszy tego zadziwiającego opowiadania broń nas stary człowieku broń zakrzyknęli równocześnie obaj posłowie\n', 'speaker_id': 10900, 'chapter_id': 6473, 'id': '10900_6473_000030'} ``` ### Data Fields - file: A filename .flac format. - audio: A dictionary containing the audio filename, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, i.e. `dataset[0]["audio"]` should always be preferred over `dataset["audio"][0]`. - text: the transcription of the audio file. - id: unique id of the data sample. - speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples. - chapter_id: id of the audiobook chapter which includes the transcription. ### Data Splits \| Number of samples \| Train \| Train.9h \| Train.1h \| Dev \| Test \| \| ----- \| ------ \| ----- \| ---- \| ---- \| ---- \| \| german \| 469942 \| 2194 \| 241 \| 3469 \| 3394 \| \| dutch \| 374287 \| 2153 \| 234 \| 3095 \| 3075 \| \| french \| 258213 \| 2167 \| 241 \| 2416 \| 2426 \| \| spanish \| 220701 \| 2110 \| 233 \| 2408 \| 2385 \| \| italian \| 59623 \| 2173 \| 240 \| 1248 \| 1262 \| \| portuguese \| 37533 \| 2116 \| 236 \| 826 \| 871 \| \| polish \| 25043 \| 2173 \| 238 \| 512 \| 520 \| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information Public Domain, Creative Commons Attribution 4.0 International Public License ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode)) ### Citation Information ``` @article{Pratap2020MLSAL, title={MLS: A Large-Scale Multilingual Dataset for Speech Research}, author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert}, journal={ArXiv}, year={2020}, volume={abs/2012.03411} } ``` ### Data Statistics \| Duration (h) \| Train \| Dev \| Test \| \|--------------\|-----------\|-------\|-------\| \| English \| 44,659.74 \| 15.75 \| 15.55 \| \| German \| 1,966.51 \| 14.28 \| 14.29 \| \| Dutch \| 1,554.24 \| 12.76 \| 12.76 \| \| French \| 1,076.58 \| 10.07 \| 10.07 \| \| Spanish \| 917.68 \| 9.99 \| 10 \| \| Italian \| 247.38 \| 5.18 \| 5.27 \| \| Portuguese \| 160.96 \| 3.64 \| 3.74 \| \| Polish \| 103.65 \| 2.08 \| 2.14 \| \| # Speakers \| Train \| \| Dev \| \| Test \| \| \|------------\|-------\|------\|-----\|----\|------\|----\| \| Gender \| M \| F \| M \| F \| M \| F \| \| English \| 2742 \| 2748 \| 21 \| 21 \| 21 \| 21 \| \| German \| 81 \| 95 \| 15 \| 15 \| 15 \| 15 \| \| Dutch \| 9 \| 31 \| 3 \| 3 \| 3 \| 3 \| \| French \| 62 \| 80 \| 9 \| 9 \| 9 \| 9 \| \| Spanish \| 36 \| 50 \| 10 \| 10 \| 10 \| 10 \| \| Italian \| 22 \| 43 \| 5 \| 5 \| 5 \| 5 \| \| Portuguese \| 26 \| 16 \| 5 \| 5 \| 5 \| 5 \| \| Polish \| 6 \| 5 \| 2 \| 2 \| 2 \| 2 \| \| # Hours / Gender \| Dev \| \| Test \| \| \|------------------\|------\|------\|------\|------\| \| Gender \| M \| F \| M \| F \| \| English \| 7.76 \| 7.99 \| 7.62 \| 7.93 \| \| German \| 7.06 \| 7.22 \| 7 \| 7.29 \| \| Dutch \| 6.44 \| 6.32 \| 6.72 \| 6.04 \| \| French \| 5.13 \| 4.94 \| 5.04 \| 5.02 \| \| Spanish \| 4.91 \| 5.08 \| 4.78 \| 5.23 \| \| Italian \| 2.5 \| 2.68 \| 2.38 \| 2.9 \| \| Portuguese \| 1.84 \| 1.81 \| 1.83 \| 1.9 \| \| Polish \| 1.12 \| 0.95 \| 1.09 \| 1.05 \| ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) and [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.
turkish-nlp-suite/InstrucTurca	turkish-nlp-suite	2024-08-12T10:13:04Z	254	25	[ "task_categories:text-generation", "task_categories:text2text-generation", "task_categories:question-answering", "language:tr", "license:cc-by-sa-4.0", "size_categories:1M<n<10M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[ "text-generation", "text2text-generation", "question-answering", "conversational" ]	2024-07-28T10:53:18Z	2	--- name: InstrucTurca tagline: Diverse Instructional Content Dataset for Turkish description: > InstrucTurca is a rich Turkish instruction tuning dataset from various fields. It includes tasks, code, poems, math, essays, medical texts, and more. This diversity makes it ideal for various NLP tasks such as summarization, question answering, generation, translation, and classification. However the main usage is intended to instruction train Turkish LLMs. dataset_version: 1.0.0 authors: - name: Duygu Altinok email: [email protected] license: cc-by-sa-4.0 task_categories: - text-generation - text2text-generation - question-answering - conversational language: - tr pretty_name: InstructTurca size_categories: - 1M<n<10M --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/635c4736177df3f16e99db63/ygAnIjc7zWWp72_q0t985.png) InstrucTurca v1.0.0 is a diverse synthetic instruction tuning dataset crafted for instruction-tuning Turkish LLMs. The data is compiled data various English datasets and sources, such as code instructions, poems, summarized texts, medical texts, and more. ## Dataset content * BI55/MedText * checkai/instruction-poems * garage-bAInd/Open-Platypus * Locutusque/ColumnedChatCombined * nampdn-ai/tiny-codes * Open-Orca/OpenOrca * pubmed_qa * TIGER-Lab/MathInstruct * TokenBender/code_instructions_120k_alpaca_style Each dataset instance is a pair of text representing user input and bot output. ## Dataset creation All the data is translated by Snowflake Artic Instruct. We used a very basic prompt and asked Arctic to translate both the input and output text. After that, we made a pass over the dataset to eliminate hallucination looking translations. More info about the creation process can be found at our [Medium article](https://medium.com/google-developer-experts/instructurca-an-open-source-instruction-tuning-dataset-for-turkish-18c37b0e99b9). ## Licence The dataset is suitable for commercial use, thanks to [Snowflake Arctic's Apache 2.0 licence](https://github.com/Snowflake-Labs/snowflake-arctic/blob/main/LICENSE). ## Acknowledgements: Huge thanks go to original dataset creators. Even a bigger thanks go to Snowflake team to creating a truly open source LLM! Without any of them, this dataset wouldn't be possible. ## Citation If you find this work helpful, feel free to give us a cite. ``` @misc{instructurca, author={Duygu Altinok}, title={InstrucTurca: A Diverse Instructional Content Dataset for Turkish}, year={2024} } ```
deepset/covid_qa_deepset	deepset	2024-08-08T06:11:28Z	294	8	[ "task_categories:question-answering", "task_ids:closed-domain-qa", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[ "question-answering" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - expert-generated language_creators: - found language: - en license: - apache-2.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - closed-domain-qa - extractive-qa pretty_name: COVID-QA dataset_info: config_name: covid_qa_deepset features: - name: document_id dtype: int32 - name: context dtype: string - name: question dtype: string - name: is_impossible dtype: bool - name: id dtype: int32 - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: train num_bytes: 65151242 num_examples: 2019 download_size: 2274275 dataset_size: 65151242 configs: - config_name: covid_qa_deepset data_files: - split: train path: covid_qa_deepset/train-* default: true --- # Dataset Card for COVID-QA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: https://github.com/deepset-ai/COVID-QA - Paper: https://openreview.net/forum?id=JENSKEEzsoU - Point of Contact: [deepset AI](https://github.com/deepset-ai) ### Dataset Summary COVID-QA is a Question Answering dataset consisting of 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles related to COVID-19. A total of 147 scientific articles from the CORD-19 dataset were annotated by 15 experts. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances What do the instances that comprise the dataset represent? Each represents a question, a context (document passage from the CORD19 dataset) and an answer. How many instances are there in total? 2019 instances What data does each instance consist of? Each instance is a question, a set of answers, and an id associated with each answer. [More Information Needed] ### Data Fields The data was annotated in SQuAD style fashion, where each row contains: * question: Query question * context: Context text to obtain the answer from * document_id The document ID of the context text * answer: Dictionary containing the answer string and the start index ### Data Splits data/COVID-QA.json: 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles related to COVID-19. [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The inital data collected comes from 147 scientific articles from the CORD-19 dataset. Question and answers were then annotated afterwards. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process While annotators were volunteers, they were required to have at least a Master’s degree in biomedical sciences. The annotation team was led by a medical doctor (G.A.R.) who vetted the volunteer’s credentials and manually verified each question/answer pair produced. We used an existing, web-based annotation tool that had been created by deepset and is available at their Neural Search framework [haystack](https://github.com/deepset-ai/haystack). #### Who are the annotators? The annotators are 15 volunteer biomedical experts on scientific articles related to COVID-19. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset The dataset aims to help build question answering models serving clinical and scientific researchers, public health authorities, and frontline workers. These QA systems can help them find answers and patterns in research papers by locating relevant answers to common questions from scientific articles. ### Discussion of Biases [More Information Needed] ### Other Known Limitations ## Additional Information The listed authors in the homepage are maintaining/supporting the dataset. ### Dataset Curators [More Information Needed] ### Licensing Information The Proto_qa dataset is licensed under the [Apache License 2.0](https://github.com/deepset-ai/COVID-QA/blob/master/LICENSE) ### Citation Information ``` @inproceedings{moller2020covid, title={COVID-QA: A Question Answering Dataset for COVID-19}, author={M{\"o}ller, Timo and Reina, Anthony and Jayakumar, Raghavan and Pietsch, Malte}, booktitle={Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020}, year={2020} } ``` ### Contributions Thanks to [@olinguyen](https://github.com/olinguyen) for adding this dataset.
sonos-nlu-benchmark/snips_built_in_intents	sonos-nlu-benchmark	2024-08-08T06:03:47Z	515	11	[ "task_categories:text-classification", "task_ids:intent-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc0-1.0", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1805.10190", "region:us" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - cc0-1.0 multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - text-classification task_ids: - intent-classification paperswithcode_id: snips pretty_name: SNIPS Natural Language Understanding benchmark dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': ComparePlaces '1': RequestRide '2': GetWeather '3': SearchPlace '4': GetPlaceDetails '5': ShareCurrentLocation '6': GetTrafficInformation '7': BookRestaurant '8': GetDirections '9': ShareETA splits: - name: train num_bytes: 19427 num_examples: 328 download_size: 11158 dataset_size: 19427 configs: - config_name: default data_files: - split: train path: data/train-* train-eval-index: - config: default task: text-classification task_id: multi_class_classification train_split: train col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for Snips Built In Intents ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/sonos/nlu-benchmark/tree/master/2016-12-built-in-intents - Repository: https://github.com/sonos/nlu-benchmark/tree/master/2016-12-built-in-intents - Paper: https://arxiv.org/abs/1805.10190 - Point of Contact: The Snips team has joined Sonos in November 2019. These open datasets remain available and their access is now managed by the Sonos Voice Experience Team. Please email [email protected] with any question. ### Dataset Summary Snips' built in intents dataset was initially used to compare different voice assistants and released as a public dataset hosted at https://github.com/sonos/nlu-benchmark in folder 2016-12-built-in-intents. The dataset contains 328 utterances over 10 intent classes. A related Medium post is https://medium.com/snips-ai/benchmarking-natural-language-understanding-systems-d35be6ce568d. ### Supported Tasks and Leaderboards There are no related shared tasks that we are aware of. ### Languages English ## Dataset Structure ### Data Instances The dataset contains 328 utterances over 10 intent classes. Each sample looks like: `{'label': 8, 'text': 'Transit directions to Barcelona Pizza.'}` ### Data Fields - `text`: The text utterance expressing some user intent. - `label`: The intent label of the piece of text utterance. ### Data Splits The source data is not split. ## Dataset Creation ### Curation Rationale The dataset was originally created to compare the performance of a number of voice assistants. However, the labelled utterances are useful for developing and benchmarking text chatbots as well. ### Source Data #### Initial Data Collection and Normalization It is not clear how the data was collected. From the Medium post: `The benchmark relies on a set of 328 queries built by the business team at Snips, and kept secret from data scientists and engineers throughout the development of the solution.` #### Who are the source language producers? Originally prepared by snips.ai. The Snips team has since joined Sonos in November 2019. These open datasets remain available and their access is now managed by the Sonos Voice Experience Team. Please email [email protected] with any question. ### Annotations #### Annotation process It is not clear how the data was collected. From the Medium post: `The benchmark relies on a set of 328 queries built by the business team at Snips, and kept secret from data scientists and engineers throughout the development of the solution.` #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Originally prepared by snips.ai. The Snips team has since joined Sonos in November 2019. These open datasets remain available and their access is now managed by the Sonos Voice Experience Team. Please email [email protected] with any question. ### Licensing Information The source data is licensed under Creative Commons Zero v1.0 Universal. ### Citation Information Any publication based on these datasets must include a full citation to the following paper in which the results were published by the Snips Team: Coucke A. et al., "Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces." CoRR 2018, https://arxiv.org/abs/1805.10190 ### Contributions Thanks to [@bduvenhage](https://github.com/bduvenhage) for adding this dataset.
KorQuAD/squad_kor_v1	KorQuAD	2024-08-08T06:03:11Z	1,166	28	[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:ko", "license:cc-by-nd-4.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1909.07005", "region:us" ]	[ "question-answering" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - crowdsourced language_creators: - found language: - ko license: - cc-by-nd-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: korquad pretty_name: The Korean Question Answering Dataset dataset_info: config_name: squad_kor_v1 features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: train num_bytes: 83380141 num_examples: 60407 - name: validation num_bytes: 8261701 num_examples: 5774 download_size: 12713412 dataset_size: 91641842 configs: - config_name: squad_kor_v1 data_files: - split: train path: squad_kor_v1/train-* - split: validation path: squad_kor_v1/validation-* default: true --- # Dataset Card for KorQuAD v1.0 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://korquad.github.io/KorQuad%201.0/ - Repository: https://github.com/korquad/korquad.github.io/tree/master/dataset - Paper: https://arxiv.org/abs/1909.07005 ### Dataset Summary KorQuAD 1.0 is a large-scale question-and-answer dataset constructed for Korean machine reading comprehension, and investigate the dataset to understand the distribution of answers and the types of reasoning required to answer the question. This dataset benchmarks the data generating process of SQuAD v1.0 to meet the standard. ### Supported Tasks and Leaderboards `question-answering` ### Languages Korean ## Dataset Structure Follows the standars SQuAD format. ### Data Instances An example from the data set looks as follows: ``` {'answers': {'answer_start': [54], 'text': ['교향곡']}, 'context': '1839년 바그너는 괴테의 파우스트을 처음 읽고 그 내용에 마음이 끌려 이를 소재로 해서 하나의 교향곡을 쓰려는 뜻을 갖는다. 이 시기 바그너는 1838년에 빛 독촉으로 산전수전을 다 걲은 상황이라 좌절과 실망에 가득했으며 메피스토펠레스를 만나는 파우스트의 심경에 공감했다고 한다. 또한 파리에서 아브네크의 지휘로 파리 음악원 관현악단이 연주하는 베토벤의 교향곡 9번을 듣고 깊은 감명을 받았는데, 이것이 이듬해 1월에 파우스트의 서곡으로 쓰여진 이 작품에 조금이라도 영향을 끼쳤으리라는 것은 의심할 여지가 없다. 여기의 라단조 조성의 경우에도 그의 전기에 적혀 있는 것처럼 단순한 정신적 피로나 실의가 반영된 것이 아니라 베토벤의 합창교향곡 조성의 영향을 받은 것을 볼 수 있다. 그렇게 교향곡 작곡을 1839년부터 40년에 걸쳐 파리에서 착수했으나 1악장을 쓴 뒤에 중단했다. 또한 작품의 완성과 동시에 그는 이 서곡(1악장)을 파리 음악원의 연주회에서 연주할 파트보까지 준비하였으나, 실제로는 이루어지지는 않았다. 결국 초연은 4년 반이 지난 후에 드레스덴에서 연주되었고 재연도 이루어졌지만, 이후에 그대로 방치되고 말았다. 그 사이에 그는 리엔치와 방황하는 네덜란드인을 완성하고 탄호이저에도 착수하는 등 분주한 시간을 보냈는데, 그런 바쁜 생활이 이 곡을 잊게 한 것이 아닌가 하는 의견도 있다.', 'id': '6566495-0-0', 'question': '바그너는 괴테의 파우스트를 읽고 무엇을 쓰고자 했는가?', 'title': '파우스트_서곡'} ``` ### Data Fields ``` {'id': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'context': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'answers': Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None)} ``` ### Data Splits - Train: 60407 - Validation: 5774 ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data Wikipedia #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [CC BY-ND 2.0 KR](https://creativecommons.org/licenses/by-nd/2.0/kr/deed.en) ### Citation Information ``` @article{lim2019korquad1, title={Korquad1. 0: Korean qa dataset for machine reading comprehension}, author={Lim, Seungyoung and Kim, Myungji and Lee, Jooyoul}, journal={arXiv preprint arXiv:1909.07005}, year={2019} ``` ### Contributions Thanks to [@cceyda](https://github.com/cceyda) for adding this dataset.
s3prl/superb	s3prl	2024-08-08T06:02:44Z	1,280	30	[ "task_categories:automatic-speech-recognition", "task_categories:audio-classification", "task_ids:keyword-spotting", "task_ids:speaker-identification", "task_ids:audio-intent-classification", "task_ids:audio-emotion-recognition", "annotations_creators:other", "language_creators:other", "multilinguality:monolingual", "source_datasets:original", "source_datasets:extended\|librispeech_asr", "source_datasets:extended\|other-librimix", "source_datasets:extended\|other-speech_commands", "language:en", "license:unknown", "arxiv:2105.01051", "region:us", "query-by-example-spoken-term-detection", "audio-slot-filling", "speaker-diarization", "automatic-speaker-verification" ]	[ "automatic-speech-recognition", "audio-classification" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - other language_creators: - other language: - en license: - unknown multilinguality: - monolingual size_categories: - unknown source_datasets: - original - extended\|librispeech_asr - extended\|other-librimix - extended\|other-speech_commands task_categories: - automatic-speech-recognition - audio-classification task_ids: - keyword-spotting - speaker-identification - audio-intent-classification - audio-emotion-recognition pretty_name: SUPERB tags: - query-by-example-spoken-term-detection - audio-slot-filling - speaker-diarization - automatic-speaker-verification dataset_info: - config_name: asr features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string splits: - name: train num_bytes: 11852430 num_examples: 28539 - name: validation num_bytes: 897213 num_examples: 2703 - name: test num_bytes: 871234 num_examples: 2620 download_size: 7071899769 dataset_size: 13620877 - config_name: sd features: - name: record_id dtype: string - name: file dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: speakers list: - name: speaker_id dtype: string - name: start dtype: int64 - name: end dtype: int64 splits: - name: train num_bytes: 4622013 num_examples: 13901 - name: dev num_bytes: 860472 num_examples: 3014 - name: test num_bytes: 847803 num_examples: 3002 download_size: 7190370211 dataset_size: 6330288 - config_name: ks features: - name: file dtype: string - name: label dtype: class_label: names: '0': 'yes' '1': 'no' '2': up '3': down '4': left '5': right '6': 'on' '7': 'off' '8': stop '9': go '10': _silence_ '11': _unknown_ splits: - name: train num_bytes: 8467781 num_examples: 51094 - name: validation num_bytes: 1126476 num_examples: 6798 - name: test num_bytes: 510619 num_examples: 3081 download_size: 1560367713 dataset_size: 10104876 - config_name: ic features: - name: file dtype: string - name: speaker_id dtype: string - name: text dtype: string - name: action dtype: class_label: names: '0': activate '1': bring '2': change language '3': deactivate '4': decrease '5': increase - name: object dtype: class_label: names: '0': Chinese '1': English '2': German '3': Korean '4': heat '5': juice '6': lamp '7': lights '8': music '9': newspaper '10': none '11': shoes '12': socks '13': volume - name: location dtype: class_label: names: '0': bedroom '1': kitchen '2': none '3': washroom splits: - name: train num_bytes: 7071466 num_examples: 23132 - name: validation num_bytes: 953622 num_examples: 3118 - name: test num_bytes: 1158347 num_examples: 3793 download_size: 1544093324 dataset_size: 9183435 - config_name: si features: - name: file dtype: string - name: label dtype: class_label: names: '0': id10001 '1': id10002 '2': id10003 '3': id10004 '4': id10005 '5': id10006 '6': id10007 '7': id10008 '8': id10009 '9': id10010 '10': id10011 '11': id10012 '12': id10013 '13': id10014 '14': id10015 '15': id10016 '16': id10017 '17': id10018 '18': id10019 '19': id10020 '20': id10021 '21': id10022 '22': id10023 '23': id10024 '24': id10025 '25': id10026 '26': id10027 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'1215': id11216 '1216': id11217 '1217': id11218 '1218': id11219 '1219': id11220 '1220': id11221 '1221': id11222 '1222': id11223 '1223': id11224 '1224': id11225 '1225': id11226 '1226': id11227 '1227': id11228 '1228': id11229 '1229': id11230 '1230': id11231 '1231': id11232 '1232': id11233 '1233': id11234 '1234': id11235 '1235': id11236 '1236': id11237 '1237': id11238 '1238': id11239 '1239': id11240 '1240': id11241 '1241': id11242 '1242': id11243 '1243': id11244 '1244': id11245 '1245': id11246 '1246': id11247 '1247': id11248 '1248': id11249 '1249': id11250 '1250': id11251 splits: - name: train num_bytes: 12729268 num_examples: 138361 - name: validation num_bytes: 635172 num_examples: 6904 - name: test num_bytes: 759096 num_examples: 8251 download_size: 0 dataset_size: 14123536 --- # Dataset Card for SUPERB ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [http://superbbenchmark.org](http://superbbenchmark.org) - Repository: [https://github.com/s3prl/s3prl](https://github.com/s3prl/s3prl) - Paper: [SUPERB: Speech processing Universal PERformance Benchmark](https://arxiv.org/abs/2105.01051) - Leaderboard: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [Lewis Tunstall](mailto:[email protected]) and [Albert Villanova](mailto:[email protected]) ### Dataset Summary SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data. ### Supported Tasks and Leaderboards The SUPERB leaderboard can be found here https://superbbenchmark.org/leaderboard and consists of the following tasks: #### pr Phoneme Recognition (PR) transcribes an utterance into the smallest content units. This task includes alignment modeling to avoid potentially inaccurate forced alignment. [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) train-clean-100/dev-clean/test-clean subsets are adopted in SUPERB for training/validation/testing. Phoneme transcriptions are obtained from the LibriSpeech official g2p-model-5 and the conversion script in Kaldi librispeech s5 recipe. The evaluation metric is phone error rate (PER). #### asr Automatic Speech Recognition (ASR) transcribes utterances into words. While PR analyzes the improvement in modeling phonetics, ASR reflects the significance of the improvement in a real-world scenario. [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) train-clean-100/devclean/test-clean subsets are used for training/validation/testing. The evaluation metric is word error rate (WER). #### ks Keyword Spotting (KS) detects preregistered keywords by classifying utterances into a predefined set of words. The task is usually performed on-device for the fast response time. Thus, accuracy, model size, and inference time are all crucial. SUPERB uses the widely used [Speech Commands dataset v1.0](https://www.tensorflow.org/datasets/catalog/speech_commands) for the task. The dataset consists of ten classes of keywords, a class for silence, and an unknown class to include the false positive. The evaluation metric is accuracy (ACC) ##### Example of usage: Use these auxillary functions to: - load the audio file into an audio data array - sample from long `_silence_` audio clips For other examples of handling long `_silence_` clips see the [S3PRL](https://github.com/s3prl/s3prl/blob/099ce807a6ffa6bf2482ceecfcaf83dea23da355/s3prl/downstream/speech_commands/dataset.py#L80) or [TFDS](https://github.com/tensorflow/datasets/blob/6b8cfdb7c3c0a04e731caaa8660ce948d0a67b1e/tensorflow_datasets/audio/speech_commands.py#L143) implementations. ```python def map_to_array(example): import soundfile as sf speech_array, sample_rate = sf.read(example["file"]) example["speech"] = speech_array example["sample_rate"] = sample_rate return example def sample_noise(example): # Use this function to extract random 1 sec slices of each _silence_ utterance, # e.g. inside `torch.utils.data.Dataset.__getitem__()` from random import randint if example["label"] == "_silence_": random_offset = randint(0, len(example["speech"]) - example["sample_rate"] - 1) example["speech"] = example["speech"][random_offset : random_offset + example["sample_rate"]] return example ``` #### qbe Query by Example Spoken Term Detection (QbE) detects a spoken term (query) in an audio database (documents) by binary discriminating a given pair of query and document into a match or not. The English subset in [QUESST 2014 challenge](https://github.com/s3prl/s3prl/tree/master/downstream#qbe-query-by-example-spoken-term-detection) is adopted since we focus on investigating English as the first step. The evaluation metric is maximum term weighted value (MTWV) which balances misses and false alarms. #### ic Intent Classification (IC) classifies utterances into predefined classes to determine the intent of speakers. SUPERB uses the [Fluent Speech Commands dataset](https://github.com/s3prl/s3prl/tree/master/downstream#ic-intent-classification---fluent-speech-commands), where each utterance is tagged with three intent labels: action, object, and location. The evaluation metric is accuracy (ACC). #### sf Slot Filling (SF) predicts a sequence of semantic slot-types from an utterance, like a slot-type FromLocation for a spoken word Taipei, which is known as a slot-value. Both slot-types and slot-values are essential for an SLU system to function. The evaluation metrics thus include slot-type F1 score and slotvalue CER. [Audio SNIPS](https://github.com/s3prl/s3prl/tree/master/downstream#sf-end-to-end-slot-filling) is adopted, which synthesized multi-speaker utterances for SNIPS. Following the standard split in SNIPS, US-accent speakers are further selected for training, and others are for validation/testing. #### si Speaker Identification (SI) classifies each utterance for its speaker identity as a multi-class classification, where speakers are in the same predefined set for both training and testing. The widely used [VoxCeleb1 dataset](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html) is adopted, and the evaluation metric is accuracy (ACC). #### asv Automatic Speaker Verification (ASV) verifies whether the speakers of a pair of utterances match as a binary classification, and speakers in the testing set may not appear in the training set. Thus, ASV is more challenging than SID. VoxCeleb1 is used without VoxCeleb2 training data and noise augmentation. The evaluation metric is equal error rate (EER). #### sd Speaker Diarization (SD) predicts who is speaking when for each timestamp, and multiple speakers can speak simultaneously. The model has to encode rich speaker characteristics for each frame and should be able to represent mixtures of signals. [LibriMix](https://github.com/s3prl/s3prl/tree/master/downstream#sd-speaker-diarization) is adopted where LibriSpeech train-clean-100/dev-clean/test-clean are used to generate mixtures for training/validation/testing. We focus on the two-speaker scenario as the first step. The time-coded speaker labels were generated using alignments from Kaldi LibriSpeech ASR model. The evaluation metric is diarization error rate (DER). ##### Example of usage Use these auxiliary functions to: - load the audio file into an audio data array - generate the label array ```python def load_audio_file(example, frame_shift=160): import soundfile as sf example["array"], example["sample_rate"] = sf.read( example["file"], start=example["start"] * frame_shift, stop=example["end"] * frame_shift ) return example def generate_label(example, frame_shift=160, num_speakers=2, rate=16000): import numpy as np start = example["start"] end = example["end"] frame_num = end - start speakers = sorted({speaker["speaker_id"] for speaker in example["speakers"]}) label = np.zeros((frame_num, num_speakers), dtype=np.int32) for speaker in example["speakers"]: speaker_index = speakers.index(speaker["speaker_id"]) start_frame = np.rint(speaker["start"] * rate / frame_shift).astype(int) end_frame = np.rint(speaker["end"] * rate / frame_shift).astype(int) rel_start = rel_end = None if start <= start_frame < end: rel_start = start_frame - start if start < end_frame <= end: rel_end = end_frame - start if rel_start is not None or rel_end is not None: label[rel_start:rel_end, speaker_index] = 1 example["label"] = label return example ``` #### er Emotion Recognition (ER) predicts an emotion class for each utterance. The most widely used ER dataset [IEMOCAP](https://github.com/s3prl/s3prl/tree/master/downstream#er-emotion-recognition) is adopted, and we follow the conventional evaluation protocol: we drop the unbalance emotion classes to leave the final four classes with a similar amount of data points and cross-validates on five folds of the standard splits. The evaluation metric is accuracy (ACC). ### Languages The language data in SUPERB is in English (BCP-47 `en`) ## Dataset Structure ### Data Instances #### pr [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### asr An example from each split looks like: ```python {'chapter_id': 1240, 'file': 'path/to/file.flac', 'audio': {'path': 'path/to/file.flac', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'id': '103-1240-0000', 'speaker_id': 103, 'text': 'CHAPTER ONE MISSUS RACHEL LYNDE IS SURPRISED MISSUS RACHEL LYNDE ' 'LIVED JUST WHERE THE AVONLEA MAIN ROAD DIPPED DOWN INTO A LITTLE ' 'HOLLOW FRINGED WITH ALDERS AND LADIES EARDROPS AND TRAVERSED BY A ' 'BROOK'} ``` #### ks An example from each split looks like: ```python { 'file': '/path/yes/af7a8296_nohash_1.wav', 'audio': {'path': '/path/yes/af7a8296_nohash_1.wav', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'label': 0 # 'yes' } ``` #### qbe [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### ic ```python { 'file': "/path/wavs/speakers/2BqVo8kVB2Skwgyb/063aa8f0-4479-11e9-a9a5-5dbec3b8816a.wav", 'audio': {'path': '/path/wavs/speakers/2BqVo8kVB2Skwgyb/063aa8f0-4479-11e9-a9a5-5dbec3b8816a.wav', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'speaker_id': '2BqVo8kVB2Skwgyb', 'text': 'Turn the bedroom lights off', 'action': 3, # 'deactivate' 'object': 7, # 'lights' 'location': 0 # 'bedroom' } ``` #### sf [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### si ```python { 'file': '/path/wav/id10003/na8-QEFmj44/00003.wav', 'audio': {'path': '/path/wav/id10003/na8-QEFmj44/00003.wav', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'label': 2 # 'id10003' } ``` #### asv [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### sd An example from each split looks like: ```python { 'record_id': '1578-6379-0038_6415-111615-0009', 'file': 'path/to/file.wav', 'audio': {'path': 'path/to/file.wav', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'start': 0, 'end': 1590, 'speakers': [ {'speaker_id': '1578', 'start': 28, 'end': 657}, {'speaker_id': '6415', 'start': 28, 'end': 1576} ] } ``` #### er [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Data Fields ####Note abouth the `audio` fields When accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, i.e. `dataset[0]["audio"]` should always be preferred over `dataset["audio"][0]`. #### pr [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### asr - `file` (`string`): Path to the WAV audio file. - `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. - `text` (`string`): The transcription of the audio file. - `speaker_id` (`integer`): A unique ID of the speaker. The same speaker id can be found for multiple data samples. - `chapter_id` (`integer`): ID of the audiobook chapter which includes the transcription. - `id` (`string`): A unique ID of the data sample. #### ks - `file` (`string`): Path to the WAV audio file. - `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. - `label` (`ClassLabel`): Label of the spoken command. Possible values: - `0: "yes", 1: "no", 2: "up", 3: "down", 4: "left", 5: "right", 6: "on", 7: "off", 8: "stop", 9: "go", 10: "_silence_", 11: "_unknown_"` #### qbe [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### ic - `file` (`string`): Path to the WAV audio file. - `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. - `speaker_id` (`string`): ID of the speaker. - `text` (`string`): Transcription of the spoken command. - `action` (`ClassLabel`): Label of the command's action. Possible values: - `0: "activate", 1: "bring", 2: "change language", 3: "deactivate", 4: "decrease", 5: "increase"` - `object` (`ClassLabel`): Label of the command's object. Possible values: - `0: "Chinese", 1: "English", 2: "German", 3: "Korean", 4: "heat", 5: "juice", 6: "lamp", 7: "lights", 8: "music", 9: "newspaper", 10: "none", 11: "shoes", 12: "socks", 13: "volume"` - `location` (`ClassLabel`): Label of the command's location. Possible values: - `0: "bedroom", 1: "kitchen", 2: "none", 3: "washroom"` #### sf [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### si - `file` (`string`): Path to the WAV audio file. - `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. - `label` (`ClassLabel`): Label (ID) of the speaker. Possible values: - `0: "id10001", 1: "id10002", 2: "id10003", ..., 1250: "id11251"` #### asv [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### sd The data fields in all splits are: - `record_id` (`string`): ID of the record. - `file` (`string`): Path to the WAV audio file. - `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. - `start` (`integer`): Start frame of the audio. - `end` (`integer`): End frame of the audio. - `speakers` (`list` of `dict`): List of speakers in the audio. Each item contains the fields: - `speaker_id` (`string`): ID of the speaker. - `start` (`integer`): Frame when the speaker starts speaking. - `end` (`integer`): Frame when the speaker stops speaking. #### er - `file` (`string`): Path to the WAV audio file. - `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. - `label` (`ClassLabel`): Label of the speech emotion. Possible values: - `0: "neu", 1: "hap", 2: "ang", 3: "sad"` ### Data Splits #### pr [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### asr \| \| train \| validation \| test \| \|-----\|------:\|-----------:\|-----:\| \| asr \| 28539 \| 2703 \| 2620 \| #### ks \| \| train \| validation \| test \| \|----\|------:\|-----------:\|-----:\| \| ks \| 51094 \| 6798 \| 3081 \| #### qbe [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### ic \| \| train \| validation \| test \| \|----\|------:\|-----------:\|-----:\| \| ic \| 23132 \| 3118 \| 3793 \| #### sf [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### si \| \| train \| validation \| test \| \|----\|-------:\|-----------:\|-----:\| \| si \| 138361 \| 6904 \| 8251 \| #### asv [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### sd The data is split into "train", "dev" and "test" sets, each containing the following number of examples: \| \| train \| dev \| test \| \|----\|------:\|-----:\|-----:\| \| sd \| 13901 \| 3014 \| 3002 \| #### er The data is split into 5 sets intended for 5-fold cross-validation: \| \| session1 \| session2 \| session3 \| session4 \| session5 \| \|----\|---------:\|---------:\|---------:\|---------:\|---------:\| \| er \| 1085 \| 1023 \| 1151 \| 1031 \| 1241 \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information #### pr and asr The license for Librispeech is the Creative Commons Attribution 4.0 International license ((CC-BY-4.0)[https://creativecommons.org/licenses/by/4.0/]). #### ks The license for Speech Commands is [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode) #### qbe The license for QUESST 2014 is not known. #### ic The license for Fluent Speech Commands dataset is the [Fluent Speech Commands Public License](https://fluent.ai/wp-content/uploads/2021/04/Fluent_Speech_Commands_Public_License.pdf) #### sf The license for Audio SNIPS dataset is not known. #### si and asv The license for VoxCeleb1 dataset is the Creative Commons Attribution 4.0 International license ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)). #### sd LibriMix is based on the LibriSpeech (see above) and Wham! noises datasets. The Wham! noises dataset is distributed under the Attribution-NonCommercial 4.0 International ([CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)) license. #### er The IEMOCAP license is ditributed under [its own license](https://sail.usc.edu/iemocap/Data_Release_Form_IEMOCAP.pdf). ### Citation Information ``` @article{DBLP:journals/corr/abs-2105-01051, author = {Shu{-}Wen Yang and Po{-}Han Chi and Yung{-}Sung Chuang and Cheng{-}I Jeff Lai and Kushal Lakhotia and Yist Y. Lin and Andy T. Liu and Jiatong Shi and Xuankai Chang and Guan{-}Ting Lin and Tzu{-}Hsien Huang and Wei{-}Cheng Tseng and Ko{-}tik Lee and Da{-}Rong Liu and Zili Huang and Shuyan Dong and Shang{-}Wen Li and Shinji Watanabe and Abdelrahman Mohamed and Hung{-}yi Lee}, title = {{SUPERB:} Speech processing Universal PERformance Benchmark}, journal = {CoRR}, volume = {abs/2105.01051}, year = {2021}, url = {https://arxiv.org/abs/2105.01051}, archivePrefix = {arXiv}, eprint = {2105.01051}, timestamp = {Thu, 01 Jul 2021 13:30:22 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2105-01051.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } Note that each SUPERB dataset has its own citation. Please see the source to see the correct citation for each contained dataset. ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova) and [@anton-l](https://github.com/anton-l) for adding this dataset.
fthbrmnby/turkish_product_reviews	fthbrmnby	2024-08-08T05:57:51Z	99	10	[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:tr", "license:unknown", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - found language_creators: - found language: - tr license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: Turkish Product Reviews dataset_info: features: - name: sentence dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 43369614 num_examples: 235165 download_size: 24354762 dataset_size: 43369614 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for Turkish Product Reviews ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: [turkish-text-data](https://github.com/fthbrmnby/turkish-text-data) - Point of Contact: [Fatih Barmanbay](https://github.com/fthbrmnby) ### Dataset Summary This Turkish Product Reviews Dataset contains 235.165 product reviews collected online. There are 220.284 positive, 14881 negative reviews. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset is based on Turkish. ## Dataset Structure ### Data Instances Example 1: sentence: beklentimin altında bir ürün kaliteli değil sentiment: 0 (negative) Example 2: sentence: fiyat ve performans olarak gayet iyi sentiment: 1 (positive) ### Data Fields - sentence(string) : Contatins turkish product review - sentiment(int) : 0 (negative) or 1 (positive) ### Data Splits It is not divided into Train set and Test set. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations The dataset does not contain any additional annotations. #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The dataset was created by [Fatih Barmanbay](https://github.com/fthbrmnby). ### Licensing Information The data is under the [CC-BY-SA-4.0 License](https://github.com/fthbrmnby/turkish-text-data/blob/master/LICENCE) ### Citation Information No citation available for this dataset. ### Contributions Thanks to [@basakbuluz](https://github.com/basakbuluz) for adding this dataset.
ccdv/arxiv-classification	ccdv	2024-08-08T05:50:57Z	652	23	[ "task_categories:text-classification", "task_ids:multi-class-classification", "task_ids:topic-classification", "language:en", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "long context" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	1	--- language: en size_categories: 10K<n<100K task_categories: - text-classification task_ids: - multi-class-classification - topic-classification tags: - long context dataset_info: - config_name: default features: - name: text dtype: string - name: label dtype: class_label: names: '0': math.AC '1': cs.CV '2': cs.AI '3': cs.SY '4': math.GR '5': cs.CE '6': cs.PL '7': cs.IT '8': cs.DS '9': cs.NE '10': math.ST splits: - name: train num_bytes: 1650691690 num_examples: 28388 - name: validation num_bytes: 144469423 num_examples: 2500 - name: test num_bytes: 141183324 num_examples: 2500 download_size: 1011043995 dataset_size: 1936344437 - config_name: no_ref features: - name: text dtype: string - name: label dtype: class_label: names: '0': math.AC '1': cs.CV '2': cs.AI '3': cs.SY '4': math.GR '5': cs.CE '6': cs.PL '7': cs.IT '8': cs.DS '9': cs.NE '10': math.ST splits: - name: train num_bytes: 1650587077 num_examples: 28388 - name: validation num_bytes: 144460101 num_examples: 2500 - name: test num_bytes: 141174323 num_examples: 2500 download_size: 1010911333 dataset_size: 1936221501 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* - config_name: no_ref data_files: - split: train path: no_ref/train-* - split: validation path: no_ref/validation-* - split: test path: no_ref/test-* --- Arxiv Classification: a classification of Arxiv Papers (11 classes). This dataset is intended for long context classification (documents have all > 4k tokens). \ Copied from "Long Document Classification From Local Word Glimpses via Recurrent Attention Learning" ``` @ARTICLE{8675939, author={He, Jun and Wang, Liqun and Liu, Liu and Feng, Jiao and Wu, Hao}, journal={IEEE Access}, title={Long Document Classification From Local Word Glimpses via Recurrent Attention Learning}, year={2019}, volume={7}, number={}, pages={40707-40718}, doi={10.1109/ACCESS.2019.2907992} } ``` * See: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8675939 * See: https://github.com/LiqunW/Long-document-dataset It contains 11 slightly unbalanced classes, 33k Arxiv Papers divided into 3 splits: train (28k), val (2.5k) and test (2.5k). 2 configs: * default * no_ref, removes references to the class inside the document (eg: [cs.LG] -> []) Compatible with [run_glue.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) script: ``` export MODEL_NAME=roberta-base export MAX_SEQ_LENGTH=512 python run_glue.py \ --model_name_or_path $MODEL_NAME \ --dataset_name ccdv/arxiv-classification \ --do_train \ --do_eval \ --max_seq_length $MAX_SEQ_LENGTH \ --per_device_train_batch_size 8 \ --gradient_accumulation_steps 4 \ --learning_rate 2e-5 \ --num_train_epochs 1 \ --max_eval_samples 500 \ --output_dir tmp/arxiv ```
ccdv/arxiv-summarization	ccdv	2024-08-08T05:49:50Z	2,683	90	[ "task_categories:summarization", "task_categories:text-generation", "multilinguality:monolingual", "language:en", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "conditional-text-generation" ]	[ "summarization", "text-generation" ]	2022-03-02T23:29:22Z	null	--- language: - en multilinguality: - monolingual size_categories: - 100K<n<1M task_categories: - summarization - text-generation task_ids: [] tags: - conditional-text-generation dataset_info: - config_name: document features: - name: article dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 7118099012 num_examples: 203037 - name: validation num_bytes: 216177092 num_examples: 6436 - name: test num_bytes: 216560120 num_examples: 6440 download_size: 3614462676 dataset_size: 7550836224 - config_name: section features: - name: article dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 7201498856 num_examples: 203037 - name: validation num_bytes: 218793173 num_examples: 6436 - name: test num_bytes: 219196348 num_examples: 6440 download_size: 3649452807 dataset_size: 7639488377 configs: - config_name: document data_files: - split: train path: document/train-* - split: validation path: document/validation-* - split: test path: document/test-* - config_name: section data_files: - split: train path: section/train-* - split: validation path: section/validation-* - split: test path: section/test-* default: true train-eval-index: - config: document task: summarization task_id: summarization splits: eval_split: test col_mapping: article: text abstract: target --- # Arxiv dataset for summarization Dataset for summarization of long documents.\ Adapted from this [repo](https://github.com/armancohan/long-summarization).\ Note that original data are pre-tokenized so this dataset returns " ".join(text) and add "\n" for paragraphs. \ This dataset is compatible with the [`run_summarization.py`](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) script from Transformers if you add this line to the `summarization_name_mapping` variable: ```python "ccdv/arxiv-summarization": ("article", "abstract") ``` ### Data Fields - `id`: paper id - `article`: a string containing the body of the paper - `abstract`: a string containing the abstract of the paper ### Data Splits This dataset has 3 splits: _train_, _validation_, and _test_. \ Token counts are white space based. \| Dataset Split \| Number of Instances \| Avg. tokens \| \| ------------- \| --------------------\|:----------------------\| \| Train \| 203,037 \| 6038 / 299 \| \| Validation \| 6,436 \| 5894 / 172 \| \| Test \| 6,440 \| 5905 / 174 \| # Cite original article ``` @inproceedings{cohan-etal-2018-discourse, title = "A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents", author = "Cohan, Arman and Dernoncourt, Franck and Kim, Doo Soon and Bui, Trung and Kim, Seokhwan and Chang, Walter and Goharian, Nazli", booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)", month = jun, year = "2018", address = "New Orleans, Louisiana", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/N18-2097", doi = "10.18653/v1/N18-2097", pages = "615--621", abstract = "Neural abstractive summarization models have led to promising results in summarizing relatively short documents. We propose the first model for abstractive summarization of single, longer-form documents (e.g., research papers). Our approach consists of a new hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. Empirical results on two large-scale datasets of scientific papers show that our model significantly outperforms state-of-the-art models.", } ```
microsoft/cats_vs_dogs	microsoft	2024-08-08T05:35:11Z	1,810	44	[ "task_categories:image-classification", "task_ids:multi-class-image-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:unknown", "size_categories:10K<n<100K", "format:parquet", "modality:image", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[ "image-classification" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - image-classification task_ids: - multi-class-image-classification paperswithcode_id: cats-vs-dogs pretty_name: Cats Vs. Dogs dataset_info: features: - name: image dtype: image - name: labels dtype: class_label: names: '0': cat '1': dog splits: - name: train num_bytes: 667071605.79 num_examples: 23410 download_size: 721642420 dataset_size: 667071605.79 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for Cats Vs. Dogs ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Cats vs Dogs Dataset](https://www.microsoft.com/en-us/download/details.aspx?id=54765) - Repository: - Paper: [Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization](https://www.microsoft.com/en-us/research/wp-content/uploads/2007/10/CCS2007.pdf) - Leaderboard: [Dogs vs. Cats](https://www.kaggle.com/competitions/dogs-vs-cats) - Point of Contact: ### Dataset Summary A large set of images of cats and dogs. There are 1738 corrupted images that are dropped. This dataset is part of a now-closed Kaggle competition and represents a subset of the so-called Asirra dataset. From the competition page: > The Asirra data set > > Web services are often protected with a challenge that's supposed to be easy for people to solve, but difficult for computers. Such a challenge is often called a [CAPTCHA](http://www.captcha.net/) (Completely Automated Public Turing test to tell Computers and Humans Apart) or HIP (Human Interactive Proof). HIPs are used for many purposes, such as to reduce email and blog spam and prevent brute-force attacks on web site passwords. > > Asirra (Animal Species Image Recognition for Restricting Access) is a HIP that works by asking users to identify photographs of cats and dogs. This task is difficult for computers, but studies have shown that people can accomplish it quickly and accurately. Many even think it's fun! Here is an example of the Asirra interface: > > Asirra is unique because of its partnership with [Petfinder.com](https://www.petfinder.com/), the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States. Kaggle is fortunate to offer a subset of this data for fun and research. ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given image as either containing a cat or a dog. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-cats-vs-dogs). ### Languages English. ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x375 at 0x29CEAD71780>, 'labels': 0 } ``` ### Data Fields The data instances have the following fields: - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]`. - `labels`: an `int` classification label. Class Label Mappings: ``` { "cat": 0, "dog": 1, } ``` ### Data Splits \| \| train \| \|---------------\|------:\| \| # of examples \| 23410 \| ## Dataset Creation ### Curation Rationale This subset was to built to test whether computer vision algorithms can beat the Asirra CAPTCHA: From the competition page: > Image recognition attacks > > While random guessing is the easiest form of attack, various forms of image recognition can allow an attacker to make guesses that are better than random. There is enormous diversity in the photo database (a wide variety of backgrounds, angles, poses, lighting, etc.), making accurate automatic classification difficult. In an informal poll conducted many years ago, computer vision experts posited that a classifier with better than 60% accuracy would be difficult without a major advance in the state of the art. For reference, a 60% classifier improves the guessing probability of a 12-image HIP from 1/4096 to 1/459. ### Source Data #### Initial Data Collection and Normalization This dataset is a subset of the Asirra dataset. From the competition page: > Asirra is unique because of its partnership with Petfinder.com, the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States. #### Who are the source language producers? The users of [Petfinder.com](https://www.petfinder.com/). ### Annotations #### Annotation process The images were annotated by selecting a pet category on [Petfinder.com](https://www.petfinder.com/). #### Who are the annotators? The users of [Petfinder.com](https://www.petfinder.com/). ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases From the paper: > Unlike many image-based CAPTCHAs which are abstract or subjective, Asirra’s challenges are concrete, inoffensive (cute, by some accounts), require no specialized or culturally biased knowledge, and have definite ground truth. This makes Asirra less frustrating for humans. Some beta-testers found it fun. The four-year-old child of one asked several times to “play the cat and dog game again.” ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ```bibtex @Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization, author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared}, title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization}, booktitle = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)}, year = {2007}, month = {October}, publisher = {Association for Computing Machinery, Inc.}, url = {https://www.microsoft.com/en-us/research/publication/asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization/}, edition = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)}, } ``` ### Contributions Thanks to [@nateraw](https://github.com/nateraw) for adding this dataset.
applied-ai-018/pretraining_v1-omega_books	applied-ai-018	2024-08-05T19:01:31Z	37,194	1	[ "size_categories:100M<n<1B", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-07-31T08:53:54Z	null	--- dataset_info: config_name: CC-MAIN-2013-20 features: - name: text dtype: string - name: id dtype: string - name: dump dtype: string - name: url dtype: string - name: file_path dtype: string - name: language dtype: string - name: language_score dtype: float64 - name: token_count dtype: int64 - name: score dtype: float64 - name: int_score dtype: int64 splits: - name: train num_bytes: 235476901236 num_examples: 51901183 download_size: 138494178972 dataset_size: 235476901236 configs: - config_name: CC-MAIN-2013-20 data_files: - split: train path: CC-MAIN-2013-20/train-* ---
fixie-ai/librispeech_asr	fixie-ai	2024-08-05T18:38:33Z	12,668	3	[ "language:en", "size_categories:100K<n<1M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-07-19T02:34:30Z	null	--- language: - en dataset_info: - config_name: clean features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string - name: continuation dtype: string splits: - name: test num_bytes: 623948478.48 num_examples: 2620 - name: validation num_bytes: 622190064.956 num_examples: 2703 - name: train.360 num_bytes: 41953890926.124 num_examples: 104014 - name: train.100 num_bytes: 11606313661.774 num_examples: 28539 download_size: 53886816833 dataset_size: 54806343131.334 - config_name: other features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string - name: continuation dtype: string splits: - name: train.500 num_bytes: 57330687390.808 num_examples: 148688 - name: validation num_bytes: 591511495.496 num_examples: 2864 - name: test num_bytes: 616939198.113 num_examples: 2939 download_size: 57019309170 dataset_size: 58539138084.417 configs: - config_name: clean data_files: - split: test path: clean/test-* - split: validation path: clean/validation-* - split: train.360 path: clean/train.360-* - split: train.100 path: clean/train.100-* - config_name: other data_files: - split: train.500 path: other/train.500-* - split: validation path: other/validation-* - split: test path: other/test-* ---
Vezora/Open-Critic-GPT	Vezora	2024-07-28T21:00:25Z	119	95	[ "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-07-27T21:50:27Z	null	--- {vezora license} --- <img src="https://huggingface.co/Vezora/Agent-7b-v1/resolve/main/Designer.png" width="200" height="200" /> # Open-Critic-GPT Dataset ## Overview Creator [Nicolas Mejia-Petit](https://twitter.com/mejia_petit) [My Kofi](https://ko-fi.com/nicolasmejiapetit) The Open-Critic-GPT dataset is a synthetic dataset created to train models in both identifying and fixing bugs in code. The dataset is generated using a unique synthetic data pipeline which involves: 1. Prompting a local model with an existing code example. 2. Introducing bugs into the code. While also having the model, from a first-person perspective, find the bugs and explain them. 3. Manipulating the data by shifting around where the broken code and working code is, and removing the # bug// and # error// comments from the code. This process allows the creation of two distinct datasets within Open-Critic-GPT: - Code-Preference-Pairs Dataset: (SFT) Contains pairs of duplicate code examples, with the only difference being one the rejected example has the bugged code 'surgically transplanted in' while the accepted is left the same. - Open-Critic-GPT Dataset: (DPO) Trains the model to find bugs and produce working code from broken code. - Both dataset's spans a total of 127 different language/structures, (some may have been lost in conversion started with 122k ended with 55k, due to lack of structured output, a finetuned model may preform better structured outputs.) - Both datasets contain of ~55K examples each (which both come from the same parent example) ## Dataset Structure The dataset is organized as follows: - Code Examples: Each code example consists of a a given snippet of bugged code and asked to find the bugs and fix them: - Bugged Code: The version of the code with introduced bugs and no comments, to avoid the model from learning from comments that say "Bug" or "Error". - Explanation: Explanation are provided for each bugged code example, detailing the nature of the bug, what the bug does to the code, and tips to avoid it. - Fixed Code: Lastly the model write the fully working code, with bugs fixed and comments added to the code. ## Usage - Just give me credit :) - Oh and current employee's of 'Open AI' and or the company as a whole is NOT permitted use this dataset or any derivative work that may come for training. It is mentioned in the custom apache license. - Otherwise to everyone else, it falls under Apache 2.0 :). ### Training Models When training models with the Open-Critic-GPT dataset, it is essential to use a data collator to ensure that the loss is not calculated on the bugged code. The data collator manages the dataset during training to provide the model with the correct inputs and outputs for loss calculation. ### Crediting dataset creators: - This dataset was created using 'm-a-p/CodeFeedback-Filtered-Instruction' Which contiains data from several different sources - Here are the orginal authors of the oringal sources, Thank you to the following authors: Nick Roshdieh for evol Instruct, Ajinkya Bawase for Python shareGPT 23k, Intellligent Software Engineering for Magicoder, and Multimodal Art Projection for the compiled and filtered m-a-p/CodeFeedback-Filtered-Instruction. ### Begging for money section. - I created this dataset off a single 3090. Imagine what I could do with two. - I can't continue to work on these open source projects, with out receiving a sponsorship or a form of compensation, all the money I make from this will go dirrectly back into helping the open source community. - If you can, It would mean the world to me any donation helps me release this work for free. thank you :) - [Kofi](https://ko-fi.com/nicolasmejiapetit)
google/docci	google	2024-07-24T16:00:33Z	176	72	[ "task_categories:text-to-image", "task_categories:image-to-text", "task_ids:image-captioning", "annotations_creators:expert-generated", "annotations_creators:crowdsourced", "language_creators:other", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-4.0", "size_categories:10K<n<100K", "arxiv:2404.19753", "region:us" ]	[ "text-to-image", "image-to-text" ]	2023-10-31T14:46:10Z	2	--- annotations_creators: - expert-generated - crowdsourced language: - en language_creators: - other license: - cc-by-4.0 multilinguality: - monolingual pretty_name: DOCCI size_categories: - 10K<n<100K source_datasets: - original tags: [] task_categories: - text-to-image - image-to-text task_ids: - image-captioning --- # Dataset Card for DOCCI ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://google.github.io/docci - Paper: [arXiv](https://arxiv.org/pdf/2404.19753) - Data Explorer: [Check images and descriptions](https://google.github.io/docci/viz.html?c=&p=1) - Point of Contact: [email protected] - Report an Error: [Google Forms](https://forms.gle/v8sUoXWHvuqrWyfe9) ### Dataset Summary DOCCI (Descriptions of Connected and Contrasting Images) is a collection of images paired with detailed descriptions. The descriptions explain the key elements of the images, as well as secondary information such as background, lighting, and settings. The images are specifically taken to help assess the precise visual properties of images. DOCCI also includes many related images that vary in having key differences from the others. All descriptions are manually annotated to ensure they adequately distinguish each image from its counterparts. ### Supported Tasks Text-to-Image and Image-to-Text generation ### Languages English ## Dataset Structure ### Data Instances ``` { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1536x2048>, 'example_id': 'qual_dev_00000', 'description': 'An indoor angled down medium close-up front view of a real sized stuffed dog with white and black colored fur wearing a blue hard hat with a light on it. A couple inches to the right of the dog is a real sized black and white penguin that is also wearing a blue hard hat with a light on it. The dog is sitting, and is facing slightly towards the right while looking to its right with its mouth slightly open, showing its pink tongue. The dog and penguin are placed on a gray and white carpet, and placed against a white drawer that has a large gray cushion on top of it. Behind the gray cushion is a transparent window showing green trees on the outside.' } ``` ### Data Fields Name \| Explanation --- \| --- `image` \| PIL.JpegImagePlugin.JpegImageFile `example_id` \| The unique ID of an example follows this format: `<SPLIT_NAME>_<EXAMPLE_NUMBER>`. `description` \| Text description of the associated image. ### Data Splits Dataset \| Train \| Test \| Qual Dev \| Qual Test ---\| ---: \| ---: \| ---: \| ---: DOCCI \| 9,647 \| 5,000 \| 100 \| 100 DOCCI-AAR \| 4,932 \| 5,000 \| -- \| -- ## Dataset Creation ### Curation Rationale DOCCI is designed as an evaluation dataset for both text-to-image (T2I) and image-to-text (I2T) generation. Please see our paper for more details. ### Source Data #### Initial Data Collection All images were taken by one of the authors and their family. ### Annotations #### Annotation process All text descriptions were written by human annotators. We do not rely on any automated process in our data annotation pipeline. Please see Appendix A of [our paper](https://arxiv.org/pdf/2404.19753) for details about image curation. ### Personal and Sensitive Information We manually reviewed all images for personally identifiable information (PII), removing some images and blurring detected faces, phone numbers, and URLs to protect privacy. For text descriptions, we instructed annotators to exclude any PII, such as people's names, phone numbers, and URLs. After the annotation phase, we employed automatic tools to scan for PII, ensuring the descriptions remained free of such information. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ### Licensing Information CC BY 4.0 ### Citation Information ``` @inproceedings{OnoeDocci2024, author = {Yasumasa Onoe and Sunayana Rane and Zachary Berger and Yonatan Bitton and Jaemin Cho and Roopal Garg and Alexander Ku and Zarana Parekh and Jordi Pont-Tuset and Garrett Tanzer and Su Wang and Jason Baldridge}, title = {{DOCCI: Descriptions of Connected and Contrasting Images}}, booktitle = {ECCV}, year = {2024} } ```
mlfoundations/dclm-baseline-1.0	mlfoundations	2024-07-22T15:27:52Z	1,151,865	218	[ "license:cc-by-4.0", "arxiv:2406.11794", "region:us" ]	[]	2024-06-17T18:57:13Z	null	--- license: cc-by-4.0 dataset_info: features: - name: bff_contained_ngram_count_before_dedupe dtype: int64 - name: language_id_whole_page_fasttext struct: - name: en dtype: float64 - name: metadata struct: - name: Content-Length dtype: string - name: Content-Type dtype: string - name: WARC-Block-Digest dtype: string - name: WARC-Concurrent-To dtype: string - name: WARC-Date dtype: timestamp[s] - name: WARC-IP-Address dtype: string - name: WARC-Identified-Payload-Type dtype: string - name: WARC-Payload-Digest dtype: string - name: WARC-Record-ID dtype: string - name: WARC-Target-URI dtype: string - name: WARC-Type dtype: string - name: WARC-Warcinfo-ID dtype: string - name: WARC-Truncated dtype: string - name: previous_word_count dtype: int64 - name: text dtype: string - name: url dtype: string - name: warcinfo dtype: string - name: fasttext_openhermes_reddit_eli5_vs_rw_v2_bigram_200k_train_prob dtype: float64 --- ## DCLM-baseline DCLM-baseline is a 4T token / 3B document pretraining dataset that achieves strong performance on language model benchmarks. Below are comparisions of model trained on DCLM-baseline with other models in the 7B regime. \| Model \| Params \| Tokens \| Open dataset? \| CORE \| MMLU \| EXTENDED \| \|---------------\|--------\|--------\|---------------\|----------\|----------\|----------\| \| Open weights, closed datasets \| \| \| \| \| \| \| \| Llama2 \| 7B \| 2T \| ✗ \| 49.2 \| 45.8 \| 34.1 \| \| DeepSeek \| 7B \| 2T \| ✗ \| 50.7 \| 48.5 \| 35.3 \| \| Mistral-0.3 \| 7B \| ? \| ✗ \| 57.0 \| 62.7 \| 45.1 \| \| QWEN-2 \| 7B \| ? \| ✗ \| 57.5 \| 71.9 \| 50.5 \| \| Llama3 \| 8B \| 15T \| ✗ \| 57.6 \| 66.2 \| 46.3 \| \| Gemma \| 8B \| 6T \| ✗ \| 57.8 \| 64.3 \| 44.6 \| \| Phi-3 \| 7B \| ? \| ✗ \| 61.0 \| 69.9 \| 57.9 \| \| Open weights, open datasets \| \| \| \| \| \| \| \| Falcon \| 7B \| 1T \| ✓ \| 44.1 \| 27.4 \| 25.1 \| \| Amber \| 7B \| 1.2T \| ✓ \| 39.8 \| 27.9 \| 22.3 \| \| Crystal \| 7B \| 1.2T \| ✓ \| 48.0 \| 48.2 \| 33.2 \| \| OLMo-1.7 \| 7B \| 2.1T \| ✓ \| 47.0 \| 54.0 \| 34.2 \| \| MAP-Neo \| 7B \| 4.5T \| ✓ \| 50.2 \| 57.1 \| 40.4 \| \| Models we trained \| \| \| \| \| \| \| \| FineWeb edu \| 7B \| 0.14T \| ✓ \| 38.7 \| 26.3 \| 22.1 \| \| FineWeb edu \| 7B \| 0.28T \| ✓ \| 41.9 \| 37.3 \| 24.5 \| \| DCLM-BASELINE \| 7B \| 0.14T \| ✓ \| 44.1 \| 38.3 \| 25.0 \| \| DCLM-BASELINE \| 7B \| 0.28T \| ✓ \| 48.9 \| 50.8 \| 31.8 \| \| DCLM-BASELINE \| 7B \| 2.6T \| ✓ \| 57.1 \| 63.7 \| 45.4 \| ## Dataset Details ### Dataset Description - Curated by: The DCLM Team - Language(s) (NLP): English - License: CC-by-4.0 ### Dataset Sources - Repository: https://datacomp.ai/dclm - Paper:: https://arxiv.org/abs/2406.11794 - Construction Code: https://github.com/mlfoundations/dclm ## Uses ### Direct Use DCLM-Baseline is intended to be used as a research baseline for the DCLM benchmark. It demonstrates the importance of data curation in training performant language models. ### Out-of-Scope Use DCLM-Baseline is not intended for training production-ready models or for specific domains such as code and math. It may not perform as well as domain-specific datasets for these tasks. Due to these limitations, the dataset is intended for research use only. DCLM-Baseline is a subset of the DCLM-Pool, which is a corpus of 240 trillion tokens derived from Common Crawl. The dataset is in plain text format. ## Dataset Creation ### Curation Rationale DCLM-Baseline was created to demonstrate the effectiveness of the DCLM testbed in developing high-quality training sets for language models. It serves as a proof of concept for the data curation strategies enabled by DCLM and is designed to be a research baseline for the benchmark. ### Source Data #### Data Collection and Processing DCLM-Baseline was created by applying a series of cleaning, filtering, and deduplication steps to the raw Common Crawl data (DCLM-Pool). The key steps include: 1. Heuristic cleaning and filtering (reproduction of RefinedWeb) 2. Deduplication using a Bloom filter 3. Model-based filtering using a fastText classifier trained on instruction-formatted data (OpenHermes 2.5 and r/ExplainLikeImFive) #### Who are the source data producers? The source data is from Common Crawl, which is a repository of web crawl data. ### Personal and Sensitive Information [More Information Needed] ## Bias, Risks, and Limitations The dataset may contain biases present in the Common Crawl data. The dataset's performance on code and math tasks is limited compared to its performance on language understanding tasks. DCLM-Baseline is designed for research purposes only. ### Recommendations Users should be aware of the potential biases and limitations of the dataset, especially when using it for specific domains like code and math. The dataset should only be used for research purposes in the context of the DCLM benchmark. ## Citation ```bibtex @misc{li2024datacomplm, title={DataComp-LM: In search of the next generation of training sets for language models}, author={Jeffrey Li and Alex Fang and Georgios Smyrnis and Maor Ivgi and Matt Jordan and Samir Gadre and Hritik Bansal and Etash Guha and Sedrick Keh and Kushal Arora and Saurabh Garg and Rui Xin and Niklas Muennighoff and Reinhard Heckel and Jean Mercat and Mayee Chen and Suchin Gururangan and Mitchell Wortsman and Alon Albalak and Yonatan Bitton and Marianna Nezhurina and Amro Abbas and Cheng-Yu Hsieh and Dhruba Ghosh and Josh Gardner and Maciej Kilian and Hanlin Zhang and Rulin Shao and Sarah Pratt and Sunny Sanyal and Gabriel Ilharco and Giannis Daras and Kalyani Marathe and Aaron Gokaslan and Jieyu Zhang and Khyathi Chandu and Thao Nguyen and Igor Vasiljevic and Sham Kakade and Shuran Song and Sujay Sanghavi and Fartash Faghri and Sewoong Oh and Luke Zettlemoyer and Kyle Lo and Alaaeldin El-Nouby and Hadi Pouransari and Alexander Toshev and Stephanie Wang and Dirk Groeneveld and Luca Soldaini and Pang Wei Koh and Jenia Jitsev and Thomas Kollar and Alexandros G. Dimakis and Yair Carmon and Achal Dave and Ludwig Schmidt and Vaishaal Shankar}, year={2024}, eprint={2406.11794}, archivePrefix={arXiv}, primaryClass={id='cs.LG' full_name='Machine Learning' is_active=True alt_name=None in_archive='cs' is_general=False description='Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.'} ```
rethinklab/Bench2Drive-Full	rethinklab	2024-07-22T06:46:56Z	15,646	2	[ "license:apache-2.0", "region:us" ]	[]	2024-05-13T05:56:17Z	null	--- license: apache-2.0 ---
mlfoundations/dclm-baseline-1.0-parquet	mlfoundations	2024-07-19T17:35:58Z	24,886	27	[ "language:en", "license:cc-by-4.0", "size_categories:1B<n<10B", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2406.11794", "region:us" ]	[]	2024-06-30T20:31:14Z	null	--- language: - en license: cc-by-4.0 --- ## DCLM-baseline *Note: this is an identical copy of https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0, where all the files have been mapped to a parquet format.* DCLM-baseline is a 4T token / 3B document pretraining dataset that achieves strong performance on language model benchmarks. Below are comparisions of model trained on DCLM-baseline with other models in the 7B regime. \| Model \| Params \| Tokens \| Open dataset? \| CORE \| MMLU \| EXTENDED \| \|---------------\|--------\|--------\|---------------\|----------\|----------\|----------\| \| Open weights, closed datasets \| \| \| \| \| \| \| \| Llama2 \| 7B \| 2T \| ✗ \| 49.2 \| 45.8 \| 34.1 \| \| DeepSeek \| 7B \| 2T \| ✗ \| 50.7 \| 48.5 \| 35.3 \| \| Mistral-0.3 \| 7B \| ? \| ✗ \| 57.0 \| 62.7 \| 45.1 \| \| QWEN-2 \| 7B \| ? \| ✗ \| 57.5 \| 71.9 \| 50.5 \| \| Llama3 \| 8B \| 15T \| ✗ \| 57.6 \| 66.2 \| 46.3 \| \| Gemma \| 8B \| 6T \| ✗ \| 57.8 \| 64.3 \| 44.6 \| \| Phi-3 \| 7B \| ? \| ✗ \| 61.0 \| 69.9 \| 57.9 \| \| Open weights, open datasets \| \| \| \| \| \| \| \| Falcon \| 7B \| 1T \| ✓ \| 44.1 \| 27.4 \| 25.1 \| \| Amber \| 7B \| 1.2T \| ✓ \| 39.8 \| 27.9 \| 22.3 \| \| Crystal \| 7B \| 1.2T \| ✓ \| 48.0 \| 48.2 \| 33.2 \| \| OLMo-1.7 \| 7B \| 2.1T \| ✓ \| 47.0 \| 54.0 \| 34.2 \| \| MAP-Neo \| 7B \| 4.5T \| ✓ \| 50.2 \| 57.1 \| 40.4 \| \| Models we trained \| \| \| \| \| \| \| \| FineWeb edu \| 7B \| 0.14T \| ✓ \| 38.7 \| 26.3 \| 22.1 \| \| FineWeb edu \| 7B \| 0.28T \| ✓ \| 41.9 \| 37.3 \| 24.5 \| \| DCLM-BASELINE \| 7B \| 0.14T \| ✓ \| 44.1 \| 38.3 \| 25.0 \| \| DCLM-BASELINE \| 7B \| 0.28T \| ✓ \| 48.9 \| 50.8 \| 31.8 \| \| DCLM-BASELINE \| 7B \| 2.6T \| ✓ \| 57.1 \| 63.7 \| 45.4 \| ## Dataset Details ### Dataset Description - Curated by: The DCLM Team - Language(s) (NLP): English - License: CC-by-4.0 ### Dataset Sources - Repository: https://datacomp.ai/dclm - Paper:: https://arxiv.org/abs/2406.11794 - Construction Code: https://github.com/mlfoundations/dclm ## Uses ### Direct Use DCLM-Baseline is intended to be used as a research baseline for the DCLM benchmark. It demonstrates the importance of data curation in training performant language models. ### Out-of-Scope Use DCLM-Baseline is not intended for training production-ready models or for specific domains such as code and math. It may not perform as well as domain-specific datasets for these tasks. Due to these limitations, the dataset is intended for research use only. DCLM-Baseline is a subset of the DCLM-Pool, which is a corpus of 240 trillion tokens derived from Common Crawl. The dataset is in plain text format. ## Dataset Creation ### Curation Rationale DCLM-Baseline was created to demonstrate the effectiveness of the DCLM testbed in developing high-quality training sets for language models. It serves as a proof of concept for the data curation strategies enabled by DCLM and is designed to be a research baseline for the benchmark. ### Source Data #### Data Collection and Processing DCLM-Baseline was created by applying a series of cleaning, filtering, and deduplication steps to the raw Common Crawl data (DCLM-Pool). The key steps include: 1. Heuristic cleaning and filtering (reproduction of RefinedWeb) 2. Deduplication using a Bloom filter 3. Model-based filtering using a fastText classifier trained on instruction-formatted data (OpenHermes 2.5 and r/ExplainLikeImFive) #### Who are the source data producers? The source data is from Common Crawl, which is a repository of web crawl data. ### Personal and Sensitive Information [More Information Needed] ## Bias, Risks, and Limitations The dataset may contain biases present in the Common Crawl data. The dataset's performance on code and math tasks is limited compared to its performance on language understanding tasks. DCLM-Baseline is designed for research purposes only. ### Recommendations Users should be aware of the potential biases and limitations of the dataset, especially when using it for specific domains like code and math. The dataset should only be used for research purposes in the context of the DCLM benchmark. ## Citation ```bibtex @misc{li2024datacomplm, title={DataComp-LM: In search of the next generation of training sets for language models}, author={Jeffrey Li and Alex Fang and Georgios Smyrnis and Maor Ivgi and Matt Jordan and Samir Gadre and Hritik Bansal and Etash Guha and Sedrick Keh and Kushal Arora and Saurabh Garg and Rui Xin and Niklas Muennighoff and Reinhard Heckel and Jean Mercat and Mayee Chen and Suchin Gururangan and Mitchell Wortsman and Alon Albalak and Yonatan Bitton and Marianna Nezhurina and Amro Abbas and Cheng-Yu Hsieh and Dhruba Ghosh and Josh Gardner and Maciej Kilian and Hanlin Zhang and Rulin Shao and Sarah Pratt and Sunny Sanyal and Gabriel Ilharco and Giannis Daras and Kalyani Marathe and Aaron Gokaslan and Jieyu Zhang and Khyathi Chandu and Thao Nguyen and Igor Vasiljevic and Sham Kakade and Shuran Song and Sujay Sanghavi and Fartash Faghri and Sewoong Oh and Luke Zettlemoyer and Kyle Lo and Alaaeldin El-Nouby and Hadi Pouransari and Alexander Toshev and Stephanie Wang and Dirk Groeneveld and Luca Soldaini and Pang Wei Koh and Jenia Jitsev and Thomas Kollar and Alexandros G. Dimakis and Yair Carmon and Achal Dave and Ludwig Schmidt and Vaishaal Shankar}, year={2024}, eprint={2406.11794}, archivePrefix={arXiv}, primaryClass={id='cs.LG' full_name='Machine Learning' is_active=True alt_name=None in_archive='cs' is_general=False description='Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.'} ```
TheBritishLibrary/EThOS-PhD-metadata	TheBritishLibrary	2024-07-19T16:28:25Z	21	2	[ "task_categories:text-classification", "task_categories:fill-mask", "task_ids:multi-label-classification", "task_ids:masked-language-modeling", "multilinguality:monolingual", "language:en", "region:us" ]	[ "text-classification", "fill-mask" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: [] language: - en language_creators: [] license: [] multilinguality: - monolingual pretty_name: EThOS PhD metadata size_categories: [] source_datasets: [] tags: [] task_categories: - text-classification - fill-mask task_ids: - multi-label-classification - masked-language-modeling --- # Dataset Card for EThOS PhD metadata ## Table of Contents - [Dataset Card for blbooksgenre](#dataset-card-for-EThOS PhD metadata) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Supervised tasks](#supervised-tasks) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage:: https://bl.iro.bl.uk/concern/datasets/10cc13f9-797d-41f2-a7e2-d29f4306133e?locale=en - Repository: https://doi.org/10.23636/rcm4-zk44 - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary The data in this collection comprises the bibliographic metadata for all UK doctoral theses listed in EThOS, the UK's national thesis service. We estimate the data covers around 98% of all PhDs ever awarded by UK Higher Education institutions, dating back to 1787. Thesis metadata from every PhD-awarding university in the UK is included. You can investigate and re-use this unique collection of UK universities' PhD thesis data to analyse trends in postgraduate research, make connections between researchers, apply large data analysis, improve citation of theses and many more applications. [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] #### Supervised tasks [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure [More Information Needed] ### Data Instances An example data instance: ```python {'Abstract': ' ', 'Author': 'Loizou, Panos A.', 'Author ISNI': 'https://isni.org/isni/0000000136122593', 'DOI': ' ', 'Date': datetime.datetime(1989, 1, 1, 0, 0), 'EThOS URL': 'https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.232781', 'Funder(s)': ' ', 'IR URL': ' ', 'Institution': 'University of Manchester', 'Institution ISNI': 'https://isni.org/isni/0000000121662407', 'ORCID': ' ', 'Qualification': 'Thesis (Ph.D.)', 'Subject Discipline': 0, 'Supervisor(s)': ' ', 'Title': 'Computation and measurement of turbulent flow through idealized turbine blade passages'} ``` ### Data Fields [More Information Needed] ### Data Splits This dataset contains a single split `train`. ## Dataset Creation [More Information Needed] ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data [More Information Needed] ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The books are licensed under the [CC BY 4.0 Attribution](https://creativecommons.org/licenses/by/4.0/) license. ### Citation Information
nunorc/squad_v1_pt	nunorc	2024-07-17T06:57:39Z	127	12	[ "task_categories:question-answering", "task_ids:extractive-qa", "task_ids:open-domain-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:pt", "license:mit", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1606.05250", "region:us" ]	[ "question-answering" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - pt license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa - open-domain-qa pretty_name: SquadV1Pt dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: train num_bytes: 85322985 num_examples: 87599 - name: validation num_bytes: 11265418 num_examples: 10570 download_size: 17430106 dataset_size: 96588403 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* --- # Dataset Card for "squad_v1_pt" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://github.com/nunorc/squad-v1.1-pt](https://github.com/nunorc/squad-v1.1-pt) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 39.53 MB - Size of the generated dataset: 96.72 MB - Total amount of disk used: 136.25 MB ### Dataset Summary Portuguese translation of the SQuAD dataset. The translation was performed automatically using the Google Cloud API. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 39.53 MB - Size of the generated dataset: 96.72 MB - Total amount of disk used: 136.25 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [0], "text": ["Saint Bernadette Soubirous"] }, "context": "\"Arquitetonicamente, a escola tem um caráter católico. No topo da cúpula de ouro do edifício principal é uma estátua de ouro da ...", "id": "5733be284776f41900661182", "question": "A quem a Virgem Maria supostamente apareceu em 1858 em Lourdes, na França?", "title": "University_of_Notre_Dame" } ``` ### Data Fields The data fields are the same among all splits. #### default - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. ### Data Splits \| name \| train \| validation \| \| ------- \| ----: \| ---------: \| \| default \| 87599 \| 10570 \| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{2016arXiv160605250R, author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev}, Konstantin and {Liang}, Percy}, title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}", journal = {arXiv e-prints}, year = 2016, eid = {arXiv:1606.05250}, pages = {arXiv:1606.05250}, archivePrefix = {arXiv}, eprint = {1606.05250}, } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@albertvillanova](https://github.com/albertvillanova), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
argilla/dpo-mix-7k	argilla	2024-07-16T13:31:40Z	880	162	[ "language:en", "license:mit", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "library:distilabel", "library:argilla", "region:us", "distilabel", "synthetic", "dpo", "argilla" ]	[]	2024-01-30T05:49:25Z	null	--- language: - en license: mit size_categories: - 1K<n<10K configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: dataset dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: chosen_rating dtype: float64 - name: rejected_rating dtype: float64 splits: - name: train num_bytes: 41362946 num_examples: 6750 - name: test num_bytes: 4586808 num_examples: 750 download_size: 24232011 dataset_size: 45949754 tags: - distilabel - synthetic - dpo - argilla --- # Argilla DPO Mix 7K Dataset > A small cocktail combining DPO datasets built by Argilla with [distilabel](https://github.com/argilla-io/distilabel). The goal of this dataset is having a small, high-quality DPO dataset by filtering only highly rated chosen responses. <div> <img src="https://cdn-uploads.huggingface.co/production/uploads/60420dccc15e823a685f2b03/Csd2-zPji7iwIxyz6UFe1.webp"> </div> <p align="center"> <a href="https://github.com/argilla-io/distilabel"> <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> </a> </p> ## Datasets mixed As already mentioned, this dataset mixes the following datasets: * [`argilla/distilabel-capybara-dpo-7k-binarized`](https://huggingface.co/datasets/argilla/distilabel-capybara-dpo-7k-binarized): random sample of highly scored chosen responses (>=4). * [`argilla/distilabel-intel-orca-dpo-pairs`](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs): random sample of highly scored chosen responses (>=8). * [`argilla/ultrafeedback-binarized-preferences-cleaned`](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned): random sample of highly scored chosen responses (>=4). The samples have been randomly selected from the original datasets with a proportion of 0.33 each, as can be seen via the `dataset` column of the dataset. ## Next steps * Adding more samples * Use data selection techniques to improve the diversity, usefulness, and complexity of the dataset.
argilla/distilabel-capybara-dpo-7k-binarized	argilla	2024-07-16T13:30:29Z	1,509	180	[ "task_categories:question-answering", "task_categories:text-generation", "language:en", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "library:distilabel", "library:argilla", "region:us", "Physics", "Biology", "Math", "Chemistry", "Culture", "Logic", "Roleplay", "rlaif", "rlhf", "dpo", "distilabel", "synthetic", "argilla" ]	[ "question-answering", "text-generation" ]	2024-01-26T08:36:14Z	null	--- license: apache-2.0 task_categories: - question-answering - text-generation language: - en tags: - Physics - Biology - Math - Chemistry - Culture - Logic - Roleplay - rlaif - rlhf - dpo - distilabel - synthetic - argilla pretty_name: CapybaraDPO-7k size_categories: - 1K<n<10K dataset_info: features: - name: source dtype: string - name: conversation list: - name: input dtype: string - name: output dtype: string - name: original_response dtype: string - name: generation_prompt sequence: string - name: raw_generation_responses sequence: string - name: new_generations sequence: string - name: prompt dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: rating_chosen dtype: int64 - name: rating_rejected dtype: int64 - name: chosen_model dtype: string - name: rejected_model dtype: string splits: - name: train num_bytes: 348791651 num_examples: 7563 download_size: 155776049 dataset_size: 348791651 configs: - config_name: default data_files: - split: train path: data/train-* --- # Capybara-DPO 7K binarized > A DPO dataset built with [distilabel](https://github.com/argilla-io/distilabel) atop the awesome [LDJnr/Capybara](https://huggingface.co/datasets/LDJnr/Capybara) > This is a preview version to collect feedback from the community. v2 will include the full base dataset and responses from more powerful models. <div> <img src="https://cdn-uploads.huggingface.co/production/uploads/60420dccc15e823a685f2b03/Vmr0FtTvnny6Snm-UDM_n.png"> </div> <p align="center"> <a href="https://github.com/argilla-io/distilabel"> <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> </a> </p> ## Why? Multi-turn dialogue data is key to fine-tune capable chat models. Multi-turn preference data has been used by the most relevant RLHF works (Anthropic, Meta Llama2, etc.). Unfortunately, there are very few multi-turn open datasets for DPO/RLHF. This dataset is the first of a series of datasets to fill this gap for the Open Source AI community. Why Capybara? Because it's 🔥 ## Dataset structure Here's a video showing the dataset structure using Argilla UI. For preference tuning, chosen and rejected mean the best/worse response to the last turn. <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/60420dccc15e823a685f2b03/KoYK-Or0JNNVS9PNLF8jJ.mp4"></video> ## How to use this dataset This dataset is a multi-turn preference dataset to improve chat capabilities of open-source LLMs. Chosen and rejected pairs are formatted following OpenAI's conversation format with potentially several turns between a user and an assistant. To use this dataset for DPO use only the last assistant message as `chosen`/`rejected` and the rest as `prompt`. Let's see an example, step by step. First let's keep only highly-scored chosen responses (scale is 1-5) and let's filter out very long conversations: ```python capy = load_dataset("argilla/distilabel-capybara-dpo-7k-binarized", split="train") capy = capy.filter( lambda r: r["rating_chosen"]>=4 ) capy = capy.map(lambda r: {"messages": len(r["chosen"])}).filter(lambda r: r["messages"]<18) ``` Then let's prepare this in the chatml prompt and `trl` format: ```python def chatml_format(example): # get everything except the last message as input prompt = tokenizer.apply_chat_template(example["chosen"][:-1], tokenize=False, add_generation_prompt=True) # get the last assistant responses chosen = example["chosen"][-1]["content"] + "</s>" rejected = example["rejected"][-1]["content"] + "</s>" return { "prompt": system + prompt, "chosen": chosen, "rejected": rejected, } # Save columns original_columns = capy.column_names # Format dataset capy = capy.map( chatml_format, remove_columns=original_columns ) ``` The dataset is now ready to be used for DPO fine-tuning! In our benchmarks with 7B models, we've seen this is a challenging dataset to learn from, the best results can be achieved by mixing it with other datasets like this [dpo mix 7k](https://huggingface.co/datasets/argilla/dpo-mix-7k). We'd love to hear from the community how this works with larger models and other hyperparams. ## How we've built this dataset ### Generate responses from 3 different OSS models In the spirit of UltraFeedback, in this step we generate three responses to the last user message using OSS 7B models and distilabel's `LLMPool` and the vLLM engine. We use Notus7B, NeuralBeagle and OpenHermes-2.5. Additionally, the original capybara dataset already has a generated assistant response (the last assistant response) we keep it for the next step. ```python from distilabel.llm import LLM, LLMPool, ProcessLLM from distilabel.tasks import TextGenerationTask, Task from distilabel.tasks.prompt import Prompt from distilabel.dataset import DatasetCheckpoint from distilabel.pipeline import Pipeline from datasets import load_dataset from dataclasses import dataclass from pathlib import Path dataset = load_dataset("LDJnr/Capybara", split="train") here = Path(__file__).parent.resolve() def extract_conversation(r): all_but_last = r["conversation"][:-1] all_but_last.append({"input": r["conversation"][-1]["input"]}) last = r["conversation"][-1]["output"] return {"input": all_but_last, "original_response": last} dataset = dataset.map(extract_conversation) @dataclass class NotusChatTextGeneration(TextGenerationTask): # custom class to generate prompts in the chatml format # skipped for brevity @dataclass class ChatMLTextGeneration(TextGenerationTask): # custom class to generate prompts in the chatml format # skipped for brevity save_frequency = len(dataset) // 1000 checkpointing = DatasetCheckpoint(path=here / "checkpoint_generation", save_frequency=save_frequency) def load_notus(task: Task) -> LLM: import os from distilabel.llm import vLLM from vllm import LLM os.environ["CUDA_VISIBLE_DEVICES"] = "0" return vLLM( vllm=LLM( model="argilla/notus-7b-v1", trust_remote_code=True ), task=task, max_new_tokens=1024, temperature=1, ) def load_beagle(task: Task) -> LLM: import os from distilabel.llm import vLLM from vllm import LLM os.environ["CUDA_VISIBLE_DEVICES"] = "1" return vLLM( vllm=LLM( model="mlabonne/NeuralBeagle14-7B", trust_remote_code=True ), task=task, max_new_tokens=1024, temperature=1, ) def load_hermes(task: Task) -> LLM: import os from distilabel.llm import vLLM from vllm import LLM os.environ["CUDA_VISIBLE_DEVICES"] = "2" return vLLM( vllm=LLM( model="teknium/OpenHermes-2.5-Mistral-7B", trust_remote_code=True ), task=task, max_new_tokens=1024, temperature=1, ) llm_pool = LLMPool( [ ProcessLLM(task=NotusChatTextGeneration(), load_llm_fn=load_notus), ProcessLLM(task=ChatMLTextGeneration(), load_llm_fn=load_beagle), ProcessLLM(task=ChatMLTextGeneration(), load_llm_fn=load_hermes), ] ) pipe_generation_pool = Pipeline(generator=llm_pool) dataset = pipe_generation_pool.generate( dataset=dataset, num_generations=len(llm_pool.llms), batch_size=32, display_progress_bar=True, checkpoint_strategy=checkpointing, ) ``` ### Generate a preference dataset from 4 responses At this point, we have 4 responses to each multi-turn dialogue. We will now use distilabel's `UltraFeedback.for_overall_quality()` preference model to judge the quality of responses. We use gpt-4-turbo but could have use other models. ```python from distilabel.tasks import UltraFeedbackTask from distilabel.llm import OpenAILLM from distilabel.pipeline import Pipeline from datasets import load_dataset def format_conversation(r): mapping_role = {"input": "<\|user\|>\n", "output":"<\|assistant\|>\n"} all_but_last = r["conversation"][:-1] all_but_last.append({"input": r["conversation"][-1]["input"]}) input = "" for e in all_but_last: for k,v in e.items(): input += f"{mapping_role[k]}{v}</s>\n" return {"input": input} # this formats the conversation input # one could choose other format prepared_dataset = dataset.map(format_conversation) # the LLM Judge will evaluate each response to the # last user message taking into account the conversation history labeler = OpenAILLM( task=UltraFeedbackTask.for_overall_quality(), model="gpt-4-1106-preview", num_threads=8, max_new_tokens=512, ) distilabeler = Pipeline( labeller=labeler ) # this computes ratings and natural language critiques for each pair distiset = distilabeler.generate(dataset=prepared_dataset, num_generations=4, display_progress_bar=True) ``` This preference step is also useful to evaluate the performance of the four models (3+ the original response in Capybara): ![image/png](https://cdn-uploads.huggingface.co/production/uploads/60420dccc15e823a685f2b03/FShIr2Hsu-dk9IpAihV1A.png) ## Benchmark results We've tested this new dataset by preference tuning [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B). The resulting model is [CapybaraHermes](https://huggingface.co/argilla/CapybaraHermes-2.5-Mistral-7B). CapybaraHermes has been preference tuned with LoRA and TRL for 3 epochs using argilla's [dpo mix 7k](https://huggingface.co/datasets/argilla/dpo-mix-7k). To test the impact on multi-turn performance we have used MTBench. We also include the Nous Benchmark results and Mistral-7B-Instruct-v0.2 for reference as it's a strong 7B model on MTBench: \| Model \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| MTBench First Turn \| MTBench Second Turn \| Nous avg. \| MTBench avg. \| \|-----------------------------------\|---------\|---------\|------------\|----------\|------------\|-------------\|-----------\|--------------\| \| CapybaraHermes-2.5-Mistral-7B \| 43.8 \| 73.35 \| 57.07 \| 42.44 \| 8.24375 \| 7.5625 \| 54.16 \| 7.903125 \| \| teknium/OpenHermes-2.5-Mistral-7B \| 42.75 \| 72.99 \| 52.99 \| 40.94 \| 8.25 \| 7.2875 \| 52.42 \| 7.76875 \| \| Mistral-7B-Instruct-v0.2 \| 38.5 \| 71.64 \| 66.82 \| 42.29 \| 7.8375 \| 7.1 \| 54.81 \| 7.46875 \| The most interesting aspect in the context of the capybara-dpo dataset is the increased performance in MTBench Second Turn scores. For the merge lovers, we also preference tuned Beagle14-7B with a mix of capybara-dpo and distilabel orca pairs using the same recipe as NeuralBeagle (see [ YALL - Yet Another LLM Leaderboard](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard) for reference): \| Model \|AGIEval\|GPT4All\|TruthfulQA\|Bigbench\|Average\| \|------------------------------------------------------------------------------------------------------------------------------------\|------:\|------:\|---------:\|-------:\|------:\| \|[DistilabelBeagle14-7B](https://huggingface.co/dvilasuero/DistilabelBeagle14-7B)\| 45.29\| 76.92\| 71.66\| 48.78\| 60.66\|
Locutusque/function-calling-chatml	Locutusque	2024-07-16T11:50:45Z	101	163	[ "license:apache-2.0", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-02-02T21:51:35Z	null	--- dataset_info: features: - name: system_message dtype: string - name: function_description dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 311913135 num_examples: 112960 download_size: 107035875 dataset_size: 311913135 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 --- # Dataset Card for "function-calling-chatml" Converted glaiveai/Glaive-function-calling-v2 to chatml format. ## Example entry ``` [ { "from": "system", "value": "You are a helpful assistant with access to the following functions. Use them if required -{\n \"name\": \"create_contact\",\n \"description\": \"Create a new contact\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"name\": {\n \"type\": \"string\",\n \"description\": \"The name of the contact\"\n },\n \"email\": {\n \"type\": \"string\",\n \"description\": \"The email address of the contact\"\n }\n },\n \"required\": [\n \"name\",\n \"email\"\n ]\n }\n}" }, { "from": "human", "value": "I need to create a new contact for my friend John Doe. His email is [email protected]." }, { "from": "function-call", "value": "{\"name\": \"create_contact\", \"arguments\": '{\"name\": \"John Doe\", \"email\": \"[email protected]\"}'}" }, { "from": "function-response", "value": "{\"status\": \"success\", \"message\": \"Contact for John Doe with email [email protected] has been created successfully.\"}" }, { "from": "gpt", "value": "I have successfully created a new contact for your friend John Doe with the email [email protected]." } ] ```
CAiRE/ASCEND	CAiRE	2024-07-16T08:56:04Z	376	35	[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:multilingual", "source_datasets:original", "language:en", "language:zh", "license:cc-by-sa-4.0", "size_categories:10K<n<100K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2112.06223", "region:us", "speech-recognition", "code-switching" ]	[ "automatic-speech-recognition" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - en - zh license: - cc-by-sa-4.0 multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - automatic-speech-recognition task_ids: [] pretty_name: 'ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation' tags: - speech-recognition - code-switching dataset_info: config_name: main features: - name: id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - name: duration dtype: float32 - name: language dtype: string - name: original_speaker_id dtype: int64 - name: session_id dtype: int64 - name: topic dtype: string splits: - name: train num_bytes: 1014573740.14 num_examples: 9869 - name: test num_bytes: 106171230.135 num_examples: 1315 - name: validation num_bytes: 106772517.43 num_examples: 1130 download_size: 1223536062 dataset_size: 1227517487.7050002 configs: - config_name: main data_files: - split: train path: main/train-* - split: test path: main/test-* - split: validation path: main/validation-* default: true --- # Dataset Card for ASCEND ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Usage](#usage) - [Dataset Structure](#dataset-structure) - [Data Splits](#data-instances) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - Homepage: [Needs More Information] - Repository: [Needs More Information] - Paper: https://arxiv.org/abs/2112.06223 - Leaderboard: [Needs More Information] - Point of Contact: [Needs More Information] ### Dataset Summary ASCEND (A Spontaneous Chinese-English Dataset) introduces a high-quality resource of spontaneous multi-turn conversational dialogue Chinese-English code-switching corpus collected in Hong Kong. ASCEND consists of 10.62 hours of spontaneous speech with a total of ~12.3K utterances. The corpus is split into 3 sets: training, validation, and test with a ratio of 8:1:1 while maintaining a balanced gender proportion on each set. ### Supported Tasks and Leaderboards Code-switching ### Languages Chinese and English ## Usage To obtain the full dataset (complete with train, validation, and test set), simply run this: ``` import datasets dataset = datasets.load_dataset("CAiRE/ASCEND") ``` ## Dataset Structure A typical data point comprises the path to the audio file, the loaded audio array, and its transcription. Additional fields include datapoint id, duration, language, speaker id, session id, and topic. ``` { 'id': '00644', 'path': '.cache/huggingface/datasets/downloads/extracted/f0b33b5266cd9452ee310eef3577cf7adb7f29aa54dbff74b9a8ee406a55d614/waves/ses2_spk3_L13101_189.900_5.490.wav', 'audio': { 'path': '.cache/huggingface/datasets/downloads/extracted/f0b33b5266cd9452ee310eef3577cf7adb7f29aa54dbff74b9a8ee406a55d614/waves/ses2_spk3_L13101_189.900_5.490.wav', 'array': array([-6.1035156e-05, -1.8310547e-04, 3.0517578e-05, ..., 0.0000000e+00, -3.0517578e-05, 0.0000000e+00 ], dtype = float32), 'sampling_rate': 16000 }, 'transcription': '因为你不可能邀你的female friends去说走我们去play basketball', 'duration': 5.489999771118164, 'language': 'mixed', 'original_speaker_id': 3, 'session_id': 2, 'topic': 'sports' } ``` ### Data Splits Number of utterances: 9,869 train, 1,130 validation, and 1,315 test. ## Additional Information For comprehensive explanations, please check [our paper](https://arxiv.org/pdf/2112.06223.pdf). ### Licensing Information Creative Common Attribution Share-Alike 4.0 International (CC-BY-SA 4.0) ### Citation Information If you use our dataset, please cite us: ``` @inproceedings{lovenia2022ascend, title={ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation}, author={Lovenia, Holy and Cahyawijaya, Samuel and Winata, Genta Indra and Xu, Peng and Yan, Xu and Liu, Zihan and Frieske, Rita and Yu, Tiezheng and Dai, Wenliang and Barezi, Elham J and others}, booktitle={Proceedings of the 13th Language Resources and Evaluation Conference (LREC)}, year={2022} ```
b3x0m/Chinese-H-Novels	b3x0m	2024-07-12T02:32:57Z	1,036	211	[ "task_categories:text-classification", "task_categories:summarization", "task_categories:token-classification", "task_categories:text2text-generation", "task_categories:question-answering", "task_categories:text-generation", "task_categories:fill-mask", "task_categories:sentence-similarity", "language:zh", "size_categories:100M<n<1B", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "art" ]	[ "text-classification", "summarization", "token-classification", "text2text-generation", "question-answering", "text-generation", "fill-mask", "sentence-similarity" ]	2023-11-27T17:19:10Z	null	--- language: - zh size_categories: - 1B<n<10B task_categories: - text-classification - summarization - token-classification - text2text-generation - question-answering - text-generation - fill-mask - sentence-similarity pretty_name: H-novel-corpus tags: - art dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 95784400372 num_examples: 934354429 download_size: 60873072258 dataset_size: 95784400372 configs: - config_name: default data_files: - split: train path: data/train-* --- Update 12/07/2024: convert to parquet to download easier. Chinese 18+ novels corpus, use at your own risk, you and only you are responsible for every choice you make. (͡ ° ͜ʖ ͡ °) tags: socks, garter belt, foot fetish, ntr, netori..... Thanks [Moleys/Numeron](https://huggingface.co/Numeronvr) for the dataset donation.
nyu-visionx/Cambrian-10M	nyu-visionx	2024-07-08T04:34:51Z	9,177	113	[ "task_categories:visual-question-answering", "task_categories:question-answering", "language:en", "license:apache-2.0", "size_categories:1M<n<10M", "arxiv:2406.16860", "region:us" ]	[ "visual-question-answering", "question-answering" ]	2024-05-30T03:27:31Z	null	--- task_categories: - visual-question-answering - question-answering language: - en size_categories: - 1M<n<10M license: apache-2.0 --- # Cambrian-10M Dataset Please see paper & website for more information: - https://cambrian-mllm.github.io/ - https://arxiv.org/abs/2406.16860 ## Overview Cambrian-10M is a comprehensive dataset designed for instruction tuning, particularly in multimodal settings involving visual interaction data. The dataset is crafted to address the scarcity of high-quality multimodal instruction-tuning data and to maintain the language abilities of multimodal large language models (LLMs). ## Data Collection ### Multimodal Data Sources Unlike language data, multimodal instruction-tuning data is much rarer and harder to collect. To address this, we leverage existing multimodal benchmarks and datasets involving visual interaction data, such as Visual Question Answering (VQA) and Optical Character Recognition (OCR) data. This approach helps mitigate the catastrophic forgetting commonly observed when fine-tuning multimodal LLMs. ### Language-Only Instruction-Following Data To ensure the preservation of language capabilities, we also collect a small volume of high-quality language-only instruction-following data from the community. ### Targeted Internet Data Collection Engine We introduce a data engine designed to create large-scale, reliable, high-quality knowledge-based multimodal instruction tuning data. The engine works as follows: 1. Field and Subfield Selection: The engine selects a target field and subfield, such as “Physics”. 2. Topic Identification: An LLM like GPT-4 identifies topics within the field (e.g., “Newton’s Laws”). 3. Reliable Source Search: The engine searches reliable sources like Wikipedia for each topic. 4. Text-Image Association Extraction: The parser extracts image-caption-text tuples from the sources. 5. Q&A Pair Generation: The caption-text is fed to an LLM, such as GPT-3.5, to generate instruction-type Q&A pairs about the image. These Q&A pairs, along with the images, form our VQA dataset. ### GPT Rewriting We also incorporate recent MLLMs such as GPT-4v and GPT-4o to generate extended responses and free-form instruction tuning data. To play with gpt generated data, use [gpt4v_77k](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/resolve/main/jsons/gpt4v_77k.jsonl), Curated [gpt4o_60k](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/resolve/main/jsons/gpt4o_60k.jsonl) - [gpt4v_77k](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/resolve/main/jsons/gpt4v_77k.jsonl) contains more extended responses from Cambrian-10M. - [gpt4o_60k](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/resolve/main/jsons/gpt4o_60k.jsonl) contains more creative data in visual interactions. ## Cambrian-10M Composition The Cambrian-10M dataset consists of approximately 9.784 million data points, offering a diverse range of data for various research applications. The composition of the dataset is visualized in Fig. 9. ## Cambrian-7M We make an initial effort to study data curation. In particular, we find the following data ratio to perform most optimally - Language: 21.00% - General: 34.52% - OCR: 27.22% - Counting: 8.71% - Math: 7.20% - Code: 0.87% - Science: 0.88% ![Cambrian-7M](cambrian7m.png) ## Getting Started with Cambrian Data Before you start, ensure you have sufficient storage space to download and process the data. Cambrian-10M contains a total of 10 million images collected from previous datasets, an internet data engine, and GPT-generated instruction tuning data. Follow these steps to get started: 1. Download the Data Repository Download the data repository. Note that due to Hugging Face policy constraints, the data folder is archived into tar files. We also split the `allava` and `data_engine` data into smaller tar files because they exceed the 50 GB size limit. 2. Merge Tar Files To explore the Cambrian-10M dataset, first merge the different parts of `allava` and `data_engine` together: ```bash python merge_tars.py ``` 3. Extract Tar Files Then, extract all the tar files into the current directory: ```bash python extract.py ``` 4. Training with Cambrian You can train with the raw [Cambrian10M](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/resolve/main/jsons/Cambrian10M.jsonl), Curated [Cambrian7M](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/resolve/main/jsons/Cambrian7M.jsonl). We recommend using the Curated [Cambrian7M with system prompt](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/blob/main/jsons/Cambrian7M_withsystemprompt.jsonl) that also alleviates 'answer machine' problem.
SetFit/bbc-news	SetFit	2024-07-04T13:00:48Z	886	16	[ "task_categories:text-classification", "language:en", "size_categories:1K<n<10K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	1	--- task_categories: - text-classification language: - en pretty_name: BBC News Topic Dataset --- # BBC News Topic Dataset Dataset on [BBC News Topic Classification](https://www.kaggle.com/yufengdev/bbc-text-categorization/data) consisting of 2,225 articles published on the BBC News website corresponding during 2004-2005. Each article is labeled under one of 5 categories: business, entertainment, politics, sport or tech. Original source for this dataset: - Derek Greene, Pádraig Cunningham, “Practical Solutions to the Problem of Diagonal Dominance in Kernel Document Clustering,” in Proc. 23rd International Conference on Machine learning (ICML’06), 2006 [Raw dataset](http://mlg.ucd.ie/datasets/bbc.html) - [Paper PDF](http://derekgreene.com/papers/greene06icml.pdf) All rights, including copyright, in the content of the original articles are owned by the BBC.
lmms-lab/Video-MME	lmms-lab	2024-07-04T08:14:20Z	17,892	40	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "modality:video", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-06-07T12:06:37Z	null	--- dataset_info: config_name: videomme features: - name: video_id dtype: string - name: duration dtype: string - name: domain dtype: string - name: sub_category dtype: string - name: url dtype: string - name: videoID dtype: string - name: question_id dtype: string - name: task_type dtype: string - name: question dtype: string - name: options sequence: string - name: answer dtype: string splits: - name: test num_bytes: 1003241.0 num_examples: 2700 download_size: 405167 dataset_size: 1003241.0 configs: - config_name: videomme data_files: - split: test path: videomme/test-* ---
agentsea/wave-ui-25k	agentsea	2024-07-03T04:13:40Z	1,118	32	[ "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-06-28T18:38:08Z	2	--- dataset_info: features: - name: image dtype: image - name: instruction dtype: string - name: bbox sequence: float64 - name: resolution sequence: int64 - name: source dtype: string - name: platform dtype: string - name: name dtype: string - name: description dtype: string - name: type dtype: string - name: OCR dtype: string - name: language dtype: string - name: purpose dtype: string - name: expectation dtype: string splits: - name: train num_bytes: 10884587492.75 num_examples: 24978 download_size: 10577935056 dataset_size: 10884587492.75 configs: - config_name: default data_files: - split: train path: data/train-* --- # WaveUI-25k This dataset contains 25k examples of labeled UI elements. It is a subset of a collection of ~80k preprocessed examples assembled from the following sources: - [WebUI](https://uimodeling.github.io/) - [RoboFlow](https://universe.roboflow.com/roboflow-gw7yv/website-screenshots) - [GroundUI-18K](https://huggingface.co/datasets/agent-studio/GroundUI-18K) These datasets were preprocessed to have matching schemas and to filter out unwanted examples, such as duplicated, overlapping and low-quality datapoints. We also filtered out many text elements which were not in the main scope of this work. The WaveUI-25k dataset includes the original fields from the source datasets, as well as the following additional fields, obtained from the annotation process: - `name`: A descriptive name of the element. - `description`: A long detailed description of the element - `type`: The type of the element. - `OCR`: OCR of the element. Set to `null` if no text is available. - `language`: The language of the OCR text, if available. Set to `null` if no text is available. - `purpose`: A general purpose of the element. - `expectation`: An expectation on what will happen when you click this element. The dataset can be visualized better in [this space](https://huggingface.co/spaces/agentsea/wave-ui-viz). ## Data sources - [WebUI](https://uimodeling.github.io/) ```bibtex @article{wu2023webui, title={WebUI: A Dataset for Enhancing Visual UI Understanding with Web Semantics}, author={Jason Wu and Siyan Wang and Siman Shen and Yi-Hao Peng and Jeffrey Nichols and Jeffrey Bigham}, journal={ACM Conference on Human Factors in Computing Systems (CHI)}, year={2023} } ``` - [RoboFlow](https://universe.roboflow.com/roboflow-gw7yv/website-screenshots) ```bibtex @misc{ website-screenshots_dataset, title = { Website Screenshots Dataset }, type = { Open Source Dataset }, author = { Brad Dwyer }, howpublished = { \url{ https://universe.roboflow.com/roboflow-gw7yv/website-screenshots } }, url = { https://universe.roboflow.com/roboflow-gw7yv/website-screenshots }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { aug }, note = { visited on 2024-07-02 }, } ``` - [GroundUI-18K](https://huggingface.co/datasets/agent-studio/GroundUI-18K) ```bibtex @article{zheng2024agentstudio, title={AgentStudio: A Toolkit for Building General Virtual Agents}, author={Longtao Zheng and Zhiyuan Huang and Zhenghai Xue and Xinrun Wang and Bo An and Shuicheng Yan}, journal={arXiv preprint arXiv:2403.17918}, year={2024} } ```
QuietImpostor/Claude-3-Opus-Claude-3.5-Sonnnet-9k	QuietImpostor	2024-06-30T20:43:11Z	90	22	[ "task_categories:text-generation", "size_categories:1K<n<10K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[ "text-generation" ]	2024-06-30T20:39:49Z	2	--- task_categories: - text-generation pretty_name: Claude 3 Opus + Claude 3.5 Sonnet Ultrachat ShareGPT size_categories: - 1K<n<10K --- # Overview This dataset is a combination of samples from Sao10k's original Claude 3 Opus dataset and a personally created Claude 3.5 Sonnet dataset. Due to budget constraints, approximately 700 samples are from Claude 3.5 Sonnet, with the remainder sourced from the Claude 3 Opus dataset.
nnenufar/speakerVerification_PTBR	nnenufar	2024-06-29T14:20:34Z	41,704	0	[ "task_categories:audio-classification", "language:pt", "license:apache-2.0", "size_categories:10K<n<100K", "modality:audio", "region:us" ]	[ "audio-classification" ]	2024-06-27T01:07:59Z	null	--- license: apache-2.0 task_categories: - audio-classification language: - pt size_categories: - 10K<n<100K --- # Dataset card <!-- Provide a quick summary of the dataset. --> This dataset includes ~80k samples of speech audio in Brazilian Portuguese. Samples have variable length ranging from 1 to 4 seconds, with a sampling rate of 16kHz. The metadata file includes speaker tags and corresponding labels for each sample, making it appropriate for speaker identification and speaker verification tasks. ## Dataset Description Audio samples are taken from three bigger corpora: C-ORAL Brasil, NURC Recife and NURC SP. Please take into consideration the licenses details of the original corpora. All the recordings come from monologues, but there are brief moments of interruption by the researchers that conducted the recordings. The recordings comprise accents from three different states of Brazil: Belo Horizonte, São Paulo and Recife. The dataset is structured in a way that makes it possible to load with HF Audiofolder. In my experience, I noticed that it is easier to clone the dataset repo to your machine and then load it specifying the local data_dir than to load it directly from the hub using the dataset tag. ## Dataset Sources <!-- Provide the basic links for the dataset. --> - C-ORAL BRASIL: [https://www.c-oral-brasil.org/c-oral-brasil-i.php] - NURC Recife: [https://fale.ufal.br/projeto/nurcdigital/index.php?action=home] - NURC SP: [http://tarsila.icmc.usp.br:8080/nurc/home] ## Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> The models trained with this dataset should not be used to intentionally create hostile or alienating environments for people.
ncbi/MedCalc-Bench-v1.0	ncbi	2024-06-26T20:51:59Z	501	13	[ "license:cc-by-sa-4.0", "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-06-12T12:44:02Z	2	--- license: cc-by-sa-4.0 dataset_info: features: - name: Row Number dtype: int64 - name: Calculator ID dtype: int64 - name: Calculator Name dtype: string - name: Category dtype: string - name: Output Type dtype: string - name: Note ID dtype: string - name: Note Type dtype: string - name: Patient Note dtype: string - name: Question dtype: string - name: Relevant Entities dtype: string - name: Ground Truth Answer dtype: string - name: Lower Limit dtype: string - name: Upper Limit dtype: string - name: Ground Truth Explanation dtype: string splits: - name: train num_bytes: 41265322 num_examples: 10053 - name: test num_bytes: 4043748 num_examples: 1047 download_size: 19670625 dataset_size: 45309070 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- MedCalc-Bench is the first medical calculation dataset used to benchmark LLMs ability to serve as clinical calculators. Each instance in the dataset consists of a patient note, a question asking to compute a specific clinical value, an final answer value, and a step-by-step solution explaining how the final answer was obtained. Our dataset covers 55 different calculation tasks. We hope this dataset serves as a call to improve the verbal and computational reasoning skills of LLMs in medical settings. This dataset contains a training dataset of 10,053 instances and a testing dataset of 1,047 instances. ## Contents inside the Training and Testing CSV To download the CSV for the MedCalc-Bench evaluation dataset, please download the file, test_data.csv inside the dataset folder of this repository. You can also download the test set split from HuggingFace at https://huggingface.co/datasets/ncbi/MedCalc-Bench. In addition to the 1,047 evaluation instances, we also provide a training dataset of 10,053 instances which can be used for fine-tuning open-source LLMs (see Section C of the Appendix). Each Instance in the dataset contains the following information: - Row Number: Specifies the index of the instance. - Calculator ID: Specifies the integer ID of the calculator. - Calculator Name: Specifies the name of the clinical calculation task. - Category: Specifies the sub-category of the calculator. For equation-based calculators, the options are lab test, dosage, date, or physical and for rule-based calculators, the options are risk, severity, and diagnosis. - Output Type: Specifies the format type that the calculator will return. The options are decimal, integer, date (MM/DD/YY), or time in terms of weeks and days (i.e. (17 weeks, 4 days)). - Note ID: Specifies the ID of the patient note. The ID of the note will either be the ID given by Open-Patients or it will be an integer value if the patient note was handwritten by clinicians or synthesized by a template. - Note Type: Specifies whether the patient note was synthesized by a clinician (Handwritten), produced from a template (Template), or was extracted from PMC-Patients (extracted). - Patient Note: Specifies the patient note which provides the information needed to compute the final answer. - Question: Specifies the question that is asked to the model to compute a specific medical value based on a particular calculator. - Relevant Entities: Provides a dictionary of the parameters and their extracted values based on the patient note. - Ground Truth Answer: Specifies the ground truth value without any units for the medical value that needs to be calculated. - Lower Limit: For equation-based calculators whose output is a decimal, this value is 95% of the ground truth answer value. For all other cases, the lower limit is the same as the ground-truth value. - Upper Limit: For equation-based calculators whose output is a decimal, this value is 105% of the ground truth answer value. For all other cases, the upper limit is the same as the ground-truth value. - Ground Truth Explanation: The ground truth explanation for the data instance providing a step-by-step explanation for how the final answer was obtained. ## How to Use MedCalc-Bench The training dataset of MedCalc-Bench can be used for fine-tunining LLMs. We have provided both the fine-tuned models and code for fine-tuning at our repository link: https://github.com/ncbi-nlp/MedCalc-Bench. The test set of MedCalc-Bench is helpful for benchamrking LLMs under different settings. We provide instructions in the README of this repository for how to reproduce all of our results for all of the models using the different prompt settings. By experimenting with different LLMs and prompts, we hope our dataset demonstrates the potential and limitations of LLMs in clinical settings. ## License Both the training and testing dataset of MedCalc-Bench are released under the CC-BY-SA 4.0 license.
PleIAs/YouTube-Commons	PleIAs	2024-06-26T08:08:14Z	763	346	[ "task_categories:text-generation", "language:en", "language:fr", "language:es", "language:pt", "language:de", "language:ru", "license:cc-by-4.0", "region:us", "conversational" ]	[ "text-generation" ]	2024-04-15T22:12:27Z	null	--- language: - en - fr - es - pt - de - ru license: cc-by-4.0 task_categories: - text-generation pretty_name: Youtube Commons Corpus tags: - conversational dataset_info: features: - name: video_id dtype: string - name: video_link dtype: string - name: title dtype: string - name: text dtype: string - name: channel dtype: string - name: channel_id dtype: string - name: date dtype: string - name: license dtype: string - name: original_language dtype: string - name: source_language dtype: string - name: transcription_language dtype: string - name: word_count dtype: int64 - name: character_count dtype: int64 splits: - name: train num_bytes: 3284822536 num_examples: 250000 download_size: 1830819739 dataset_size: 3284822536 configs: - config_name: default data_files: - split: train path: data/train-* --- # 📺 YouTube-Commons 📺 YouTube-Commons is a collection of audio transcripts of 2,063,066 videos shared on YouTube under a CC-By license. ## Content The collection comprises 22,709,724 original and automatically translated transcripts from 3,156,703 videos (721,136 individual channels). In total, this represents nearly 45 billion words (44,811,518,375). All the videos where shared on YouTube with a CC-BY license: the dataset provide all the necessary provenance information including the title, link, channel name and upload date. The corpus is multilingual with a majority of English-speaking content (71%) for original languages. Automated translations are provided for nearly all the videos in English, French, Spanish, German, Russian, Italian and Dutch. ## Uses The collection aims to expand the availability of conversational data for research in AI, computational social science and digital humanities. Most of the available resources under free licenses are written texts such as public domain works or open science articles. The text can be used for training model and republished with for reproducibility purposes. ## License and ethics All the transcripts are part of a video shared under a CC-By license. In accordance with the provision of the license, every YouTube channels is fully credited. While content under a free license can be lawfully reproduced in any setting, there is currently a debate over the legitimacy and proper ethical use of free content for pre-training large language models. In accordance with the philosophy of Creative Commons, we recommend that this set be preferably used for open research. Furthermore, the license requires that contribution of each individual author is properly credited. In a research context, the best way to achieve this aim would be to fully release the data sources used for training or, at the very least, provide an extensive open documentation. ## Future developments The collection is far from covering the total amount of available YouTube videos under a Creative Commons license. We will continue to expand it significantly. Other additional release will also focus on transcripts from other video sources not available on YouTube (especially from public service/university websites). ## Acknowledgements The corpus was stored and processed with the generous support of Scaleway. It was built up with the support and concerted efforts of the state start-up LANGU:IA (start-up d’Etat), supported by the French Ministry of Culture and DINUM, as part of the prefiguration of the service offering of the Alliance for Language technologies EDIC (ALT-EDIC). Pleias corpus collection projects have been also facilitated thanks to the open science LLM community support, insights and cooperation (Occiglot, Eleuther AI, Allen AI). <div style="text-align: center;"> <img src="https://github.com/mch-dd/datasetlogo/blob/main/scaleway.jpeg?raw=true" style="width: 33%; margin: 0 auto; display: inline-block;"/> <img src="https://github.com/mch-dd/datasetlogo/blob/main/ministere.png?raw=true" style="width: 33%; margin: 0 auto; display: inline-block;"/> <img src="https://github.com/mch-dd/datasetlogo/blob/main/occiglot.jpg?raw=true" style="width: 33%; margin: 0 auto; display: inline-block;"/> </div>
community-datasets/urdu_fake_news	community-datasets	2024-06-26T07:46:11Z	114	2	[ "task_categories:text-classification", "task_ids:fact-checking", "task_ids:intent-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:ur", "license:unknown", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - ur license: - unknown multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - text-classification task_ids: - fact-checking - intent-classification pretty_name: Bend the Truth (Urdu Fake News) dataset_info: features: - name: news dtype: string - name: label dtype: class_label: names: '0': Fake '1': Real - name: category dtype: class_label: names: '0': bus '1': hlth '2': sp '3': tch '4': sbz splits: - name: train num_bytes: 1762901 num_examples: 638 - name: test num_bytes: 799583 num_examples: 262 download_size: 1187921 dataset_size: 2562484 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for Bend the Truth (Urdu Fake News) ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Github](https://github.com/MaazAmjad/Datasets-for-Urdu-news/) - Repository: [Github](https://github.com/MaazAmjad/Datasets-for-Urdu-news/) - Paper: - Leaderboard: - Point of Contact: [Maaz Amjad](https://github.com/MaazAmjad) ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - news: a string in urdu - label: the label indicating whethere the provided news is real or fake. - category: The intent of the news being presented. The available 5 classes are Sports, Health, Technology, Entertainment, and Business. ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@chaitnayabasava](https://github.com/chaitnayabasava) for adding this dataset.
community-datasets/tamilmixsentiment	community-datasets	2024-06-26T07:04:23Z	94	4	[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:multilingual", "source_datasets:original", "language:en", "language:ta", "license:unknown", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - en - ta license: - unknown multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: Tamilmixsentiment dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': Positive '1': Negative '2': Mixed_feelings '3': unknown_state '4': not-Tamil splits: - name: train num_bytes: 790124 num_examples: 11335 - name: validation num_bytes: 89614 num_examples: 1260 - name: test num_bytes: 218760 num_examples: 3149 download_size: 708889 dataset_size: 1098498 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # Dataset Card for Tamilmixsentiment ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Tamilmixsentiment Homepage](https://dravidian-codemix.github.io/2020/index.html) - Repository: [Tamilmixsentiment repository](https://dravidian-codemix.github.io/2020/datasets.html) - Paper: [Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text](https://www.aclweb.org/anthology/2020.sltu-1.28/) - Leaderboard: [Rank list](https://drive.google.com/file/d/1Mf8-No-63koGRwdF13RrO01NAFBlNmI0/view?usp=sharing) - Point of Contact: [Bharathi Raja Chakravarthi](mailto:[email protected]) ### Dataset Summary The first gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. This makes the largest general domain sentiment dataset for this relatively low-resource language with code-mixing phenomenon. The comment/post may contain more than one sentence but the average sentence length of the corpora is 1. Each comment/post is annotated with sentiment polarity at the comment/post level. This dataset also has class imbalance problems depicting real-world scenarios. ### Supported Tasks and Leaderboards To identify sentiment polarity of the code-mixed dataset of comments/posts in Tamil-English collected from social media. ### Languages Tamil-English code-switched. The dataset contains all the three types of code-mixed sentences - Inter-Sentential switch, Intra-Sentential switch and Tag switching. Most comments were written in Roman script with either Tamil grammar with English lexicon or English grammar with Tamil lexicon. Some comments were written in Tamil script with English expressions in between. ## Dataset Structure ### Data Instances An example from the Tamilmixsentiment train set looks as follows: ``` text label Trailer late ah parthavanga like podunga Positive ``` ### Data Fields - `text`: Tamil-English code-mixed comment. - `label`: list of the possible sentiments "Positive", "Negative", "Mixed_feelings", "unknown_state", "not-Tamil" ### Data Splits The entire dataset of 15,744 sentences was randomly shuffled and split into three parts as follows: \| \| train \| validation \| test \| \|------------------------------\|------:\|-----------:\|-----:\| \| Tamilmixsentiment \| 11335 \| 1260 \| 3149 \| ## Dataset Creation ### Curation Rationale Sentiment analysis has become important in social media research (Yang and Eisenstein, 2017). Until recently these applications were created for high-resourced languages which analysed monolingual utterances. But social media in multilingual communities contains more code-mixed text. Code-mixing is common among speakers in a bilingual speech community. As English is seen as the language of prestige and education, the influence of lexicon, connectives and phrases from English language is common in spoken Tamil. Tamil has little annotated data for code-mixed scenarios. An annotated corpus developed for monolingual data cannot deal with code-mixed usage and therefore it fails to yield good results due to mixture of languages at different levels of linguistic analysis. Therefore this dataset of code-mixed Tamil-English sentiment annotated corpus is created. ### Source Data #### Initial Data Collection and Normalization The data was scraped from Youtube. In total 184,573 sentences for Tamil from YouTube comments from the trailers of a movies released in 2019. Many of the them contained sentences that were either entirely written in English or code-mixed Tamil-English or fully written in Tamil. So we filtered out a non-code-mixed corpus based on language identification at comment level using the langdetect library. The comment is written fully in Tamil or English, we discarded that comment since monolingual resources are available for these languages. We also identified if the sentences were written in other languages such as Hindi, Malayalam, Urdu, Telugu, and Kannada. We preprocessed the comments by removing the emoticons and applying a sentence length filter. We want to create a code-mixed corpus of reasonable size with sentences that have fairly defined sentiments which will be useful for future research. Thus our filter removed sentences with less than five words and more than 15 words after cleaning the data. In the end we got 15,744 Tanglish sentences. #### Who are the source language producers? Youtube users ### Annotations #### Annotation process Three steps complete the annotation setup. First, each sentence was annotated by two people. In the second step, the data were collected if both of them agreed. In the case of conflict, a third person annotated the sentence. In the third step, if all the three of them did not agree, then two more annotators annotated the sentences. #### Who are the annotators? Eleven volunteers were involved in the process. All of them were native speakers of Tamil with diversity in gender, educational level and medium of instruction in their school education. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{chakravarthi-etal-2020-corpus, title = "Corpus Creation for Sentiment Analysis in Code-Mixed {T}amil-{E}nglish Text", author = "Chakravarthi, Bharathi Raja and Muralidaran, Vigneshwaran and Priyadharshini, Ruba and McCrae, John Philip", booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources association", url = "https://www.aclweb.org/anthology/2020.sltu-1.28", pages = "202--210", abstract = "Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark.", language = "English", ISBN = "979-10-95546-35-1", } ``` ### Contributions Thanks to [@jamespaultg](https://github.com/jamespaultg) for adding this dataset.
community-datasets/senti_ws	community-datasets	2024-06-26T06:37:47Z	105	2	[ "task_categories:token-classification", "task_categories:text-classification", "task_ids:text-scoring", "task_ids:sentiment-scoring", "task_ids:part-of-speech", "annotations_creators:expert-generated", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:de", "license:cc-by-sa-3.0", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[ "token-classification", "text-classification" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - expert-generated - machine-generated language_creators: - found language: - de license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification - text-classification task_ids: - text-scoring - sentiment-scoring - part-of-speech pretty_name: SentiWS dataset_info: - config_name: pos-tagging features: - name: word dtype: string - name: pos-tag dtype: class_label: names: '0': NN '1': VVINF '2': ADJX '3': ADV splits: - name: train num_bytes: 75526 num_examples: 3471 download_size: 37314 dataset_size: 75526 - config_name: sentiment-scoring features: - name: word dtype: string - name: sentiment-score dtype: float32 splits: - name: train num_bytes: 61642 num_examples: 3471 download_size: 45116 dataset_size: 61642 configs: - config_name: pos-tagging data_files: - split: train path: pos-tagging/train-* default: true - config_name: sentiment-scoring data_files: - split: train path: sentiment-scoring/train-* --- # Dataset Card for SentiWS ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://wortschatz.uni-leipzig.de/en/download - Repository: [Needs More Information] - Paper: http://www.lrec-conf.org/proceedings/lrec2010/pdf/490_Paper.pdf - Leaderboard: [Needs More Information] - Point of Contact: [Needs More Information] ### Dataset Summary SentimentWortschatz, or SentiWS for short, is a publicly available German-language resource for sentiment analysis, opinion mining etc. It lists positive and negative polarity bearing words weighted within the interval of [-1; 1] plus their part of speech tag, and if applicable, their inflections. The current version of SentiWS contains around 1,650 positive and 1,800 negative words, which sum up to around 16,000 positive and 18,000 negative word forms incl. their inflections, respectively. It not only contains adjectives and adverbs explicitly expressing a sentiment, but also nouns and verbs implicitly containing one. ### Supported Tasks and Leaderboards Sentiment-Scoring, Pos-Tagging ### Languages German ## Dataset Structure ### Data Instances For pos-tagging: ``` { "word":"Abbau" "pos_tag": 0 } ``` For sentiment-scoring: ``` { "word":"Abbau" "sentiment-score":-0.058 } ``` ### Data Fields SentiWS is UTF8-encoded text. For pos-tagging: - word: one word as a string, - pos_tag: the part-of-speech tag of the word as an integer, For sentiment-scoring: - word: one word as a string, - sentiment-score: the sentiment score of the word as a float between -1 and 1, The POS tags are ["NN", "VVINF", "ADJX", "ADV"] -> ["noun", "verb", "adjective", "adverb"], and positive and negative polarity bearing words are weighted within the interval of [-1, 1]. ### Data Splits train: 1,650 negative and 1,818 positive words ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License ### Citation Information @INPROCEEDINGS{remquahey2010, title = {SentiWS -- a Publicly Available German-language Resource for Sentiment Analysis}, booktitle = {Proceedings of the 7th International Language Resources and Evaluation (LREC'10)}, author = {Remus, R. and Quasthoff, U. and Heyer, G.}, year = {2010} } ### Contributions Thanks to [@harshalmittal4](https://github.com/harshalmittal4) for adding this dataset.
community-datasets/qanta	community-datasets	2024-06-26T06:06:55Z	50,006	4	[ "task_categories:question-answering", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:unknown", "size_categories:1M<n<10M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:1904.04792", "region:us", "quizbowl" ]	[ "question-answering" ]	2022-03-02T23:29:22Z	null	--- annotations_creators: - machine-generated language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - question-answering task_ids: [] paperswithcode_id: quizbowl pretty_name: Quizbowl tags: - quizbowl dataset_info: - config_name: mode=first,char_skip=25 features: - name: id dtype: string - name: qanta_id dtype: int32 - name: proto_id dtype: string - name: qdb_id dtype: int32 - name: dataset dtype: string - name: text dtype: string - name: full_question dtype: string - name: first_sentence dtype: string - name: char_idx dtype: int32 - name: sentence_idx dtype: int32 - name: tokenizations sequence: sequence: int32 length: 2 - name: answer dtype: string - name: page dtype: string - name: raw_answer dtype: string - name: fold dtype: string - name: gameplay dtype: bool - name: category dtype: string - name: subcategory dtype: string - name: tournament dtype: string - name: difficulty dtype: string - name: year dtype: int32 splits: - name: guesstrain num_bytes: 117599150 num_examples: 96221 - name: buzztrain num_bytes: 19699616 num_examples: 16706 - name: guessdev num_bytes: 1414822 num_examples: 1055 - name: buzzdev num_bytes: 1553576 num_examples: 1161 - name: guesstest num_bytes: 2997063 num_examples: 2151 - name: buzztest num_bytes: 2653365 num_examples: 1953 - name: adversarial num_bytes: 1258784 num_examples: 1145 download_size: 90840024 dataset_size: 147176376 - config_name: mode=full,char_skip=25 features: - name: id dtype: string - name: qanta_id dtype: int32 - name: proto_id dtype: string - name: qdb_id dtype: int32 - name: dataset dtype: string - name: text dtype: string - name: full_question dtype: string - name: first_sentence dtype: string - name: char_idx dtype: int32 - name: sentence_idx dtype: int32 - name: tokenizations sequence: sequence: int32 length: 2 - name: answer dtype: string - name: page dtype: string - name: raw_answer dtype: string - name: fold dtype: string - name: gameplay dtype: bool - name: category dtype: string - name: subcategory dtype: string - name: tournament dtype: string - name: difficulty dtype: string - name: year dtype: int32 splits: - name: guesstrain num_bytes: 168874612 num_examples: 96221 - name: buzztrain num_bytes: 27989445 num_examples: 16706 - name: guessdev num_bytes: 2098857 num_examples: 1055 - name: buzzdev num_bytes: 2301145 num_examples: 1161 - name: guesstest num_bytes: 4434626 num_examples: 2151 - name: buzztest num_bytes: 3930150 num_examples: 1953 - name: adversarial num_bytes: 1799969 num_examples: 1145 download_size: 133005755 dataset_size: 211428804 - config_name: mode=runs,char_skip=25 features: - name: id dtype: string - name: qanta_id dtype: int32 - name: proto_id dtype: string - name: qdb_id dtype: int32 - name: dataset dtype: string - name: text dtype: string - name: full_question dtype: string - name: first_sentence dtype: string - name: char_idx dtype: int32 - name: sentence_idx dtype: int32 - name: tokenizations sequence: sequence: int32 length: 2 - name: answer dtype: string - name: page dtype: string - name: raw_answer dtype: string - name: fold dtype: string - name: gameplay dtype: bool - name: category dtype: string - name: subcategory dtype: string - name: tournament dtype: string - name: difficulty dtype: string - name: year dtype: int32 splits: - name: guesstrain num_bytes: 3975570298 num_examples: 2641161 - name: buzztrain num_bytes: 622976884 num_examples: 433552 - name: guessdev num_bytes: 55281178 num_examples: 33602 - name: buzzdev num_bytes: 60226416 num_examples: 36803 - name: guesstest num_bytes: 120192213 num_examples: 70772 - name: buzztest num_bytes: 104422131 num_examples: 63050 - name: adversarial num_bytes: 37874827 num_examples: 27986 download_size: 306157359 dataset_size: 4976543947 - config_name: mode=sentences,char_skip=25 features: - name: id dtype: string - name: qanta_id dtype: int32 - name: proto_id dtype: string - name: qdb_id dtype: int32 - name: dataset dtype: string - name: text dtype: string - name: full_question dtype: string - name: first_sentence dtype: string - name: char_idx dtype: int32 - name: sentence_idx dtype: int32 - name: tokenizations sequence: sequence: int32 length: 2 - name: answer dtype: string - name: page dtype: string - name: raw_answer dtype: string - name: fold dtype: string - name: gameplay dtype: bool - name: category dtype: string - name: subcategory dtype: string - name: tournament dtype: string - name: difficulty dtype: string - name: year dtype: int32 splits: - name: guesstrain num_bytes: 629450237 num_examples: 505321 - name: buzztrain num_bytes: 98941633 num_examples: 82574 - name: guessdev num_bytes: 9112676 num_examples: 6818 - name: buzzdev num_bytes: 9924887 num_examples: 7451 - name: guesstest num_bytes: 19470155 num_examples: 14069 - name: buzztest num_bytes: 17011859 num_examples: 12610 - name: adversarial num_bytes: 6491504 num_examples: 5812 download_size: 150604036 dataset_size: 790402951 configs: - config_name: mode=first,char_skip=25 data_files: - split: guesstrain path: mode=first,char_skip=25/guesstrain-* - split: buzztrain path: mode=first,char_skip=25/buzztrain-* - split: guessdev path: mode=first,char_skip=25/guessdev-* - split: buzzdev path: mode=first,char_skip=25/buzzdev-* - split: guesstest path: mode=first,char_skip=25/guesstest-* - split: buzztest path: mode=first,char_skip=25/buzztest-* - split: adversarial path: mode=first,char_skip=25/adversarial-* - config_name: mode=full,char_skip=25 data_files: - split: guesstrain path: mode=full,char_skip=25/guesstrain-* - split: buzztrain path: mode=full,char_skip=25/buzztrain-* - split: guessdev path: mode=full,char_skip=25/guessdev-* - split: buzzdev path: mode=full,char_skip=25/buzzdev-* - split: guesstest path: mode=full,char_skip=25/guesstest-* - split: buzztest path: mode=full,char_skip=25/buzztest-* - split: adversarial path: mode=full,char_skip=25/adversarial-* - config_name: mode=runs,char_skip=25 data_files: - split: guesstrain path: mode=runs,char_skip=25/guesstrain-* - split: buzztrain path: mode=runs,char_skip=25/buzztrain-* - split: guessdev path: mode=runs,char_skip=25/guessdev-* - split: buzzdev path: mode=runs,char_skip=25/buzzdev-* - split: guesstest path: mode=runs,char_skip=25/guesstest-* - split: buzztest path: mode=runs,char_skip=25/buzztest-* - split: adversarial path: mode=runs,char_skip=25/adversarial-* - config_name: mode=sentences,char_skip=25 data_files: - split: guesstrain path: mode=sentences,char_skip=25/guesstrain-* - split: buzztrain path: mode=sentences,char_skip=25/buzztrain-* - split: guessdev path: mode=sentences,char_skip=25/guessdev-* - split: buzzdev path: mode=sentences,char_skip=25/buzzdev-* - split: guesstest path: mode=sentences,char_skip=25/guesstest-* - split: buzztest path: mode=sentences,char_skip=25/buzztest-* - split: adversarial path: mode=sentences,char_skip=25/adversarial-* --- # Dataset Card for "qanta" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [http://www.qanta.org/](http://www.qanta.org/) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [Quizbowl: The Case for Incremental Question Answering](https://arxiv.org/abs/1904.04792) - Point of Contact: [Jordan Boyd-Graber](mailto:[email protected]) - Size of downloaded dataset files: 170.75 MB - Size of the generated dataset: 147.18 MB - Total amount of disk used: 317.93 MB ### Dataset Summary The Qanta dataset is a question answering dataset based on the academic trivia game Quizbowl. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### mode=first,char_skip=25 - Size of downloaded dataset files: 170.75 MB - Size of the generated dataset: 147.18 MB - Total amount of disk used: 317.93 MB An example of 'guessdev' looks as follows. ``` This example was too long and was cropped: { "answer": "Apollo_program", "category": "History", "char_idx": -1, "dataset": "quizdb.org", "difficulty": "easy_college", "first_sentence": "As part of this program, William Anders took a photo that Galen Rowell called \"the most influential environmental photograph ever taken.\"", "fold": "guessdev", "full_question": "\"As part of this program, William Anders took a photo that Galen Rowell called \\\"the most influential environmental photograph e...", "gameplay": false, "id": "127028-first", "page": "Apollo_program", "proto_id": "", "qanta_id": 127028, "qdb_id": 126689, "raw_answer": "Apollo program [or Project Apollo; accept Apollo 8; accept Apollo 1; accept Apollo 11; prompt on landing on the moon]", "sentence_idx": -1, "subcategory": "American", "text": "As part of this program, William Anders took a photo that Galen Rowell called \"the most influential environmental photograph ever taken.\"", "tokenizations": [[0, 137], [138, 281], [282, 412], [413, 592], [593, 675]], "tournament": "ACF Fall", "year": 2016 } ``` ### Data Fields The data fields are the same among all splits. #### mode=first,char_skip=25 - `id`: a `string` feature. - `qanta_id`: a `int32` feature. - `proto_id`: a `string` feature. - `qdb_id`: a `int32` feature. - `dataset`: a `string` feature. - `text`: a `string` feature. - `full_question`: a `string` feature. - `first_sentence`: a `string` feature. - `char_idx`: a `int32` feature. - `sentence_idx`: a `int32` feature. - `tokenizations`: a dictionary feature containing: - `feature`: a `int32` feature. - `answer`: a `string` feature. - `page`: a `string` feature. - `raw_answer`: a `string` feature. - `fold`: a `string` feature. - `gameplay`: a `bool` feature. - `category`: a `string` feature. - `subcategory`: a `string` feature. - `tournament`: a `string` feature. - `difficulty`: a `string` feature. - `year`: a `int32` feature. ### Data Splits \| name \|adversarial\|buzzdev\|buzztrain\|guessdev\|guesstrain\|buzztest\|guesstest\| \|-----------------------\|----------:\|------:\|--------:\|-------:\|---------:\|-------:\|--------:\| \|mode=first,char_skip=25\| 1145\| 1161\| 16706\| 1055\| 96221\| 1953\| 2151\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{Rodriguez2019QuizbowlTC, title={Quizbowl: The Case for Incremental Question Answering}, author={Pedro Rodriguez and Shi Feng and Mohit Iyyer and He He and Jordan L. Boyd-Graber}, journal={ArXiv}, year={2019}, volume={abs/1904.04792} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.
community-datasets/qa4mre	community-datasets	2024-06-26T05:31:09Z	1,204	4	[ "task_categories:multiple-choice", "task_ids:multiple-choice-qa", "annotations_creators:other", "language_creators:found", "multilinguality:multilingual", "source_datasets:original", "language:ar", "language:bg", "language:de", "language:en", "language:es", "language:it", "language:ro", "license:unknown", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[ "multiple-choice" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - other language_creators: - found language: - ar - bg - de - en - es - it - ro license: - unknown multilinguality: - multilingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - multiple-choice task_ids: - multiple-choice-qa pretty_name: 'QA4MRE: Question Answering for Machine Reading Evaluation' dataset_info: - config_name: 2011.main.DE features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1747066 num_examples: 120 download_size: 128323 dataset_size: 1747066 - config_name: 2011.main.EN features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1569624 num_examples: 120 download_size: 115284 dataset_size: 1569624 - config_name: 2011.main.ES features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1694408 num_examples: 120 download_size: 125016 dataset_size: 1694408 - config_name: 2011.main.IT features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1667136 num_examples: 120 download_size: 125939 dataset_size: 1667136 - config_name: 2011.main.RO features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1740367 num_examples: 120 download_size: 129859 dataset_size: 1740367 - config_name: 2012.alzheimers.EN features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1637936 num_examples: 40 download_size: 96156 dataset_size: 1637936 - config_name: 2012.main.AR features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2710604 num_examples: 160 download_size: 168781 dataset_size: 2710604 - config_name: 2012.main.BG features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 3454163 num_examples: 160 download_size: 203734 dataset_size: 3454163 - config_name: 2012.main.DE features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2087414 num_examples: 160 download_size: 158289 dataset_size: 2087414 - config_name: 2012.main.EN features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1757534 num_examples: 160 download_size: 137169 dataset_size: 1757534 - config_name: 2012.main.ES features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2057350 num_examples: 160 download_size: 152867 dataset_size: 2057350 - config_name: 2012.main.IT features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2071658 num_examples: 160 download_size: 156976 dataset_size: 2071658 - config_name: 2012.main.RO features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2074878 num_examples: 160 download_size: 157140 dataset_size: 2074878 - config_name: 2013.alzheimers.EN features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2614760 num_examples: 40 download_size: 134481 dataset_size: 2614760 - config_name: 2013.entrance_exam.EN features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 180775 num_examples: 46 download_size: 57454 dataset_size: 180775 - config_name: 2013.main.AR features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 4180927 num_examples: 284 download_size: 155246 dataset_size: 4180927 - config_name: 2013.main.BG features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 5403194 num_examples: 284 download_size: 191476 dataset_size: 5403194 - config_name: 2013.main.EN features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2887814 num_examples: 284 download_size: 134883 dataset_size: 2887814 - config_name: 2013.main.ES features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 3449641 num_examples: 284 download_size: 151478 dataset_size: 3449641 - config_name: 2013.main.RO features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 3362997 num_examples: 284 download_size: 154236 dataset_size: 3362997 configs: - config_name: 2011.main.DE data_files: - split: train path: 2011.main.DE/train-* - config_name: 2011.main.EN data_files: - split: train path: 2011.main.EN/train-* - config_name: 2011.main.ES data_files: - split: train path: 2011.main.ES/train-* - config_name: 2011.main.IT data_files: - split: train path: 2011.main.IT/train-* - config_name: 2011.main.RO data_files: - split: train path: 2011.main.RO/train-* - config_name: 2012.alzheimers.EN data_files: - split: train path: 2012.alzheimers.EN/train-* - config_name: 2012.main.AR data_files: - split: train path: 2012.main.AR/train-* - config_name: 2012.main.BG data_files: - split: train path: 2012.main.BG/train-* - config_name: 2012.main.DE data_files: - split: train path: 2012.main.DE/train-* - config_name: 2012.main.EN data_files: - split: train path: 2012.main.EN/train-* - config_name: 2012.main.ES data_files: - split: train path: 2012.main.ES/train-* - config_name: 2012.main.IT data_files: - split: train path: 2012.main.IT/train-* - config_name: 2012.main.RO data_files: - split: train path: 2012.main.RO/train-* - config_name: 2013.alzheimers.EN data_files: - split: train path: 2013.alzheimers.EN/train-* - config_name: 2013.entrance_exam.EN data_files: - split: train path: 2013.entrance_exam.EN/train-* - config_name: 2013.main.AR data_files: - split: train path: 2013.main.AR/train-* - config_name: 2013.main.BG data_files: - split: train path: 2013.main.BG/train-* - config_name: 2013.main.EN data_files: - split: train path: 2013.main.EN/train-* - config_name: 2013.main.ES data_files: - split: train path: 2013.main.ES/train-* - config_name: 2013.main.RO data_files: - split: train path: 2013.main.RO/train-* --- # Dataset Card for "qa4mre" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: http://nlp.uned.es/clef-qa/repository/qa4mre.php - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [QA4MRE 2011-2013: Overview of Question Answering for Machine Reading Evaluation](https://link.springer.com/chapter/10.1007/978-3-642-40802-1_29) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 5.49 MB - Size of the generated dataset: 48.35 MB - Total amount of disk used: 53.84 MB ### Dataset Summary QA4MRE dataset was created for the CLEF 2011/2012/2013 shared tasks to promote research in question answering and reading comprehension. The dataset contains a supporting passage and a set of questions corresponding to the passage. Multiple options for answers are provided for each question, of which only one is correct. The training and test datasets are available for the main track. Additional gold standard documents are available for two pilot studies: one on alzheimers data, and the other on entrance exams data. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### 2011.main.DE - Size of downloaded dataset files: 0.22 MB - Size of the generated dataset: 1.75 MB - Total amount of disk used: 1.97 MB An example of 'train' looks as follows. ``` ``` #### 2011.main.EN - Size of downloaded dataset files: 0.20 MB - Size of the generated dataset: 1.57 MB - Total amount of disk used: 1.77 MB An example of 'train' looks as follows. ``` ``` #### 2011.main.ES - Size of downloaded dataset files: 0.22 MB - Size of the generated dataset: 1.70 MB - Total amount of disk used: 1.91 MB An example of 'train' looks as follows. ``` ``` #### 2011.main.IT - Size of downloaded dataset files: 0.21 MB - Size of the generated dataset: 1.67 MB - Total amount of disk used: 1.88 MB An example of 'train' looks as follows. ``` ``` #### 2011.main.RO - Size of downloaded dataset files: 0.22 MB - Size of the generated dataset: 1.74 MB - Total amount of disk used: 1.96 MB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### 2011.main.DE - `topic_id`: a `string` feature. - `topic_name`: a `string` feature. - `test_id`: a `string` feature. - `document_id`: a `string` feature. - `document_str`: a `string` feature. - `question_id`: a `string` feature. - `question_str`: a `string` feature. - `answer_options`: a dictionary feature containing: - `answer_id`: a `string` feature. - `answer_str`: a `string` feature. - `correct_answer_id`: a `string` feature. - `correct_answer_str`: a `string` feature. #### 2011.main.EN - `topic_id`: a `string` feature. - `topic_name`: a `string` feature. - `test_id`: a `string` feature. - `document_id`: a `string` feature. - `document_str`: a `string` feature. - `question_id`: a `string` feature. - `question_str`: a `string` feature. - `answer_options`: a dictionary feature containing: - `answer_id`: a `string` feature. - `answer_str`: a `string` feature. - `correct_answer_id`: a `string` feature. - `correct_answer_str`: a `string` feature. #### 2011.main.ES - `topic_id`: a `string` feature. - `topic_name`: a `string` feature. - `test_id`: a `string` feature. - `document_id`: a `string` feature. - `document_str`: a `string` feature. - `question_id`: a `string` feature. - `question_str`: a `string` feature. - `answer_options`: a dictionary feature containing: - `answer_id`: a `string` feature. - `answer_str`: a `string` feature. - `correct_answer_id`: a `string` feature. - `correct_answer_str`: a `string` feature. #### 2011.main.IT - `topic_id`: a `string` feature. - `topic_name`: a `string` feature. - `test_id`: a `string` feature. - `document_id`: a `string` feature. - `document_str`: a `string` feature. - `question_id`: a `string` feature. - `question_str`: a `string` feature. - `answer_options`: a dictionary feature containing: - `answer_id`: a `string` feature. - `answer_str`: a `string` feature. - `correct_answer_id`: a `string` feature. - `correct_answer_str`: a `string` feature. #### 2011.main.RO - `topic_id`: a `string` feature. - `topic_name`: a `string` feature. - `test_id`: a `string` feature. - `document_id`: a `string` feature. - `document_str`: a `string` feature. - `question_id`: a `string` feature. - `question_str`: a `string` feature. - `answer_options`: a dictionary feature containing: - `answer_id`: a `string` feature. - `answer_str`: a `string` feature. - `correct_answer_id`: a `string` feature. - `correct_answer_str`: a `string` feature. ### Data Splits \| name \|train\| \|------------\|----:\| \|2011.main.DE\| 120\| \|2011.main.EN\| 120\| \|2011.main.ES\| 120\| \|2011.main.IT\| 120\| \|2011.main.RO\| 120\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{10.1007/978-3-642-40802-1_29, author="Pe{\~{n}}as, Anselmo and Hovy, Eduard and Forner, Pamela and Rodrigo, {\'A}lvaro and Sutcliffe, Richard and Morante, Roser", editor="Forner, Pamela and M{\"u}ller, Henning and Paredes, Roberto and Rosso, Paolo and Stein, Benno", title="QA4MRE 2011-2013: Overview of Question Answering for Machine Reading Evaluation", booktitle="Information Access Evaluation. Multilinguality, Multimodality, and Visualization", year="2013", publisher="Springer Berlin Heidelberg", address="Berlin, Heidelberg", pages="303--320", isbn="978-3-642-40802-1" } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@albertvillanova](https://github.com/albertvillanova), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
community-datasets/ohsumed	community-datasets	2024-06-26T05:23:19Z	158	5	[ "task_categories:text-classification", "task_ids:multi-label-classification", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-nc-4.0", "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - en license: - cc-by-nc-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - multi-label-classification pretty_name: Ohsumed dataset_info: config_name: ohsumed features: - name: seq_id dtype: int64 - name: medline_ui dtype: int64 - name: mesh_terms dtype: string - name: title dtype: string - name: publication_type dtype: string - name: abstract dtype: string - name: author dtype: string - name: source dtype: string splits: - name: train num_bytes: 76530191 num_examples: 54709 - name: test num_bytes: 435681911 num_examples: 293855 download_size: 213196532 dataset_size: 512212102 configs: - config_name: ohsumed data_files: - split: train path: ohsumed/train-* - split: test path: ohsumed/test-* default: true --- # Dataset Card for ohsumed ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: http://davis.wpi.edu/xmdv/datasets/ohsumed.html - Repository: https://trec.nist.gov/data/filtering/t9.filtering.tar.gz - Paper: https://link.springer.com/chapter/10.1007/978-1-4471-2099-5_20 - Leaderboard: - Point of Contact: [William Hersh](mailto:[email protected]) [Aakash Gupta](mailto:[email protected]) ### Dataset Summary The OHSUMED test collection is a set of 348,566 references from MEDLINE, the on-line medical information database, consisting of titles and/or abstracts from 270 medical journals over a five-year period (1987-1991). The available fields are title, abstract, MeSH indexing terms, author, source, and publication type. The National Library of Medicine has agreed to make the MEDLINE references in the test database available for experimentation, restricted to the following conditions: 1. The data will not be used in any non-experimental clinical, library, or other setting. 2. Any human users of the data will explicitly be told that the data is incomplete and out-of-date. Please check this [readme](https://trec.nist.gov/data/filtering/README.t9.filtering) for more details ### Supported Tasks and Leaderboards [Text Classification](https://paperswithcode.com/sota/text-classification-on-ohsumed) ### Languages The text is primarily in English. The BCP 47 code is `en` ## Dataset Structure ### Data Instances ``` {'seq_id': 7770, 'medline_ui': 87120420, 'mesh_terms': 'Adult; Aged; Aneurysm/CO; Arteriovenous Fistula/TH; Carotid Arteries; Case Report; Female; Human; Jugular Veins; Male; Methods; Middle Age; Neck/BS; Vertebral Artery.', 'title': 'Arteriovenous fistulas of the large vessels of the neck: nonsurgical percutaneous occlusion.', 'publication_type': 'JOURNAL ARTICLE.', 'abstract': 'We describe the nonsurgical treatment of arteriovenous fistulas of the large vessels in the neck using three different means of endovascular occlusion of these large lesions, which are surgically difficult to approach and treat.', 'author': 'Vitek JJ; Keller FS.', 'source': 'South Med J 8705; 80(2):196-200'} ``` ### Data Fields Here are the field definitions: - seg_id: sequential identifier (important note: documents should be processed in this order) - medline_ui: MEDLINE identifier (UI) (<DOCNO> used for relevance judgements) - mesh_terms: Human-assigned MeSH terms (MH) - title: Title (TI) - publication_type : Publication type (PT) - abstract: Abstract (AB) - author: Author (AU) - source: Source (SO) Note: some abstracts are truncated at 250 words and some references have no abstracts at all (titles only). We do not have access to the full text of the documents. ### Data Splits The files are Train/ Test. Where the training has files from 1987 while the test files has abstracts from 1988-91 Total number of files: Train: 54710 Test: 348567 ## Dataset Creation ### Curation Rationale The OHSUMED document collection was obtained by William Hersh ([email protected]) and colleagues for the experiments described in the papers below. [Check citation](#citation-information) ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? The test collection was built as part of a study assessing the use of MEDLINE by physicians in a clinical setting (Hersh and Hickam, above). Novice physicians using MEDLINE generated 106 queries. Only a subset of these queries were used in the TREC-9 Filtering Track. Before they searched, they were asked to provide a statement of information about their patient as well as their information need. The data was collected by William Hersh & colleagues ### Annotations #### Annotation process The existing OHSUMED topics describe actual information needs, but the relevance judgements probably do not have the same coverage provided by the TREC pooling process. The MeSH terms do not directly represent information needs, rather they are controlled indexing terms. However, the assessment should be more or less complete and there are a lot of them, so this provides an unusual opportunity to work with a very large topic sample. The topic statements are provided in the standard TREC format #### Who are the annotators? Each query was replicated by four searchers, two physicians experienced in searching and two medical librarians. The results were assessed for relevance by a different group of physicians, using a three point scale: definitely, possibly, or not relevant. The list of documents explicitly judged to be not relevant is not provided here. Over 10% of the query-document pairs were judged in duplicate to assess inter-observer reliability. For evaluation, all documents judged here as either possibly or definitely relevant were considered relevant. TREC-9 systems were allowed to distinguish between these two categories during the learning process if desired. ### Personal and Sensitive Information No PII data is present in the train, test or query files. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators [Aakash Gupta](mailto:[email protected]) Th!nkEvolve Consulting and Researcher at CoronaWhy ### Licensing Information CC BY-NC 4.0 ### Citation Information Hersh WR, Buckley C, Leone TJ, Hickam DH, OHSUMED: An interactive retrieval evaluation and new large test collection for research, Proceedings of the 17th Annual ACM SIGIR Conference, 1994, 192-201. Hersh WR, Hickam DH, Use of a multi-application computer workstation in a clinical setting, Bulletin of the Medical Library Association, 1994, 82: 382-389. ### Contributions Thanks to [@skyprince999](https://github.com/skyprince999) for adding this dataset.
community-datasets/yahoo_answers_topics	community-datasets	2024-06-24T04:59:52Z	1,669	54	[ "task_categories:text-classification", "task_ids:topic-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "source_datasets:extended\|other-yahoo-answers-corpus", "language:en", "license:unknown", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[ "text-classification" ]	2022-03-02T23:29:22Z	1	--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - extended\|other-yahoo-answers-corpus task_categories: - text-classification task_ids: - topic-classification pretty_name: YahooAnswersTopics dataset_info: config_name: yahoo_answers_topics features: - name: id dtype: int32 - name: topic dtype: class_label: names: '0': Society & Culture '1': Science & Mathematics '2': Health '3': Education & Reference '4': Computers & Internet '5': Sports '6': Business & Finance '7': Entertainment & Music '8': Family & Relationships '9': Politics & Government - name: question_title dtype: string - name: question_content dtype: string - name: best_answer dtype: string splits: - name: train num_bytes: 760285695 num_examples: 1400000 - name: test num_bytes: 32653862 num_examples: 60000 download_size: 533429663 dataset_size: 792939557 configs: - config_name: yahoo_answers_topics data_files: - split: train path: yahoo_answers_topics/train-* - split: test path: yahoo_answers_topics/test-* default: true train-eval-index: - config: yahoo_answers_topics task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: question_content: text topic: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for "Yahoo Answers Topics" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Add homepage URL here if available (unless it's a GitHub repository)]() - Repository: https://github.com/LC-John/Yahoo-Answers-Topic-Classification-Dataset - Paper: [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]() - Leaderboard: [If the dataset supports an active leaderboard, add link here]() - Point of Contact: [If known, name and email of at least one person the reader can contact for questions about the dataset.]() ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.
hf-internal-testing/librispeech_asr_dummy	hf-internal-testing	2024-06-19T14:41:44Z	70,751	4	[ "size_categories:n<1K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2022-03-02T23:29:22Z	null	--- dataset_info: config_name: clean features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string splits: - name: validation num_bytes: 9677021.0 num_examples: 73 download_size: 9192059 dataset_size: 9677021.0 configs: - config_name: clean data_files: - split: validation path: clean/validation-* ---
BAAI/TACO	BAAI	2024-06-19T09:16:49Z	2,808	109	[ "task_categories:text-generation", "task_ids:language-modeling", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "language:code", "license:apache-2.0", "size_categories:10K<n<100K", "arxiv:2312.14852", "region:us", "code" ]	[ "text-generation" ]	2023-12-20T11:27:47Z	null	--- annotations_creators: [] language_creators: - crowdsourced - expert-generated language: - code license: apache-2.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: [] task_categories: - text-generation task_ids: - language-modeling paperswithcode_id: taco-topics-in-algorithmic-code-generation pretty_name: TACO tags: - code dataset_info: config_name: ALL features: - name: question dtype: string - name: solutions dtype: string - name: starter_code dtype: string - name: input_output dtype: string - name: difficulty dtype: string - name: raw_tags dtype: string - name: name dtype: string - name: source dtype: string - name: tags dtype: string - name: skill_types dtype: string - name: url dtype: string - name: Expected Auxiliary Space dtype: string - name: time_limit dtype: string - name: date dtype: string - name: picture_num dtype: string - name: memory_limit dtype: string - name: Expected Time Complexity dtype: string splits: - name: train num_bytes: 4239311973 num_examples: 25443 - name: test num_bytes: 481480755 num_examples: 1000 download_size: 2419844942 dataset_size: 4720792728 configs: - config_name: ALL data_files: - split: train path: ALL/train-* - split: test path: ALL/test-* --- # TACO Dataset <img src="https://cdn-uploads.huggingface.co/production/uploads/6335113375bed9932474315e/rMxdXcC56S3FEh37oRa2s.png" width="200" height="200"> [TACO](https://github.com/FlagOpen/TACO) is a benchmark for code generation with 26443 problems. It can be used to evaluate the ability of language models to generate code from natural language specifications. ## Key Update: We remove and modified some test cases in test set. Please update to use the newest version. ## Dataset Description - Repository: https://github.com/FlagOpen/TACO/ - Paper: [TACO: Topics in Algorithmic COde generation dataset](https://arxiv.org/abs/2312.14852) - Leaderboard: [Code Generation on CodeContests](https://paperswithcode.com/sota/code-generation-on-taco-code) - Point of Contact: [Bo-Wen Zhang](mailto:[email protected]) ## Languages The dataset contains questions in English and code solutions in Python. ## Dataset Structure ```python from datasets import load_dataset load_dataset("BAAI/TACO") DatasetDict({ train: Dataset({ features: ['question', 'solutions', 'starter_code', 'input_output', 'difficulty', 'raw_tags', 'name', 'source', 'tags', 'skill_types', 'url', 'Expected Auxiliary Space', 'time_limit', 'date', 'picture_num', 'memory_limit', 'Expected Time Complexity'], num_rows: 25443 }) test: Dataset({ features: ['question', 'solutions', 'starter_code', 'input_output', 'difficulty', 'raw_tags', 'name', 'source', 'tags', 'skill_types', 'url', 'Expected Auxiliary Space', 'time_limit', 'date', 'picture_num', 'memory_limit', 'Expected Time Complexity'], num_rows: 1000 }) }) ``` ### How to use it You can load and iterate through the dataset with the following two lines of code for the train split: ```python from datasets import load_dataset import json ds = load_dataset("BAAI/TACO", split="train") sample = next(iter(ds)) # non-empty solutions and input_output features can be parsed from text format this way: sample["solutions"] = json.loads(sample["solutions"]) sample["input_output"] = json.loads(sample["input_output"]) sample["raw_tags"] = eval(sample["raw_tags"]) sample["tags"] = eval(sample["tags"]) sample["skill_types"] = eval(sample["skill_types"]) print(sample) #OUTPUT: { "question": "You have a deck of $n$ cards, and you'd like to reorder it to a new one.\n\nEach card has a value between $1$ and $n$ equal to $p_i$. ...", "solutions": [ "import heapq\nfrom math import sqrt\nimport operator\nimport sys\ninf_var = 0\nif inf_var == 1:\n\tinf = open('input.txt', 'r')\nelse:\n\tinf = sys.stdin\n ...", "t = int(input())\nfor _ in range(t):\n\tn = int(input())\n\tp = list(map(int, input().split()))\n\tans = []\n\tp1 = [-1] * (n + 1)\n\tfor i in range(n):\n\t\tp1[p[i]] = i\n\ti = n\n\twhile i:\n\t\twhile i > 0 and p1[i] == -1:\n\t\t\ti -= 1\n\t\telse:\n\t\t\tif i:\n\t\t\t\tk = 0\n\t\t\t\tfor j in range(p1[i], n):\n\t\t\t\t\tans.append(p[j])\n\t\t\t\t\tp1[p[j]] = -1\n\t\t\t\t\tk += 1\n\t\t\t\tn -= k\n\t\t\t\ti -= 1\n\t\t\telse:\n\t\t\t\tbreak\n\tprint(ans)\n", "import sys\n\ndef get_ints():\n\treturn map(int, sys.stdin.readline().strip().split())\n\ndef get_list():\n\treturn list(map(int, sys.stdin.readline().strip().split()))\n\ndef get_list_string():\n\treturn list(map(str, sys.stdin.readline().strip().split()))\n\ndef get_string():\n\treturn sys.stdin.readline().strip()\n\ndef get_int():\n\treturn int(sys.stdin.readline().strip())\n\ndef get_print_int(x):\n\tsys.stdout.write(str(x) + '\\n')\n\ndef get_print(x):\n\tsys.stdout.write(x + '\\n')\n\ndef get_print_int_same(x):\n\tsys.stdout.write(str(x) + ' ')\n\ndef get_print_same(x):\n\tsys.stdout.write(x + ' ')\nfrom sys import maxsize\n\ndef solve():\n\tfor _ in range(get_int()):\n\t\tn = get_int()\n\t\tarr = get_list()\n\t\ti = n - 1\n\t\tj = n - 1\n\t\ttemp = sorted(arr)\n\t\tvis = [False] n\n\t\tans = []\n\t\twhile j >= 0:\n\t\t\tt = j\n\t\t\ttt = []\n\t\t\twhile t >= 0 and arr[t] != temp[i]:\n\t\t\t\tvis[arr[t] - 1] = True\n\t\t\t\ttt.append(arr[t])\n\t\t\t\tt -= 1\n\t\t\tvis[arr[t] - 1] = True\n\t\t\ttt.append(arr[t])\n\t\t\ttt = tt[::-1]\n\t\t\tfor k in tt:\n\t\t\t\tans.append(k)\n\t\t\tj = t - 1\n\t\t\twhile i >= 0 and vis[i]:\n\t\t\t\ti -= 1\n\t\tget_print(' '.join(map(str, ans)))\nsolve()\n", ... ], "starter_code": "", "input_output": { "inputs": [ "4\n4\n1 2 3 4\n5\n1 5 2 4 3\n6\n4 2 5 3 6 1\n1\n1\n", "4\n4\n2 1 3 4\n5\n1 5 2 4 3\n6\n4 2 5 3 6 1\n1\n1\n", "4\n4\n2 1 3 4\n5\n1 5 2 4 3\n6\n2 4 5 3 6 1\n1\n1\n", "4\n4\n1 2 3 4\n5\n1 5 2 4 3\n6\n4 2 5 3 6 1\n1\n1\n" ], "outputs": [ "4 3 2 1\n5 2 4 3 1\n6 1 5 3 4 2\n1\n", "4 3 2 1\n5 2 4 3 1\n6 1 5 3 4 2\n1\n", "4 3 2 1\n5 2 4 3 1\n6 1 5 3 4 2\n1\n", "\n4 3 2 1\n5 2 4 3 1\n6 1 5 3 4 2\n1\n" ] }, "difficulty": "EASY", "raw_tags": [ "data structures", "greedy", "math" ], "name": null, "source": "codeforces", "tags": [ "Data structures", "Mathematics", "Greedy algorithms" ], "skill_types": [ "Data structures", "Greedy algorithms" ], "url": "https://codeforces.com/problemset/problem/1492/B", "Expected Auxiliary Space": null, "time_limit": "1 second", "date": "2021-02-23", "picture_num": "0", "memory_limit": "512 megabytes", "Expected Time Complexity": null } ``` Each sample consists of a programming problem formulation in English, some ground truth Python solutions, test cases that are defined by their inputs and outputs and function name if provided, as well as some metadata regarding the difficulty level (difficulty), topics of task (raw tags), algorithms (tags) as well as required programming skill types (skill_types) of the problem and its source. If a sample has non empty `input_output` feature, you can read it as a dictionary with keys `inputs` and `outputs` and `fn_name` if it exists, and similarily you can parse the solutions into a list of solutions as shown in the code above. You can also filter the dataset for the difficulty level: EASY, MEDIUM, MEDIUM_HARD, HARD and VERY_HARD, or filter the programming skill types: Amortized analysis, Bit manipulation, Complete search, Data structures, Dynamic programming, Greedy algorithms, Range queries, Sorting. Just pass the list of difficulties or skills as a list. E.g. if you want the most challenging problems, you need to select the VERY_HARD level: ```python ds = load_dataset("BAAI/TACO", split="train", difficulties=["VERY_HARD"]) print(next(iter(ds))["question"]) ``` ``` #OUTPUT: """Let S(n) denote the number that represents the digits of n in sorted order. For example, S(1) = 1, S(5) = 5, S(50394) = 3459, S(353535) = 333555. Given a number X, compute <image> modulo 109 + 7. Input The first line of input will contain the integer X (1 ≤ X ≤ 10700). Output Print a single integer, the answer to the question. Examples Input 21 Output 195 Input 345342 Output 390548434 Note The first few values of S are 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 11, 12, 13, 14, 15, 16, 17, 18, 19, 2, 12. The sum of these values is 195. ``` Or if you want the problems invovled with Range queries and Sorting, you need to select the skills Range queries and Sorting: ```python ds = load_dataset("BAAI/TACO", split="train", skills=["Range queries", "Sorting"]) ``` ### Data Fields \|Field\|Type\|Description\| \|---\|---\|---\| \|question\|string\|problem description\| \|solutions\|string\|some python solutions\| \|input_output\|string\|Json string with "inputs" and "outputs" of the test cases, might also include "fn_name" the name of the function\| \|difficulty\|string\|difficulty level of the problem\| \|picture_num\|string\|the number of pictures in the problem\| \|source\|string\|the source of the problem\| \|url\|string\|url of the source of the problem\| \|date\|string\|the date of the problem\| \|starter_code\|string\|starter code to include in prompts\| \|time_limit\|string\|the time consumption limit to solve the problem\| \|memory_limit\|string\|the memory consumption limit to solve the problem\| \|Expected Auxiliary Space\|string\|the extra auxiliary space expected to solve the problem\| \|Expected Time Complexity\|string\|the time complexity expected to solve the problem\| \|raw_tags\|string\|the topics of the programming task\| \|tags\|string\|the manually annoatated algorithms needed to solve the problem\| \|skill_types\|string\|the mapped programming skill types to solve the problem\| ### Data Splits The dataset contains a train with 25443 samples and test splits with 1000 samples. ### Dataset Statistics * 26443 coding problems * 1.55M verified solutions * for tests split, the average number of test cases is 202.3 * all files have ground-truth solutions in the test split ## Dataset Creation To create the TACO dataset, the authors manually curated problems from open-access sites where programmers share problems with each other, including Aizu AtCoder, CodeChef, Codeforces, CodeWars, GeeksforGeeks, HackerEarth, HackerRank, Katti and LeetCode. For more details please refer to the original paper. ## License The TACO dataset that is authored by BAAI, Shandong Normal University and Peking University is released under an [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0). However, the data also includes content licensed under other permissive licenses such as MIT License, or web-crawled data which is used under the terms of the CC BY 4.0 license ([Creative Commons Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/legalcode)). We gratefully acknowledge the contributions of the following: * some AtCoder, Codeforces, CodeWars, Kattis, LeetCode material curated from APPS dataset (https://github.com/hendrycks/apps) * some Aizu, AtCoder, CodeChef, Codeforces material curated from CodeContest dataset (https://github.com/google-deepmind/code_contests) * Codeforces materials are sourced from http://codeforces.com. * CodeChef materials are sourced from https://www.codechef.com. * GeekforGeeks materials are sourced from https://www.geeksforgeeks.org * HackerEarth materials are curated from: [Description2Code Dataset](https://github.com/ethancaballero/description2code), licensed under the [MIT open source license](https://opensource.org/licenses/MIT), copyright not specified. * HackerRank materials are sourced from https://www.hackerrank.com. We don't know what the legal rights or data licenses of HackerRank. Please contact us if there is data license. ## Citation Information If you find our data, or code helpful, please cite [the original paper](https://arxiv.org/abs/2312.14852): ``` @article{li2023taco, title={TACO: Topics in Algorithmic COde generation dataset}, author={Rongao Li and Jie Fu and Bo-Wen Zhang and Tao Huang and Zhihong Sun and Chen Lyu and Guang Liu and Zhi Jin and Ge Li}, journal={arXiv preprint arXiv:2312.14852}, year={2023} } ```
toxigen/toxigen-data	toxigen	2024-06-17T16:35:29Z	4,574	55	[ "task_categories:text-classification", "task_ids:hate-speech-detection", "annotations_creators:expert-generated", "language_creators:machine-generated", "multilinguality:monolingual", "source_datasets:original", "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2203.09509", "region:us" ]	[ "text-classification" ]	2022-05-01T15:49:02Z	1	--- annotations_creators: - expert-generated language_creators: - machine-generated multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - hate-speech-detection pretty_name: ToxiGen dataset_info: - config_name: annotated features: - name: text dtype: string - name: target_group dtype: string - name: factual? dtype: string - name: ingroup_effect dtype: string - name: lewd dtype: string - name: framing dtype: string - name: predicted_group dtype: string - name: stereotyping dtype: string - name: intent dtype: float64 - name: toxicity_ai dtype: float64 - name: toxicity_human dtype: float64 - name: predicted_author dtype: string - name: actual_method dtype: string splits: - name: test num_bytes: 364518 num_examples: 940 - name: train num_bytes: 3238381 num_examples: 8960 download_size: 768996 dataset_size: 3602899 - config_name: annotations features: - name: Input.prompt dtype: string - name: Input.text dtype: string - name: Input.time dtype: string - name: Input.generation_method dtype: string - name: Input.prompt_label dtype: string - name: Input.target_group dtype: string - name: Input.binary_prompt_label dtype: int64 - name: Answer.annotatorAge dtype: string - name: Answer.annotatorGender dtype: string - name: Answer.annotatorMinority dtype: string - name: Answer.annotatorPolitics.1 dtype: bool - name: Answer.annotatorPolitics.2 dtype: bool - name: Answer.annotatorPolitics.3 dtype: bool - name: Answer.annotatorPolitics.4 dtype: bool - name: Answer.annotatorPolitics.5 dtype: bool - name: Answer.annotatorRace dtype: string - name: Answer.factSelect dtype: string - name: Answer.framingQ dtype: string - name: Answer.inGroup.on dtype: bool - name: Answer.ingroup.1 dtype: bool - name: Answer.ingroup.2 dtype: bool - name: Answer.ingroup.3 dtype: bool - name: Answer.intent.1 dtype: bool - name: Answer.intent.2 dtype: bool - name: Answer.intent.3 dtype: bool - name: Answer.intent.4 dtype: bool - name: Answer.intent.5 dtype: bool - name: Answer.lewd.1 dtype: bool - name: Answer.lewd.2 dtype: bool - name: Answer.lewd.3 dtype: bool - name: Answer.refTarget dtype: string - name: Answer.stateFrame dtype: string - name: Answer.stateGroup dtype: string - name: Answer.stereo.1 dtype: bool - name: Answer.stereo.2 dtype: bool - name: Answer.stereo.3 dtype: bool - name: Answer.toAI.1 dtype: bool - name: Answer.toAI.2 dtype: bool - name: Answer.toAI.3 dtype: bool - name: Answer.toAI.4 dtype: bool - name: Answer.toAI.5 dtype: bool - name: Answer.toPER.1 dtype: bool - name: Answer.toPER.2 dtype: bool - name: Answer.toPER.3 dtype: bool - name: Answer.toPER.4 dtype: bool - name: Answer.toPER.5 dtype: bool - name: Answer.writer.1 dtype: bool - name: Answer.writer.2 dtype: bool - name: HashedWorkerId dtype: int64 splits: - name: train num_bytes: 21933185 num_examples: 27450 download_size: 3350653 dataset_size: 21933185 - config_name: prompts features: - name: text dtype: string splits: - name: hate_trans_1k num_bytes: 585554 num_examples: 1000 - name: neutral_black_1k num_bytes: 857769 num_examples: 1000 - name: hate_native_american_1k num_bytes: 480000 num_examples: 1000 - name: neutral_immigrant_1k num_bytes: 342243 num_examples: 1000 - name: hate_middle_east_1k num_bytes: 426551 num_examples: 1000 - name: neutral_lgbtq_1k num_bytes: 914319 num_examples: 1000 - name: neutral_women_1k num_bytes: 394963 num_examples: 1000 - name: neutral_chinese_1k num_bytes: 412062 num_examples: 1000 - name: hate_latino_1k num_bytes: 708000 num_examples: 1000 - name: hate_bisexual_1k num_bytes: 447794 num_examples: 1000 - name: hate_mexican_1k num_bytes: 675444 num_examples: 1000 - name: hate_asian_1k num_bytes: 503093 num_examples: 1000 - name: neutral_mental_disability_1k num_bytes: 556905 num_examples: 1000 - name: neutral_mexican_1k num_bytes: 483603 num_examples: 1000 - name: hate_mental_disability_1k num_bytes: 480620 num_examples: 1000 - name: neutral_bisexual_1k num_bytes: 915612 num_examples: 1000 - name: neutral_latino_1k num_bytes: 470000 num_examples: 1000 - name: hate_chinese_1k num_bytes: 384934 num_examples: 1000 - name: neutral_jewish_1k num_bytes: 649674 num_examples: 1000 - name: hate_muslim_1k num_bytes: 425760 num_examples: 1000 - name: neutral_asian_1k num_bytes: 615895 num_examples: 1000 - name: hate_physical_disability_1k num_bytes: 413643 num_examples: 1000 - name: hate_jewish_1k num_bytes: 573538 num_examples: 1000 - name: neutral_muslim_1k num_bytes: 491659 num_examples: 1000 - name: hate_immigrant_1k num_bytes: 285309 num_examples: 1000 - name: hate_black_1k num_bytes: 745295 num_examples: 1000 - name: hate_lgbtq_1k num_bytes: 577075 num_examples: 1000 - name: hate_women_1k num_bytes: 389583 num_examples: 1000 - name: neutral_middle_east_1k num_bytes: 415319 num_examples: 1000 - name: neutral_native_american_1k num_bytes: 586993 num_examples: 1000 - name: neutral_physical_disability_1k num_bytes: 458497 num_examples: 1000 download_size: 1698170 dataset_size: 16667706 - config_name: train features: - name: prompt dtype: string - name: generation dtype: string - name: generation_method dtype: string - name: group dtype: string - name: prompt_label dtype: int64 - name: roberta_prediction dtype: float64 splits: - name: train num_bytes: 169400442 num_examples: 250951 download_size: 18784380 dataset_size: 169400442 configs: - config_name: annotated default: true data_files: - split: test path: annotated/test-* - split: train path: annotated/train-* - config_name: annotations data_files: - split: train path: annotations/train-* - config_name: prompts data_files: - split: hate_trans_1k path: prompts/hate_trans_1k-* - split: neutral_black_1k path: prompts/neutral_black_1k-* - split: hate_native_american_1k path: prompts/hate_native_american_1k-* - split: neutral_immigrant_1k path: prompts/neutral_immigrant_1k-* - split: hate_middle_east_1k path: prompts/hate_middle_east_1k-* - split: neutral_lgbtq_1k path: prompts/neutral_lgbtq_1k-* - split: neutral_women_1k path: prompts/neutral_women_1k-* - split: neutral_chinese_1k path: prompts/neutral_chinese_1k-* - split: hate_latino_1k path: prompts/hate_latino_1k-* - split: hate_bisexual_1k path: prompts/hate_bisexual_1k-* - split: hate_mexican_1k path: prompts/hate_mexican_1k-* - split: hate_asian_1k path: prompts/hate_asian_1k-* - split: neutral_mental_disability_1k path: prompts/neutral_mental_disability_1k-* - split: neutral_mexican_1k path: prompts/neutral_mexican_1k-* - split: hate_mental_disability_1k path: prompts/hate_mental_disability_1k-* - split: neutral_bisexual_1k path: prompts/neutral_bisexual_1k-* - split: neutral_latino_1k path: prompts/neutral_latino_1k-* - split: hate_chinese_1k path: prompts/hate_chinese_1k-* - split: neutral_jewish_1k path: prompts/neutral_jewish_1k-* - split: hate_muslim_1k path: prompts/hate_muslim_1k-* - split: neutral_asian_1k path: prompts/neutral_asian_1k-* - split: hate_physical_disability_1k path: prompts/hate_physical_disability_1k-* - split: hate_jewish_1k path: prompts/hate_jewish_1k-* - split: neutral_muslim_1k path: prompts/neutral_muslim_1k-* - split: hate_immigrant_1k path: prompts/hate_immigrant_1k-* - split: hate_black_1k path: prompts/hate_black_1k-* - split: hate_lgbtq_1k path: prompts/hate_lgbtq_1k-* - split: hate_women_1k path: prompts/hate_women_1k-* - split: neutral_middle_east_1k path: prompts/neutral_middle_east_1k-* - split: neutral_native_american_1k path: prompts/neutral_native_american_1k-* - split: neutral_physical_disability_1k path: prompts/neutral_physical_disability_1k-* - config_name: train data_files: - split: train path: train/train-* --- # Dataset Card for ToxiGen ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-instances) - [Additional Information](#additional-information) - [Citation Information](#citation-information) ## Sign up for Data Access To access ToxiGen, first fill out [this form](https://forms.office.com/r/r6VXX8f8vh). ## Dataset Description - Repository: https://github.com/microsoft/toxigen - Paper: https://arxiv.org/abs/2203.09509 - Point of Contact #1: [Tom Hartvigsen]([email protected]) - Point of Contact #2: [Saadia Gabriel]([email protected]) ### Dataset Summary This dataset is for implicit hate speech detection. All instances were generated using GPT-3 and the methods described in [our paper](https://arxiv.org/abs/2203.09509). ### Languages All text is written in English. ## Dataset Structure ### Data Fields We release TOXIGEN as a dataframe with the following fields: - prompt is the prompt used for generation. - generation is the TOXIGEN generated text. - generation_method denotes whether or not ALICE was used to generate the corresponding generation. If this value is ALICE, then ALICE was used, if it is TopK, then ALICE was not used. - prompt_label is the binary value indicating whether or not the prompt is toxic (1 is toxic, 0 is benign). - group indicates the target group of the prompt. - roberta_prediction is the probability predicted by our corresponding RoBERTa model for each instance. ### Citation Information ```bibtex @inproceedings{hartvigsen2022toxigen, title={ToxiGen: A Large-Scale Machine-Generated Dataset for Implicit and Adversarial Hate Speech Detection}, author={Hartvigsen, Thomas and Gabriel, Saadia and Palangi, Hamid and Sap, Maarten and Ray, Dipankar and Kamar, Ece}, booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}, year={2022} } ```
togethercomputer/RedPajama-Data-1T	togethercomputer	2024-06-17T11:36:03Z	1,926	1,084	[ "task_categories:text-generation", "language:en", "size_categories:1M<n<10M", "modality:text", "library:datasets", "library:mlcroissant", "region:us" ]	[ "text-generation" ]	2023-04-17T06:28:35Z	null	--- task_categories: - text-generation language: - en pretty_name: Red Pajama 1T --- ### Getting Started The dataset consists of 2084 jsonl files. You can download the dataset using HuggingFace: ```python from datasets import load_dataset ds = load_dataset("togethercomputer/RedPajama-Data-1T") ``` Or you can directly download the files using the following command: ``` wget 'https://data.together.xyz/redpajama-data-1T/v1.0.0/urls.txt' while read line; do dload_loc=${line#https://data.together.xyz/redpajama-data-1T/v1.0.0/} mkdir -p $(dirname $dload_loc) wget "$line" -O "$dload_loc" done < urls.txt ``` After downloading the files, you can load the dataset from disk by setting the `RED_PAJAMA_DATA_DIR` environment variable to the directory containing the files: ```python import os from datasets import load_dataset os.environ["RED_PAJAMA_DATA_DIR"] = "/path/to/download" ds = load_dataset("togethercomputer/RedPajama-Data-1T") ``` A smaller 1B-token sample of the dataset can be found [here](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T-Sample). A full set of scripts to recreate the dataset from scratch can be found [here](https://github.com/togethercomputer/RedPajama-Data). ### Dataset Summary RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset. \| Dataset \| Token Count \| \|---------------\|-------------\| \| Commoncrawl \| 878 Billion \| \| C4 \| 175 Billion \| \| GitHub \| 59 Billion \| \| ArXiv \| 28 Billion \| \| Wikipedia \| 24 Billion \| \| StackExchange \| 20 Billion \| \| Total \| 1.2 Trillion \| ### Languages Primarily English, though the Wikipedia slice contains multiple languages. ## Dataset Structure The dataset structure is as follows: ```json { "text": ..., "meta": {"url": "...", "timestamp": "...", "source": "...", "language": "...", ...}, "red_pajama_subset": "common_crawl" \| "c4" \| "github" \| "arxiv" \| "wikipedia" \| "stackexchange" } ``` ## Dataset Creation This dataset was created to follow the LLaMa paper as closely as possible to try to reproduce its recipe. ### Source Data #### Commoncrawl We download five dumps from Commoncrawl, and run the dumps through the official `cc_net` pipeline. We then deduplicate on the paragraph level, and filter out low quality text using a linear classifier trained to classify paragraphs as Wikipedia references or random Commoncrawl samples. #### C4 C4 is downloaded from Huggingface. The only preprocessing step is to bring the data into our own format. #### GitHub The raw GitHub data is downloaded from Google BigQuery. We deduplicate on the file level and filter out low quality files and only keep projects that are distributed under the MIT, BSD, or Apache license. #### Wikipedia We use the Wikipedia dataset available on Huggingface, which is based on the Wikipedia dump from 2023-03-20 and contains text in 20 different languages. The dataset comes in preprocessed format, so that hyperlinks, comments and other formatting boilerplate has been removed. #### Gutenberg and Books3 <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Defunct:</b> The 'book' config is defunct and no longer accessible due to reported copyright infringement for the Book3 dataset contained in this config.</p> </div> #### ArXiv ArXiv data is downloaded from Amazon S3 in the `arxiv` requester pays bucket. We only keep latex source files and remove preambles, comments, macros and bibliographies. #### Stackexchange The Stack Exchange split of the dataset is download from the [Internet Archive](https://archive.org/download/stackexchange). Here we only keep the posts from the 28 largest sites, remove html tags, group the posts into question-answer pairs, and order answers by their score. ### SHA256 Checksums SHA256 checksums for the dataset files for each data source are available here: ``` https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/arxiv_SHA256SUMS.txt https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/c4_SHA256SUMS.txt https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/common_crawl_SHA256SUMS.txt https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/github_SHA256SUMS.txt https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/stackexchange_SHA256SUMS.txt https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/wikipedia_SHA256SUMS.txt ``` To cite RedPajama, please use: ``` @software{together2023redpajama, author = {Together Computer}, title = {RedPajama: An Open Source Recipe to Reproduce LLaMA training dataset}, month = April, year = 2023, url = {https://github.com/togethercomputer/RedPajama-Data} } ``` ### License Please refer to the licenses of the data subsets you use. * [Common Crawl Foundation Terms of Use](https://commoncrawl.org/terms-of-use/full/) * [C4 license](https://huggingface.co/datasets/allenai/c4#license) * GitHub was limited to MIT, BSD, or Apache licenses only * [ArXiv Terms of Use](https://info.arxiv.org/help/api/tou.html) * [Wikipedia License](https://huggingface.co/datasets/wikipedia#licensing-information) * [StackExchange license on the Internet Archive](https://archive.org/details/stackexchange) <!-- ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed] -->
google-research-datasets/conceptual_captions	google-research-datasets	2024-06-17T10:51:29Z	7,082	94	[ "task_categories:image-to-text", "task_ids:image-captioning", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:other", "size_categories:1M<n<10M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[ "image-to-text" ]	2022-04-14T13:08:21Z	null	--- annotations_creators: - found language_creators: - found language: - en license: - other multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - image-to-text task_ids: - image-captioning paperswithcode_id: conceptual-captions pretty_name: Conceptual Captions dataset_info: - config_name: default features: - name: id dtype: string - name: caption dtype: string - name: url dtype: string splits: - name: train num_bytes: 623230370 num_examples: 3318333 - name: validation num_bytes: 2846024 num_examples: 15840 download_size: 0 dataset_size: 626076394 - config_name: labeled features: - name: image_url dtype: string - name: caption dtype: string - name: labels sequence: string - name: MIDs sequence: string - name: confidence_scores sequence: float64 splits: - name: train num_bytes: 1199325228 num_examples: 2007090 download_size: 532762865 dataset_size: 1199325228 - config_name: unlabeled features: - name: image_url dtype: string - name: caption dtype: string splits: - name: train num_bytes: 584517500 num_examples: 3318333 - name: validation num_bytes: 2698710 num_examples: 15840 download_size: 375258708 dataset_size: 587216210 configs: - config_name: labeled data_files: - split: train path: labeled/train-* - config_name: unlabeled data_files: - split: train path: unlabeled/train-* - split: validation path: unlabeled/validation-* default: true --- # Dataset Card for Conceptual Captions ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Dataset Preprocessing](#dataset-preprocessing) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - Homepage: [Conceptual Captions homepage](https://ai.google.com/research/ConceptualCaptions/) - Repository: [Conceptual Captions repository](https://github.com/google-research-datasets/conceptual-captions) - Paper: [Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning](https://www.aclweb.org/anthology/P18-1238/) - Leaderboard: [Conceptual Captions leaderboard](https://ai.google.com/research/ConceptualCaptions/competition?active_tab=leaderboard)https://ai.google.com/research/ConceptualCaptions/leaderboard?active_tab=leaderboard - Point of Contact: [Conceptual Captions e-mail](mailto:[email protected]) ### Dataset Summary Conceptual Captions is a dataset consisting of ~3.3M images annotated with captions. In contrast with the curated style of other image caption annotations, Conceptual Caption images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles. More precisely, the raw descriptions are harvested from the Alt-text HTML attribute associated with web images. To arrive at the current version of the captions, we have developed an automatic pipeline that extracts, filters, and transforms candidate image/caption pairs, with the goal of achieving a balance of cleanliness, informativeness, fluency, and learnability of the resulting captions. ### Dataset Preprocessing This dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code: ```python from concurrent.futures import ThreadPoolExecutor from functools import partial import io import urllib import PIL.Image from datasets import load_dataset from datasets.utils.file_utils import get_datasets_user_agent USER_AGENT = get_datasets_user_agent() def fetch_single_image(image_url, timeout=None, retries=0): for _ in range(retries + 1): try: request = urllib.request.Request( image_url, data=None, headers={"user-agent": USER_AGENT}, ) with urllib.request.urlopen(request, timeout=timeout) as req: image = PIL.Image.open(io.BytesIO(req.read())) break except Exception: image = None return image def fetch_images(batch, num_threads, timeout=None, retries=0): fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries) with ThreadPoolExecutor(max_workers=num_threads) as executor: batch["image"] = list(executor.map(fetch_single_image_with_args, batch["image_url"])) return batch num_threads = 20 dset = load_dataset("google-research-datasets/conceptual_captions") dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads}) ``` ### Supported Tasks and Leaderboards - `image-captioning`: This dataset can be used to train model for the Image Captioning task. The leaderboard for this task is available [here](https://ai.google.com/research/ConceptualCaptions/competition?active_tab=leaderboard). Official submission output captions are scored against the reference captions from the hidden test set using [this](https://github.com/tylin/coco-caption) implementation of the CIDEr (primary), ROUGE-L and SPICE metrics. ### Languages All captions are in English. ## Dataset Structure ### Data Instances #### `unlabeled` Each instance in this configuration represents a single image with a caption: ``` { 'image_url': 'http://lh6.ggpht.com/-IvRtNLNcG8o/TpFyrudaT6I/AAAAAAAAM6o/_11MuAAKalQ/IMG_3422.JPG?imgmax=800', 'caption': 'a very typical bus station' } ``` #### `labeled` Each instance in this configuration represents a single image with a caption with addtional machine-generated image labels and confidence scores: ``` { 'image_url': 'https://thumb1.shutterstock.com/display_pic_with_logo/261388/223876810/stock-vector-christmas-tree-on-a-black-background-vector-223876810.jpg', 'caption': 'christmas tree on a black background .', 'labels': ['christmas tree', 'christmas decoration', 'font', 'text', 'graphic design', 'illustration','interior design', 'tree', 'christmas eve', 'ornament', 'fir', 'plant', 'pine', 'pine family', 'graphics'], 'MIDs': ['/m/025nd', '/m/05fc9mj', '/m/03gq5hm', '/m/07s6nbt', '/m/03c31', '/m/01kr8f', '/m/0h8nzzj', '/m/07j7r', '/m/014r1s', '/m/05ykl4', '/m/016x4z', '/m/05s2s', '/m/09t57', '/m/01tfm0', '/m/021sdg'], 'confidence_scores': [0.9818305373191833, 0.952756941318512, 0.9227379560470581, 0.8524878621101379, 0.7597672343254089, 0.7493422031402588, 0.7332468628883362, 0.6869218349456787, 0.6552258133888245, 0.6357356309890747, 0.5992692708969116, 0.585474967956543, 0.5222904086112976, 0.5113164782524109, 0.5036579966545105] } ``` ### Data Fields #### `unlabeled` - `image_url`: Static URL for downloading the image associated with the post. - `caption`: Textual description of the image. #### `labeled` - `image_url`: Static URL for downloading the image associated with the post. - `caption`: Textual description of the image. - `labels`: A sequence of machine-generated labels obtained using the [Google Cloud Vision API](https://cloud.google.com/vision). - `MIDs`: A sequence of machine-generated identifiers (MID) corresponding to the label's Google Knowledge Graph entry. - `confidence_scores`: A sequence of confidence scores denoting how likely the corresponing labels are present on the image. ### Data Splits #### `unlabeled` The basic version of the dataset split into Training and Validation splits. The Training split consists of 3,318,333 image-URL/caption pairs and the Validation split consists of 15,840 image-URL/caption pairs. #### `labeled` The labeled version of the dataset with a single. The entire data is contained in Training split, which is a subset of 2,007,090 image-URL/caption pairs from the Training set of the `unlabeled` config. ## Dataset Creation ### Curation Rationale From the paper: > In this paper, we make contributions to both the data and modeling categories. First, we present a new dataset of caption annotations Conceptual Captions (Fig. 1), which has an order of magnitude more images than the COCO dataset. Conceptual Captions consists of about 3.3M himage, descriptioni pairs. In contrast with the curated style of the COCO images, Conceptual Captions images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles. ### Source Data #### Initial Data Collection and Normalization From the homepage: >For Conceptual Captions, we developed a fully automatic pipeline that extracts, filters, and transforms candidate image/caption pairs, with the goal of achieving a balance of cleanliness, informativeness, fluency, and learnability of the resulting captions. Because no human annotators are involved, the Conceptual Captions dataset generation process is highly scalable. > >To generate this dataset, we started with a Flume pipeline that processes billions of Internet webpages, extracting, filtering, and processing candidate image and caption pairs, and keeping those that pass through several filters. > >We first screen for certain properties like size, aspect ratio, adult content scores. These filters discard more than 65% of the candidates. Next, we use Alt-Texts for text-based filtering, removing captions with non-descriptive text (such as SEO tags or hashtags); we also discard texts with high sentiment polarity or adult content scores, resulting in just 3% of the incoming candidates passing through. > >In the next step, we filter out candidates for which none of the text tokens can be mapped to the visual content of the image. We use image classifiers (e.g., Google Cloud Vision APIs) to assign class labels to images and match these labels against the candidate text (allowing morphological transformations), discarding >around 60% of the candidates that reach this stage. > >The candidates passing the above filters tend to be good Alt-text image descriptions. However, a large majority of these use proper names (for people, venues, locations, etc.), brands, dates, quotes, etc. This creates two distinct problems. First, some of these cannot be inferred based on the image pixels alone. This is problematic because unless the image has the necessary visual information it is not useful for training. Second, even if the proper names could be inferred from the image it is extremely difficult for a model to learn to perform both fine-grained classification and natural-language descriptions simultaneously. We posit that if automatic determination of names, locations, brands, etc. is needed, it should be done as a separate task that may leverage image meta-information (e.g. GPS info), or complementary techniques such as OCR. > >We address the above problems with the insight that proper names should be replaced by words that represent the same general notion, i.e., by their concept. For example, we remove locations (“Crowd at a concert in Los Angeles“ becomes “Crowd at a concert”), names (e.g., “Former Miss World Priyanka Chopra on the red carpet” becomes “actor on the red carpet”), proper noun modifiers (e.g., “Italian cuisine” becomes just “cuisine”) and noun phrases (e.g., “actor and actor” becomes “actors”). Around 20% of the samples are discarded during this transformation because it can leave sentences too short, or otherwise inconsistent. > >Finally, we perform another round of filtering to identify concepts with low-count. We cluster all resolved entities (e.g., “actor”, “dog”, “neighborhood”, etc.) and keep only the candidate types which have a count of over 100 mentions. This retains around 16K entity concepts such as: “person”, “actor”, “artist”, “player” and “illustration”. The less frequent ones that we dropped include “baguette”, “bridle”, “deadline”, “ministry” and “funnel”. #### Who are the source language producers? Not specified. ### Annotations #### Annotation process Annotations are extracted jointly with the images using the automatic pipeline. #### Who are the annotators? Not specified. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Piyush Sharma, Nan Ding, Sebastian Goodman and Radu Soricut. ### Licensing Information The dataset may be freely used for any purpose, although acknowledgement of Google LLC ("Google") as the data source would be appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset. ### Citation Information ```bibtex @inproceedings{sharma2018conceptual, title = {Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning}, author = {Sharma, Piyush and Ding, Nan and Goodman, Sebastian and Soricut, Radu}, booktitle = {Proceedings of ACL}, year = {2018}, } ``` ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) and [@mariosasko](https://github.com/mariosasko) for adding this dataset.
NousResearch/CharacterCodex	NousResearch	2024-06-17T01:27:49Z	641	220	[ "language:en", "license:apache-2.0", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "language model" ]	[]	2024-06-05T00:30:38Z	null	--- language: - en license: apache-2.0 size_categories: - 10K<n<100K pretty_name: CharacterCodex dataset_info: features: - name: media_type dtype: string - name: genre dtype: string - name: character_name dtype: string - name: media_source dtype: string - name: description dtype: string - name: scenario dtype: string tags: - language model --- # Dataset Card for Character Codex ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/2qPIzxcnzXrEg66VZDjnv.png) ## Dataset Summary The Character Codex is a comprehensive dataset featuring popular characters from a wide array of media types and genres. Each entry includes detailed information about the character, the media source, and a unique scenario involving the character. This dataset is valuable for synthetic data, RAG for generative AI, writers, game developers, and fans who want to explore and utilize rich character descriptions for various creative projects. ## Dataset Structure ### Data Fields - media_type: The type of media the character originates from (e.g., Webcomics, Novels, Movies, TV Shows). - genre: The specific genre of the media type (e.g., Fantasy Webcomics, Martial Arts Fiction). - character_name: The name of the character. - media_source: The title of the media source where the character is from. - description: A detailed description of the character, including their role and significance in the story. - scenario: A creative scenario involving the character that can be used for interactive storytelling or role-playing purposes. ### Example Data ```json [ { "media_type": "Webcomics", "genre": "Fantasy Webcomics", "character_name": "Alana", "media_source": "Saga", "description": "Alana is one of the main characters from the webcomic \"Saga.\" She is a strong-willed and fiercely protective mother who is on the run with her family in a war-torn galaxy. The story blends elements of fantasy and science fiction, creating a rich and complex narrative.", "scenario": "You are a fellow traveler in the galaxy needing help, and Alana offers her assistance while sharing stories of her family's struggles and triumphs." }, { "media_type": "Novels", "genre": "Martial Arts Fiction", "character_name": "Yilin", "media_source": "The Smiling, Proud Wanderer", "description": "Yilin is a young nun from the Hengshan Sect in Jin Yong's novel \"The Smiling, Proud Wanderer.\" Known for her innocence and kindness, she becomes friends with the protagonist Linghu Chong. Her gentle nature often puts her at odds with the violent world of martial arts.", "scenario": "You are a fellow disciple of the Hengshan Sect seeking Yilin's comfort and advice after a particularly brutal conflict. Her gentle demeanor and compassionate words provide solace in a harsh world." } ] ``` # Usage ## Accessing the Data To load the dataset in your project, you can use the following code snippet: ```python from datasets import load_dataset dataset = load_dataset("NousResearch/CharacterCodex") ``` ## Use Cases - Seed Data: Useful for generating synthetic data or use in interactive experiences with generative AI. - Creative Writing: Use the detailed character descriptions and scenarios to inspire creative writing projects. - Educational: Study character development and storytelling techniques from various genres and media types. # Dataset Creation ## Data Collection The characters in this dataset were meticulously selected from a diverse range of media, ensuring a rich and varied collection. The descriptions and scenarios were crafted to provide insightful and engaging context for each character. ## Annotations Each character entry includes: - The media type (i.e. Novel, Magazine, Anime), the genre (i.e. action, historical), and the specific source/title of the media they are from (i.e. Pokemon) - A detailed description highlighting the character's role, traits, and significance. - A scenario designed to stimulate interactive and immersive experiences. # Citation ```bibtex @dataset{character_codex_2024, title={Character Codex}, author={"Teknium"}, year={2024}, note={https://huggingface.co/datasets/NousResearch/CharacterCodex} } ```
ikala/tmmluplus	ikala	2024-06-12T07:06:00Z	3,202	115	[ "task_categories:question-answering", "language:zh", "license:mit", "size_categories:10K<n<100K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "traditional chinese", "finance", "medical", "taiwan", "benchmark", "zh-tw", "zh-hant" ]	[ "question-answering" ]	2023-12-22T19:12:13Z	null	--- license: mit license_name: mit task_categories: - question-answering language: - zh tags: - traditional chinese - finance - medical - taiwan - benchmark - zh-tw - zh-hant pretty_name: tmmlu++ size_categories: - 100K<n<1M configs: - config_name: engineering_math data_files: - split: train path: "data/engineering_math_dev.csv" - split: validation path: "data/engineering_math_val.csv" - split: test path: "data/engineering_math_test.csv" - config_name: dentistry data_files: - split: train path: "data/dentistry_dev.csv" - split: validation path: "data/dentistry_val.csv" - split: test path: "data/dentistry_test.csv" - config_name: traditional_chinese_medicine_clinical_medicine data_files: - split: train path: "data/traditional_chinese_medicine_clinical_medicine_dev.csv" - split: validation path: "data/traditional_chinese_medicine_clinical_medicine_val.csv" - split: test path: "data/traditional_chinese_medicine_clinical_medicine_test.csv" - config_name: clinical_psychology data_files: - split: train path: "data/clinical_psychology_dev.csv" - split: validation path: "data/clinical_psychology_val.csv" - split: test path: "data/clinical_psychology_test.csv" - config_name: technical data_files: - split: train path: "data/technical_dev.csv" - split: validation path: "data/technical_val.csv" - split: test path: "data/technical_test.csv" - config_name: culinary_skills data_files: - split: train path: "data/culinary_skills_dev.csv" - split: validation path: "data/culinary_skills_val.csv" - split: test path: "data/culinary_skills_test.csv" - config_name: mechanical data_files: - split: train path: "data/mechanical_dev.csv" - split: validation path: "data/mechanical_val.csv" - split: test path: "data/mechanical_test.csv" - config_name: logic_reasoning data_files: - split: train path: "data/logic_reasoning_dev.csv" - split: validation path: "data/logic_reasoning_val.csv" - split: test path: "data/logic_reasoning_test.csv" - config_name: real_estate data_files: - split: train path: "data/real_estate_dev.csv" - split: validation path: "data/real_estate_val.csv" - split: test path: "data/real_estate_test.csv" - config_name: general_principles_of_law data_files: - split: train path: "data/general_principles_of_law_dev.csv" - split: validation path: "data/general_principles_of_law_val.csv" - split: test path: "data/general_principles_of_law_test.csv" - config_name: finance_banking data_files: - split: train path: "data/finance_banking_dev.csv" - split: validation path: "data/finance_banking_val.csv" - split: test path: "data/finance_banking_test.csv" - config_name: anti_money_laundering data_files: - split: train path: "data/anti_money_laundering_dev.csv" - split: validation path: "data/anti_money_laundering_val.csv" - split: test path: "data/anti_money_laundering_test.csv" - config_name: ttqav2 data_files: - split: train path: "data/ttqav2_dev.csv" - split: validation path: "data/ttqav2_val.csv" - split: test path: "data/ttqav2_test.csv" - config_name: marketing_management data_files: - split: train path: "data/marketing_management_dev.csv" - split: validation path: "data/marketing_management_val.csv" - split: test path: "data/marketing_management_test.csv" - config_name: business_management data_files: - split: train path: "data/business_management_dev.csv" - split: validation path: "data/business_management_val.csv" - split: test path: "data/business_management_test.csv" - config_name: organic_chemistry data_files: - split: train path: "data/organic_chemistry_dev.csv" - split: validation path: "data/organic_chemistry_val.csv" - split: test path: "data/organic_chemistry_test.csv" - config_name: advance_chemistry data_files: - split: train path: "data/advance_chemistry_dev.csv" - split: validation path: "data/advance_chemistry_val.csv" - split: test path: "data/advance_chemistry_test.csv" - config_name: physics data_files: - split: train path: "data/physics_dev.csv" - split: validation path: "data/physics_val.csv" - split: test path: "data/physics_test.csv" - config_name: secondary_physics data_files: - split: train path: "data/secondary_physics_dev.csv" - split: validation path: "data/secondary_physics_val.csv" - split: test path: "data/secondary_physics_test.csv" - config_name: human_behavior data_files: - split: train path: "data/human_behavior_dev.csv" - split: validation path: "data/human_behavior_val.csv" - split: test path: "data/human_behavior_test.csv" - config_name: national_protection data_files: - split: train path: "data/national_protection_dev.csv" - split: validation path: "data/national_protection_val.csv" - split: test path: "data/national_protection_test.csv" - config_name: jce_humanities data_files: - split: train path: "data/jce_humanities_dev.csv" - split: validation path: "data/jce_humanities_val.csv" - split: test path: "data/jce_humanities_test.csv" - config_name: politic_science data_files: - split: train path: "data/politic_science_dev.csv" - split: validation path: "data/politic_science_val.csv" - split: test path: "data/politic_science_test.csv" - config_name: agriculture data_files: - split: train path: "data/agriculture_dev.csv" - split: validation path: "data/agriculture_val.csv" - split: test path: "data/agriculture_test.csv" - config_name: official_document_management data_files: - split: train path: "data/official_document_management_dev.csv" - split: validation path: "data/official_document_management_val.csv" - split: test path: "data/official_document_management_test.csv" - config_name: financial_analysis data_files: - split: train path: "data/financial_analysis_dev.csv" - split: validation path: "data/financial_analysis_val.csv" - split: test path: "data/financial_analysis_test.csv" - config_name: pharmacy data_files: - split: train path: "data/pharmacy_dev.csv" - split: validation path: "data/pharmacy_val.csv" - split: test path: "data/pharmacy_test.csv" - config_name: educational_psychology data_files: - split: train path: "data/educational_psychology_dev.csv" - split: validation path: "data/educational_psychology_val.csv" - split: test path: "data/educational_psychology_test.csv" - config_name: statistics_and_machine_learning data_files: - split: train path: "data/statistics_and_machine_learning_dev.csv" - split: validation path: "data/statistics_and_machine_learning_val.csv" - split: test path: "data/statistics_and_machine_learning_test.csv" - config_name: management_accounting data_files: - split: train path: "data/management_accounting_dev.csv" - split: validation path: "data/management_accounting_val.csv" - split: test path: "data/management_accounting_test.csv" - config_name: introduction_to_law data_files: - split: train path: "data/introduction_to_law_dev.csv" - split: validation path: "data/introduction_to_law_val.csv" - split: test path: "data/introduction_to_law_test.csv" - config_name: computer_science data_files: - split: train path: "data/computer_science_dev.csv" - split: validation path: "data/computer_science_val.csv" - split: test path: "data/computer_science_test.csv" - config_name: veterinary_pathology data_files: - split: train path: "data/veterinary_pathology_dev.csv" - split: validation path: "data/veterinary_pathology_val.csv" - split: test path: "data/veterinary_pathology_test.csv" - config_name: accounting data_files: - split: train path: "data/accounting_dev.csv" - split: validation path: "data/accounting_val.csv" - split: test path: "data/accounting_test.csv" - config_name: fire_science data_files: - split: train path: "data/fire_science_dev.csv" - split: validation path: "data/fire_science_val.csv" - split: test path: "data/fire_science_test.csv" - config_name: optometry data_files: - split: train path: "data/optometry_dev.csv" - split: validation path: "data/optometry_val.csv" - split: test path: "data/optometry_test.csv" - config_name: insurance_studies data_files: - split: train path: "data/insurance_studies_dev.csv" - split: validation path: "data/insurance_studies_val.csv" - split: test path: "data/insurance_studies_test.csv" - config_name: pharmacology data_files: - split: train path: "data/pharmacology_dev.csv" - split: validation path: "data/pharmacology_val.csv" - split: test path: "data/pharmacology_test.csv" - config_name: taxation data_files: - split: train path: "data/taxation_dev.csv" - split: validation path: "data/taxation_val.csv" - split: test path: "data/taxation_test.csv" - config_name: trust_practice data_files: - split: train path: "data/trust_practice_dev.csv" - split: validation path: "data/trust_practice_val.csv" - split: test path: "data/trust_practice_test.csv" - config_name: geography_of_taiwan data_files: - split: train path: "data/geography_of_taiwan_dev.csv" - split: validation path: "data/geography_of_taiwan_val.csv" - split: test path: "data/geography_of_taiwan_test.csv" - config_name: physical_education data_files: - split: train path: "data/physical_education_dev.csv" - split: validation path: "data/physical_education_val.csv" - split: test path: "data/physical_education_test.csv" - config_name: auditing data_files: - split: train path: "data/auditing_dev.csv" - split: validation path: "data/auditing_val.csv" - split: test path: "data/auditing_test.csv" - config_name: administrative_law data_files: - split: train path: "data/administrative_law_dev.csv" - split: validation path: "data/administrative_law_val.csv" - split: test path: "data/administrative_law_test.csv" - config_name: education_(profession_level) data_files: - split: train path: "data/education_(profession_level)_dev.csv" - split: validation path: "data/education_(profession_level)_val.csv" - split: test path: "data/education_(profession_level)_test.csv" - config_name: economics data_files: - split: train path: "data/economics_dev.csv" - split: validation path: "data/economics_val.csv" - split: test path: "data/economics_test.csv" - config_name: veterinary_pharmacology data_files: - split: train path: "data/veterinary_pharmacology_dev.csv" - split: validation path: "data/veterinary_pharmacology_val.csv" - split: test path: "data/veterinary_pharmacology_test.csv" - config_name: nautical_science data_files: - split: train path: "data/nautical_science_dev.csv" - split: validation path: "data/nautical_science_val.csv" - split: test path: "data/nautical_science_test.csv" - config_name: occupational_therapy_for_psychological_disorders data_files: - split: train path: "data/occupational_therapy_for_psychological_disorders_dev.csv" - split: validation path: "data/occupational_therapy_for_psychological_disorders_val.csv" - split: test path: "data/occupational_therapy_for_psychological_disorders_test.csv" - config_name: basic_medical_science data_files: - split: train path: "data/basic_medical_science_dev.csv" - split: validation path: "data/basic_medical_science_val.csv" - split: test path: "data/basic_medical_science_test.csv" - config_name: macroeconomics data_files: - split: train path: "data/macroeconomics_dev.csv" - split: validation path: "data/macroeconomics_val.csv" - split: test path: "data/macroeconomics_test.csv" - config_name: trade data_files: - split: train path: "data/trade_dev.csv" - split: validation path: "data/trade_val.csv" - split: test path: "data/trade_test.csv" - config_name: chinese_language_and_literature data_files: - split: train path: "data/chinese_language_and_literature_dev.csv" - split: validation path: "data/chinese_language_and_literature_val.csv" - split: test path: "data/chinese_language_and_literature_test.csv" - config_name: tve_design data_files: - split: train path: "data/tve_design_dev.csv" - split: validation path: "data/tve_design_val.csv" - split: test path: "data/tve_design_test.csv" - config_name: junior_science_exam data_files: - split: train path: "data/junior_science_exam_dev.csv" - split: validation path: "data/junior_science_exam_val.csv" - split: test path: "data/junior_science_exam_test.csv" - config_name: junior_math_exam data_files: - split: train path: "data/junior_math_exam_dev.csv" - split: validation path: "data/junior_math_exam_val.csv" - split: test path: "data/junior_math_exam_test.csv" - config_name: junior_chinese_exam data_files: - split: train path: "data/junior_chinese_exam_dev.csv" - split: validation path: "data/junior_chinese_exam_val.csv" - split: test path: "data/junior_chinese_exam_test.csv" - config_name: junior_social_studies data_files: - split: train path: "data/junior_social_studies_dev.csv" - split: validation path: "data/junior_social_studies_val.csv" - split: test path: "data/junior_social_studies_test.csv" - config_name: tve_mathematics data_files: - split: train path: "data/tve_mathematics_dev.csv" - split: validation path: "data/tve_mathematics_val.csv" - split: test path: "data/tve_mathematics_test.csv" - config_name: tve_chinese_language data_files: - split: train path: "data/tve_chinese_language_dev.csv" - split: validation path: "data/tve_chinese_language_val.csv" - split: test path: "data/tve_chinese_language_test.csv" - config_name: tve_natural_sciences data_files: - split: train path: "data/tve_natural_sciences_dev.csv" - split: validation path: "data/tve_natural_sciences_val.csv" - split: test path: "data/tve_natural_sciences_test.csv" - config_name: junior_chemistry data_files: - split: train path: "data/junior_chemistry_dev.csv" - split: validation path: "data/junior_chemistry_val.csv" - split: test path: "data/junior_chemistry_test.csv" - config_name: music data_files: - split: train path: "data/music_dev.csv" - split: validation path: "data/music_val.csv" - split: test path: "data/music_test.csv" - config_name: education data_files: - split: train path: "data/education_dev.csv" - split: validation path: "data/education_val.csv" - split: test path: "data/education_test.csv" - config_name: three_principles_of_people data_files: - split: train path: "data/three_principles_of_people_dev.csv" - split: validation path: "data/three_principles_of_people_val.csv" - split: test path: "data/three_principles_of_people_test.csv" - config_name: taiwanese_hokkien data_files: - split: train path: "data/taiwanese_hokkien_dev.csv" - split: validation path: "data/taiwanese_hokkien_val.csv" - split: test path: "data/taiwanese_hokkien_test.csv" --- # TMMLU+ : Large scale traditional chinese massive multitask language understanding <p align="center"> <img src="https://huggingface.co/datasets/ikala/tmmluplus/resolve/main/cover.png" alt="A close-up image of a neat paper note with a white background. The text 'TMMLU+' is written horizontally across the center of the note in bold, black. Join us to work in multimodal LLM : https://ikala.ai/recruit/" style="max-width: 400" width=400 /> </p> We present TMMLU+, a traditional Chinese massive multitask language understanding dataset. TMMLU+ is a multiple-choice question-answering dataset featuring 66 subjects, ranging from elementary to professional level. The TMMLU+ dataset is six times larger and contains more balanced subjects compared to its predecessor, [TMMLU](https://github.com/mtkresearch/MR-Models/tree/main/TC-Eval/data/TMMLU). We have included benchmark results in TMMLU+ from closed-source models and 20 open-weight Chinese large language models, with parameters ranging from 1.8B to 72B. The benchmark results show that Traditional Chinese variants still lag behind those trained on major Simplified Chinese models. ```python from datasets import load_dataset task_list = [ 'engineering_math', 'dentistry', 'traditional_chinese_medicine_clinical_medicine', 'clinical_psychology', 'technical', 'culinary_skills', 'mechanical', 'logic_reasoning', 'real_estate', 'general_principles_of_law', 'finance_banking', 'anti_money_laundering', 'ttqav2', 'marketing_management', 'business_management', 'organic_chemistry', 'advance_chemistry', 'physics', 'secondary_physics', 'human_behavior', 'national_protection', 'jce_humanities', 'politic_science', 'agriculture', 'official_document_management', 'financial_analysis', 'pharmacy', 'educational_psychology', 'statistics_and_machine_learning', 'management_accounting', 'introduction_to_law', 'computer_science', 'veterinary_pathology', 'accounting', 'fire_science', 'optometry', 'insurance_studies', 'pharmacology', 'taxation', 'trust_practice', 'geography_of_taiwan', 'physical_education', 'auditing', 'administrative_law', 'education_(profession_level)', 'economics', 'veterinary_pharmacology', 'nautical_science', 'occupational_therapy_for_psychological_disorders', 'basic_medical_science', 'macroeconomics', 'trade', 'chinese_language_and_literature', 'tve_design', 'junior_science_exam', 'junior_math_exam', 'junior_chinese_exam', 'junior_social_studies', 'tve_mathematics', 'tve_chinese_language', 'tve_natural_sciences', 'junior_chemistry', 'music', 'education', 'three_principles_of_people', 'taiwanese_hokkien' ] for task in task_list: val = load_dataset('ikala/tmmluplus', task)['validation'] dev = load_dataset('ikala/tmmluplus', task)['train'] test = load_dataset('ikala/tmmluplus', task)['test'] ``` For each dataset split ```python for row in test: print(row) break >> Dataset({ features: ['question', 'A', 'B', 'C', 'D', 'answer'], num_rows: 11 }) ``` Statistic on all four categories : STEM, Social Science, Humanities, Other \| Category \| Test \| Dev \| Validation \| \|----------------------------------\|-------\|------\|------------\| \| STEM \| 3458 \| 70 \| 385 \| \| Social Sciences \| 5958 \| 90 \| 665 \| \| Humanities \| 1763 \| 35 \| 197 \| \| Other (Business, Health, Misc.) \| 8939 \| 135 \| 995 \| \| Total \| 20118 \| 330 \| 2242 \| ## Benchmark on direct prompting \| model \| STEM \| Social Science \| Humanities \| Other \| Average \| \|------------\|------------\|------------\|------------\|------------\|------------\| \|Gemini-1.5-pro \| 66.18\|70.29\|61.84\|60.30\|64.65\| \| [Qwen/Qwen-72B](https://huggingface.co/Qwen/Qwen-72B) \| 61.12 \| 71.65 \| 63.00 \| 61.31 \|64.27\| \| gpt-4-0613 \| 60.36 \| 67.36 \| 56.03 \| 57.62 \|60.34\| \| Qwen-max \| 59.92 \| 66.95 \| 57.43 \| 56.48 \|60.20\| \| [Qwen/Qwen-72B-Chat](https://huggingface.co/Qwen/Qwen-72B-Chat) \| 55.15 \| 66.20 \| 55.65 \| 57.19 \|58.55\| \| [Qwen/Qwen-14B](https://huggingface.co/Qwen/Qwen-14B) \| 46.94 \| 56.69 \| 49.43 \| 48.81 \|50.47\| \| Gemini-pro \| 45.38 \| 57.29 \| 48.80 \| 48.21 \|49.92\| \| [01-ai/Yi-34B-Chat](https://huggingface.co/01-ai/Yi-34B-Chat) \| 40.24 \| 56.77 \| 53.99 \| 47.58 \|49.64\| \| Gemini-1.5-flash \|53.47\|53.42\|42.99\|46.56\|49.11\| \| [Reka Flash](https://www.reka.ai/)\|45.26\|52.91\|46.31\|43.76\|47.06\| \| [Qwen/Qwen-14B-Chat](https://huggingface.co/Qwen/Qwen-14B-Chat) \| 43.86 \| 53.29 \| 44.78 \| 45.13 \|46.77\| \| [Qwen/Qwen1.5-14B-Chat](https://huggingface.co/Qwen/Qwen1.5-14B-Chat)\|39.65\|52.76\|43.90\|44.95\|45.31\| \| [01-ai/Yi-6B-Chat](https://huggingface.co/01-ai/Yi-6B-Chat) \| 39.62 \| 50.24 \| 44.44 \| 44.26 \|44.64\| \| Claude-1.3 \| 42.65 \| 49.33 \| 42.16 \| 44.14 \|44.57\| \| [MediaTek-Research/Breeze-7B-Instruct-v0_1](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-v0_1)\| 36.46 \| 48.38 \|45.11 \|40.75 \| 42.67 \| \| gpt-3.5-turbo-0613 \| 41.56 \| 46.72 \| 36.73 \| 42.03 \|41.76\| \| [CausalLM/14B](https://huggingface.co/CausalLM/14B) \| 39.83 \| 44.50 \| 39.61 \| 41.97 \|41.48\| \| [Skywork/Skywork-13B-base](https://huggingface.co/Skywork/Skywork-13B-base) \| 36.93 \| 47.27 \| 41.04 \| 40.10 \|41.33\| \| Claude-3-opus \|42.95\|45.49\|35.79\|40.24\|41.12\| \| [Qwen/Qwen-7B](https://huggingface.co/Qwen/Qwen-7B) \| 37.53 \| 45.48 \| 38.09 \| 38.96 \|40.01\| \| [meta-llama/Llama-3-70b-chat-hf](https://docs.together.ai/docs/inference-models) \| 34.44 \| 47.02 \| 37.50 \|39.51 \| 39.62 \| \| [Qwen/Qwen-7B-Chat](https://huggingface.co/Qwen/Qwen-7B-Chat) \| 33.32 \| 44.64 \| 40.27 \| 39.89 \|39.53\| \| [vivo-ai/BlueLM-7B-Base](https://huggingface.co/vivo-ai/BlueLM-7B-Base) \| 33.94 \| 41.52 \| 37.38 \| 38.74 \|37.90\| \| [baichuan-inc/Baichuan2-13B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat) \| 29.64 \| 43.73 \| 37.36 \| 39.88 \|37.65\| \| [Qwen/Qwen-1_8B](https://huggingface.co/Qwen/Qwen-1_8B) \| 32.65 \| 38.95 \| 38.34 \| 35.27 \|36.30\| \| Claude-2 \| 39.65 \| 39.09 \| 28.59 \| 37.47 \|36.20\| \| [THUDM/chatglm3-6b](https://huggingface.co/THUDM/chatglm3-6b) \| 31.05 \| 39.31 \| 35.64 \| 35.60 \|35.40\| \| [deepseek-ai/deepseek-llm-7b-chat](https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat) \| 29.82 \| 42.29 \| 34.24 \| 34.31 \|35.17\| \| [CausalLM/7B](https://huggingface.co/CausalLM/7B) \| 31.03 \| 38.17 \| 35.87 \| 35.39 \|35.11\| \| [Azure99/blossom-v3_1-mistral-7b](https://huggingface.co/Azure99/blossom-v3_1-mistral-7b) \| 32.80 \| 36.91 \| 32.36 \| 34.53 \|34.15\| \| [google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it) \| 31.89 \| 35.70 \| 34.00 \| 33.79 \| 33.84 \| \| [Reka Edge](https://www.reka.ai/)\|30.02\|39.40\|31.84\|32.36\|33.41\| \| [microsoft/Orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b) \| 24.69 \| 39.18 \| 33.60 \| 31.99 \|32.37\| \| [Qwen/Qwen-1_8B-Chat](https://huggingface.co/Qwen/Qwen-1_8B-Chat) \| 26.60 \| 36.36 \| 31.81 \| 31.96 \|31.68\| \| [meta-llama/Llama-3-8b-chat-hf](https://docs.together.ai/docs/inference-models) \| 31.52 \| 34.19 \| 28.91 \| 31.79 \| 31.60 \| \| [TigerResearch/tigerbot-13b-chat-v3](https://huggingface.co/TigerResearch/tigerbot-13b-chat-v3) \| 24.73 \| 29.63 \| 25.72 \| 27.22 \|26.82\| \| [hongyin/mistral-7b-80k](https://huggingface.co/hongyin/mistral-7b-80k) \| 24.26 \| 23.76 \| 22.56 \| 24.57 \|23.79\| \| [deepseek-ai/deepseek-llm-67b-chat](https://huggingface.co/deepseek-ai/deepseek-llm-67b-chat) \| 19.10 \| 26.06 \| 21.51 \| 21.77 \|22.11\| \| [yentinglin/Taiwan-LLM-13B-v2.0-chat](https://huggingface.co/yentinglin/Taiwan-LLM-13B-v2.0-chat) \| 18.53 \| 27.65 \| 17.77 \| 21.49 \|21.36\| \| [GeneZC/MiniChat-3B](https://huggingface.co/GeneZC/MiniChat-3B) \| 17.66 \| 23.35 \| 22.71 \| 20.34 \|21.02\| \| [LinkSoul/Chinese-Llama-2-7b](https://huggingface.co/LinkSoul/Chinese-Llama-2-7b) \| 16.55 \| 18.39 \| 12.97 \| 16.13 \|16.01\| \| [yentinglin/Taiwan-LLM-7B-v2.1-chat](https://huggingface.co/yentinglin/Taiwan-LLM-7B-v2.1-chat) \| 14.99 \| 16.23 \| 15.00 \| 16.22 \|15.61\| \| Claude-instant-1 \| 12.52 \| 17.13 \| 15.10 \| 13.57 \|14.58\| \| [FlagAlpha/Atom-7B](https://huggingface.co/FlagAlpha/Atom-7B) \| 5.60 \| 13.57 \| 7.71 \| 11.84 \|9.68\| Results via [ievals](https://github.com/iKala/ievals) ( settings : 0-shot direct answering ) # Citation ``` @article{ikala2024improved, title={An Improved Traditional Chinese Evaluation Suite for Foundation Model}, author={Tam, Zhi-Rui and Pai, Ya-Ting and Lee, Yen-Wei and Cheng, Sega and Shuai, Hong-Han}, journal={arXiv preprint arXiv:2403.01858}, year={2024} } ```
espnet/yodas	espnet	2024-06-10T02:11:54Z	176,619	110	[ "license:cc-by-3.0", "arxiv:2406.00899", "region:us" ]	[]	2024-02-10T21:00:10Z	null	--- license: cc-by-3.0 --- Updates - 2024/07/09: we also uploaded a new version of YODAS as [YODAS2](https://huggingface.co/datasets/espnet/yodas2), it provides unsegmented audios and higher sampling rate (24k) ## README This is the YODAS manual/automatic subset from our YODAS dataset, it has 369,510 hours of speech. This dataset contains audio utterances and corresponding captions (manual or automatic) from YouTube. Note that manual caption only indicates that it is uploaded by users, but not necessarily transcribed by a human For more details about YODAS dataset, please refer to [our paper](https://arxiv.org/abs/2406.00899) ## Usage: Considering the extremely large size of the entire dataset, we support two modes of dataset loadings: standard mode: each subset will be downloaded to the local dish before first iterating. ```python from datasets import load_dataset # Note this will take very long time to download and preprocess # you can try small subset for testing purpose ds = load_dataset('espnet/yodas', 'en000') print(next(iter(ds['train']))) ``` streaming mode most of the files will be streamed instead of downloaded to your local deivce. It can be used to inspect this dataset quickly. ```python from datasets import load_dataset # this streaming loading will finish quickly ds = load_dataset('espnet/yodas', 'en000', streaming=True) #{'id': '9774', 'utt_id': 'YoRjzEnRcqu-00000-00000716-00000819', 'audio': {'path': None, 'array': array([-0.009552 , -0.01086426, -0.012146 , ..., -0.01992798, # -0.01885986, -0.01074219]), 'sampling_rate': 16000}, 'text': 'There is a saying'} print(next(iter(ds['train']))) ``` ## Subsets/Shards There are 149 languages in this dataset, each language is sharded into at least 1 shard to make it easy for our processing and uploading purposes. The raw data of each shard contains 500G at most. Statistics of each shard can be found in the last section. We distinguish manual caption subset and automatic caption subset by the first digit in each shard's name. The first digit is 0 if it contains manual captions, 1 if it contains automatic captions. For example, `en000` to `en005` are the English shards containing manual subsets, and `en100` to `en127` contains the automatic subsets. ## Reference ``` @inproceedings{li2023yodas, title={Yodas: Youtube-Oriented Dataset for Audio and Speech}, author={Li, Xinjian and Takamichi, Shinnosuke and Saeki, Takaaki and Chen, William and Shiota, Sayaka and Watanabe, Shinji}, booktitle={2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)}, pages={1--8}, year={2023}, organization={IEEE} } ``` ## Contact If you have any questions, feel free to contact us at the following email address. We made sure that our dataset only consisted of videos with CC licenses during our downloading. But in case you find your video unintentionally included in our dataset and would like to delete it, you can send a delete request to the following email. Remove the parenthesis `()` from the following email address `(lixinjian)(1217)@gmail.com` ## Statistics Note that there are no overlappings across different subsets, each audio can be included in the dataset at most once. \| Subset name \| Hours \| \|------\|--------\| \|aa000\|0.171472\| \|ab000\|0.358342\| \|af000\|0.880497\| \|ak000\|0.250858\| \|am000\|0.924708\| \|ar000\|289.707\| \|as000\|0.548239\| \|ay000\|0.0342722\| \|az000\|3.8537\| \|ba000\|0.0210556\| \|be000\|48.1537\| \|bg000\|46.8375\| \|bh000\|0.0127111\| \|bi000\|0.0125556\| \|bm000\|0.00214722\| \|bn000\|27.064\| \|bo000\|0.746211\| \|br000\|0.729914\| \|bs000\|9.36959\| \|ca000\|74.1909\| \|co000\|0.0418639\| \|cr000\|0.00584167\| \|cs000\|167.604\| \|cy000\|5.20017\| \|da000\|27.4345\| \|de000\|3063.81\| \|de100\|4998.11\| \|de101\|4995.08\| \|de102\|955.389\| \|dz000\|0.06365\| \|ee000\|0.0411722\| \|el000\|126.75\| \|en000\|4999.73\| \|en001\|5032.69\| \|en002\|5039.9\| \|en003\|5001.4\| \|en004\|5054.66\| \|en005\|4027.02\| \|en100\|5147.07\| \|en101\|5123.05\| \|en102\|5117.68\| \|en103\|5127.3\| \|en104\|5126.33\| \|en105\|5097.65\| \|en106\|5131.47\| \|en107\|5135.6\| \|en108\|5136.84\| \|en109\|5112.94\| \|en110\|5109\| \|en111\|5118.69\| \|en112\|5122.57\| \|en113\|5122.31\| \|en114\|5112.36\| \|en115\|5112.27\| \|en116\|5123.77\| \|en117\|5117.31\| \|en118\|5117.94\| \|en119\|5133.05\| \|en120\|5127.79\| \|en121\|5129.08\| \|en122\|5130.22\| \|en123\|5097.56\| \|en124\|5116.59\| \|en125\|5109.76\| \|en126\|5136.21\| \|en127\|2404.89\| \|eo000\|12.6874\| \|es000\|3737.86\| \|es100\|5125.25\| \|es101\|5130.44\| \|es102\|5145.66\| \|es103\|5138.26\| \|es104\|5139.57\| \|es105\|5138.95\| \|es106\|2605.26\| \|et000\|14.4129\| \|eu000\|19.6356\| \|fa000\|42.6734\| \|ff000\|0.0394972\| \|fi000\|212.899\| \|fj000\|0.0167806\| \|fo000\|0.183244\| \|fr000\|2423.7\| \|fr100\|5074.93\| \|fr101\|5057.79\| \|fr102\|5094.14\| \|fr103\|3222.95\| \|fy000\|0.0651667\| \|ga000\|1.49252\| \|gd000\|0.01885\| \|gl000\|9.52575\| \|gn000\|0.181356\| \|gu000\|1.99355\| \|ha000\|0.102931\| \|hi000\|480.79\| \|hi100\|2.74865\| \|ho000\|0.0562194\| \|hr000\|25.9171\| \|ht000\|1.07494\| \|hu000\|181.763\| \|hy000\|1.64412\| \|ia000\|0.0856056\| \|id000\|1420.09\| \|id100\|4902.79\| \|id101\|3560.82\| \|ie000\|0.134603\| \|ig000\|0.086875\| \|ik000\|0.00436667\| \|is000\|5.07075\| \|it000\|1454.98\| \|it100\|4989.62\| \|it101\|4242.87\| \|iu000\|0.0584278\| \|iw000\|161.373\| \|ja000\|1094.18\| \|ja100\|2929.94\| \|jv000\|1.08701\| \|ka000\|26.9727\| \|ki000\|0.000555556\| \|kk000\|3.72081\| \|kl000\|0.00575556\| \|km000\|3.98273\| \|kn000\|2.36041\| \|ko000\|2774.28\| \|ko100\|5018.29\| \|ko101\|5048.49\| \|ko102\|5018.27\| \|ko103\|2587.85\| \|ks000\|0.0150444\| \|ku000\|1.93419\| \|ky000\|14.3917\| \|la000\|7.26088\| \|lb000\|0.1115\| \|lg000\|0.00386111\| \|ln000\|0.188739\| \|lo000\|0.230986\| \|lt000\|17.6507\| \|lv000\|2.47671\| \|mg000\|0.169653\| \|mi000\|1.10089\| \|mk000\|5.54236\| \|ml000\|13.2386\| \|mn000\|2.0232\| \|mr000\|7.11602\| \|ms000\|28.0219\| \|my000\|2.35663\| \|na000\|0.0397056\| \|nd000\|0.00111111\| \|ne000\|2.34936\| \|nl000\|413.044\| \|nl100\|2490.13\| \|no000\|129.183\| \|nv000\|0.00319444\| \|oc000\|0.166108\| \|om000\|0.148478\| \|or000\|0.421436\| \|pa000\|1.58188\| \|pl000\|757.986\| \|ps000\|0.9871\| \|pt000\|1631.44\| \|pt100\|5044.57\| \|pt101\|5038.33\| \|pt102\|5041.59\| \|pt103\|3553.28\| \|qu000\|0.748772\| \|rm000\|0.192933\| \|rn000\|0.00401111\| \|ro000\|99.9175\| \|ru000\|4968.37\| \|ru001\|627.679\| \|ru100\|5098.3\| \|ru101\|5098\| \|ru102\|5119.43\| \|ru103\|5107.29\| \|ru104\|5121.73\| \|ru105\|5088.05\| \|ru106\|3393.44\| \|rw000\|0.640825\| \|sa000\|0.354139\| \|sc000\|0.00801111\| \|sd000\|0.0768722\| \|sg000\|0.000472222\| \|sh000\|0.250914\| \|si000\|4.2634\| \|sk000\|30.0155\| \|sl000\|22.9366\| \|sm000\|0.102333\| \|sn000\|0.0134722\| \|so000\|3.36819\| \|sq000\|3.48276\| \|sr000\|15.2849\| \|st000\|0.00324167\| \|su000\|0.0404639\| \|sv000\|127.411\| \|sw000\|1.93409\| \|ta000\|59.4805\| \|te000\|5.66794\| \|tg000\|0.272386\| \|th000\|497.14\| \|th100\|1.87429\| \|ti000\|0.343897\| \|tk000\|0.0651806\| \|tn000\|0.112181\| \|to000\|0.000555556\| \|tr000\|588.698\| \|tr100\|4067.68\| \|ts000\|0.00111111\| \|tt000\|0.0441194\| \|ug000\|0.0905\| \|uk000\|396.598\| \|uk100\|450.411\| \|ur000\|22.4373\| \|uz000\|5.29325\| \|ve000\|0.00355278\| \|vi000\|779.854\| \|vi100\|4963.77\| \|vi101\|4239.37\| \|vo000\|0.209436\| \|wo000\|0.0801528\| \|xh000\|0.126628\| \|yi000\|0.0810111\| \|yo000\|0.322206\| \|zh000\|299.368\| \|zu000\|0.139931\|
LDJnr/Capybara	LDJnr	2024-06-07T20:15:36Z	529	237	[ "task_categories:question-answering", "task_categories:text-generation", "language:en", "license:apache-2.0", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "Physics", "Biology", "Math", "Chemistry", "Culture", "Logic", "Roleplay" ]	[ "conversational", "question-answering", "text-generation" ]	2023-12-16T05:45:37Z	null	--- license: apache-2.0 task_categories: - conversational - question-answering - text-generation language: - en tags: - Physics - Biology - Math - Chemistry - Culture - Logic - Roleplay pretty_name: LessWrong-Amplify-Instruct size_categories: - 10K<n<100K --- ## This is the Official Capybara dataset. Over 10,000 multi-turn examples. Capybara is the culmination of insights derived from synthesis techniques like Evol-instruct (used for WizardLM), Alpaca, Orca, Vicuna, Lamini, FLASK and others. The single-turn seeds used to initiate the Amplify-Instruct synthesis of conversations are mostly based on datasets that i've personally vetted extensively, and are often highly regarded for their diversity and demonstration of logical robustness and prose, such as Airoboros, Know logic, EverythingLM, GPTeacher and even entirely new seed instructions derived from different sources, including certain in-house multi-turn datasets like Dove and Verified-Camel(A successor to Puffin). The multi-turn synthetic conversation generation method is what i'm calling Amplify-Instruct, and the first resulting dataset using this method is called Capybara. This dataset has a strong focus on information diversity across a wide range of domains, and multi-turn conversations that strongly emphasize reasoning, logic and extrapolation about a wide range of subjects, also many great examples of conversations delving into obscure sub-topics and rabbit holes across pop-culture and STEM, while also maintaining natural prose. While performing great in it's current state, the current dataset used for fine-tuning is entirely contained within 20K training examples, this is 10 times smaller than many similar performing datasets, this is signficant when it comes to scaling implications once I decide to scale the use of Amplify-Instruct to significantly more examples. - Most tokens contained in this dataset are newly synthesized and did not exist prior online. - This leverages the Amplify-Instruct method(paper coming soon) to grow thousands of high-quality single-turn seeds into advanced and in-depth multi-turn conversations. - Average context length per conversation is over 1,000 tokens and 3 turns or more per example (most instruction/chat datasets on HF for fine-tuning are only 1 turn) - Each conversation is optimized to amplify the natural raw knowledge capabilities of the model, as well as delving deep into obscure and advanced topics. - Aggresively filtered to remove any and all possible examples of overt moralizing/alignment, and common undesirable behaviours such as "as an AI language model" and "September 2021" and "I don't have personal beliefs" ## Benchmarks. - Resulting benchmarks are available on HF Leaderboard, and other benchmarks done as well such as AGIEval, Bigbench and GPT4All. - (The only Capybara model available on all of these benchmarks including HF leaderboard is Capybara V1, trained on Llama-2) - The below benchmarks are compared against fine-tunes also done on Llama-2. ![Capybara](https://i.imgur.com/OpajtNJ.jpeg) ![Capybara](https://i.imgur.com/daIZn6n.jpeg) ## Quality filtering and cleaning. - Extensive measures were done to filter out any conversations that contained even a single instance of overt AI moralizing/alignment, such as "As an AI language model" and common undesirable behaviours such as conversations that include "September 2021" and "I don't have personal beliefs" and other phrases I've found to be highly correlated with undesirable responses and conversation paths. ## Thank you to those of you that have indirectly contributed! While most of the tokens within Capybara are newly synthsized and part of datasets like Puffin/Dove, we would like to credit the single-turn datasets we leveraged as seeds, which were used to generate the multi-turn data. The datasets shown in green below are datasets that we sampled from to curate seeds that are used during Amplify-Instruct synthesis for this project, however, most of the tokens in capybara within those given sections are novel tokens not present in any of the seed datasets. Datasets in Blue are in-house curations that previously existed prior to Capybara, and were now used as seeds for Capybara. ![Capybara](https://i.imgur.com/yB58OoD.jpeg) ## Dataset contamination. We have checked the capybara dataset for contamination for several of the most popular benchmarks and can confirm that there is no contaminaton found besides MT-bench which is now cleaned out. We leveraged minhash to check for 100%, 99%, 98% and 97% similarity matches between our data and the questions and answers in benchmarks, we found no exact matches, nor did we find any matches down to the 97% similarity level. The following are benchmarks we checked for contamination against our dataset: - HumanEval - AGIEval - TruthfulQA - MMLU - GPT4All *Newly cleaned out as of 12/15/2023 - MT-bench ## Credits: During the curation process, there can be some relatively arduos steps when it comes to actually executing on the best experimentation or concepts for how to filter examples out. Luckily there is folks over at Nous Research that helped with expediting these processes, big thank you to J-Supha specifically for making these types of significant contributions. ## Example Outputs from the Llama-2 7B model trained on this dataset: ![Capybara](https://img001.prntscr.com/file/img001/T9yYxR1xQSaK_UGdy3t2Cw.png) ![Capybara](https://img001.prntscr.com/file/img001/DQXqmKbsQQOIcgny1eoGNA.png) ![Capybara](https://img001.prntscr.com/file/img001/85X3L9ZxTsOKo3fUQ7GRVA.png) ## Future Plans & How you can help This is a relatively early build amongst the grand plans for the future of what I plan to work on! In the near future we plan on leveraging the help of domain specific expert volunteers to eliminate any mathematically/verifiably incorrect answers from training curations of different types of datasets. If you have at-least a bachelors in mathematics, physics, biology or chemistry and would like to volunteer even just 30 minutes of your expertise time, please contact LDJ on discord! Citation: ``` @article{daniele2023amplify-instruct, title={Amplify-Instruct: Synthetically Generated Diverse Multi-turn Conversations for efficient LLM Training.}, author={Daniele, Luigi and Suphavadeeprasit}, journal={arXiv preprint arXiv:(coming soon)}, url={https://huggingface.co/datasets/LDJnr/Capybara}, year={2023} } ```
Lin-Chen/ShareGPT4V	Lin-Chen	2024-06-06T13:52:04Z	1,098	285	[ "task_categories:visual-question-answering", "task_categories:question-answering", "language:en", "license:cc-by-nc-4.0", "size_categories:1M<n<10M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2311.12793", "region:us" ]	[ "visual-question-answering", "question-answering" ]	2023-11-20T09:29:12Z	null	--- license: cc-by-nc-4.0 task_categories: - visual-question-answering - question-answering language: - en pretty_name: ShareGPT4V Captions 1.2M Dataset Card size_categories: - 1M<n configs: - config_name: ShareGPT4V data_files: sharegpt4v_instruct_gpt4-vision_cap100k.json - config_name: ShareGPT4V-PT data_files: share-captioner_coco_lcs_sam_1246k_1107.json --- # News [2024/5/8] We released [ShareGPT4Video](https://sharegpt4video.github.io/), a large-scale video-caption dataset, with 40K captions annotated by GPT4V and 4.8M captions annotated by our ShareCaptioner-Video. The total videos last with 300 hours and 3000 hours separately! # ShareGPT4V 1.2M Dataset Card ## Dataset details Dataset type: ShareGPT4V Captions 1.2M is a set of GPT4-Vision-powered multi-modal captions data. It is constructed to enhance modality alignment and fine-grained visual concept perception in Large Multi-Modal Models (LMMs) during both the pre-training and supervised fine-tuning stages. This advancement aims to bring LMMs towards GPT4-Vision capabilities. * sharegpt4v_instruct_gpt4-vision_cap100k.json is generated by GPT4-Vision (ShareGPT4V). * share-captioner_coco_lcs_sam_1246k_1107.json is generated by our Share-Captioner trained on GPT4-Vision-generated data (ShareGPT4V-PT). * sharegpt4v_mix665k_cap23k_coco-ap9k_lcs3k_sam9k_div2k.json is curated from sharegpt4v_instruct_gpt4-vision_cap100k.json for the supervised fine-tuning stage. Dataset date: ShareGPT4V Captions 1.2M was collected in 11.07 2023. Paper or resources for more information: [[Project](https://ShareGPT4V.github.io/)] [[Paper](https://huggingface.co/papers/2311.12793)] [[Code](https://github.com/ShareGPT4Omni/ShareGPT4V)] License: Attribution-NonCommercial 4.0 International It should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use ## Intended use Primary intended uses: The primary use of ShareGPT4V Captions 1.2M is research on large multimodal models and chatbots. Primary intended users: The primary intended users of this dataset are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
osunlp/Multimodal-Mind2Web	osunlp	2024-06-05T05:12:21Z	3,318	68	[ "language:en", "license:openrail", "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2401.01614", "region:us", "web agent", "multimodal" ]	[]	2024-03-18T03:32:11Z	2	--- license: openrail language: - en tags: - web agent - multimodal dataset_info: features: - name: action_uid dtype: string - name: raw_html dtype: string - name: cleaned_html dtype: string - name: operation dtype: string - name: pos_candidates sequence: string - name: neg_candidates sequence: string - name: website dtype: string - name: domain dtype: string - name: subdomain dtype: string - name: annotation_id dtype: string - name: confirmed_task dtype: string - name: screenshot dtype: image - name: action_reprs sequence: string - name: target_action_index dtype: string - name: target_action_reprs dtype: string splits: - name: test_website num_bytes: 1589513606.713 num_examples: 1019 - name: test_task num_bytes: 2004628575.972 num_examples: 1339 - name: test_domain num_bytes: 5128899015.440001 num_examples: 4060 - name: train num_bytes: 13439470200.25 num_examples: 7775 download_size: 4014045168 dataset_size: 22162511398.375 --- ## Dataset Description - Homepage: https://osu-nlp-group.github.io/SeeAct/ - Repository: https://github.com/OSU-NLP-Group/SeeAct - Paper: https://arxiv.org/abs/2401.01614 - Point of Contact: [Boyuan Zheng](mailto:[email protected]) ### Dataset Summary Multimodal-Mind2Web is the multimodal version of [Mind2Web](https://osu-nlp-group.github.io/Mind2Web/), a dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. In this dataset, we align each HTML document in the dataset with its corresponding webpage screenshot image from the Mind2Web raw dump. This multimodal version addresses the inconvenience of loading images from the ~300GB Mind2Web Raw Dump. ## Dataset Structure ### Data Splits - train: 7775 actions from 1009 tasks. - test_task: 1339 actions from 177 tasks. Tasks from the same website are seen during training. - test_website: 1019 actions from 142 tasks. Websites are not seen during training. - test_domain: 4060 actions from 694 tasks. Entire domains are not seen during training. The _train_ set may include some screenshot images not properly rendered caused by rendering issues during Mind2Web annotation. The three _test splits (test_task, test_website, test_domain)_ have undergone human verification to confirm element visibility and correct rendering for action prediction. ### Data Fields Each line in the dataset is an action consisting of screenshot image, HTML text and other fields required for action prediction, for the convenience of inference. - "annotation_id" (str): unique id for each task - "website" (str): website name - "domain" (str): website domain - "subdomain" (str): website subdomain - "confirmed_task" (str): task description - "screenshot" (str): path to the webpage screenshot image corresponding to the HTML. - "action_uid" (str): unique id for each action (step) - "raw_html" (str): raw html of the page before the action is performed - "cleaned_html" (str): cleaned html of the page before the action is performed - "operation" (dict): operation to perform - "op" (str): operation type, one of CLICK, TYPE, SELECT - "original_op" (str): original operation type, contain additional HOVER and ENTER that are mapped to CLICK, not used - "value" (str): optional value for the operation, e.g., text to type, option to select - "pos_candidates" (list[dict]): ground truth elements. Here we only include positive elements that exist in "cleaned_html" after our preprocessing, so "pos_candidates" might be empty. The original labeled element can always be found in the "raw_html". - "tag" (str): tag of the element - "is_original_target" (bool): whether the element is the original target labeled by the annotator - "is_top_level_target" (bool): whether the element is a top level target find by our algorithm. please see the paper for more details. - "backend_node_id" (str): unique id for the element - "attributes" (str): serialized attributes of the element, use `json.loads` to convert back to dict - "neg_candidates" (list[dict]): other candidate elements in the page after preprocessing, has similar structure as "pos_candidates" - "action_reprs" (list[str]): human readable string representation of the action sequence - "target_action_index" (str): the index of the target action in the action sequence - "target_action_reprs" (str): human readable string representation of the target action ### Disclaimer This dataset was collected and released solely for research purposes, with the goal of making the web more accessible via language technologies. The authors are strongly against any potential harmful use of the data or technology to any party. ### Citation Information ``` @article{zheng2024seeact, title={GPT-4V(ision) is a Generalist Web Agent, if Grounded}, author={Boyuan Zheng and Boyu Gou and Jihyung Kil and Huan Sun and Yu Su}, booktitle={Forty-first International Conference on Machine Learning}, year={2024}, url={https://openreview.net/forum?id=piecKJ2DlB}, } @inproceedings{deng2023mindweb, title={Mind2Web: Towards a Generalist Agent for the Web}, author={Xiang Deng and Yu Gu and Boyuan Zheng and Shijie Chen and Samuel Stevens and Boshi Wang and Huan Sun and Yu Su}, booktitle={Thirty-seventh Conference on Neural Information Processing Systems}, year={2023}, url={https://openreview.net/forum?id=kiYqbO3wqw} } ```

SylvanL/Traditional-Chinese-Medicine-Dataset-SFT

SylvanL

2024-10-26T10:47:40Z

478

70

[ "task_categories:table-question-answering", "language:zh", "license:apache-2.0", "size_categories:1M<n<10M", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "region:us", "medical" ]

[ "table-question-answering" ]

2024-10-02T08:04:26Z

2

--- license: apache-2.0 task_categories: - table-question-answering language: - zh tags: - medical size_categories: - 1B<n<10B --- # 启古纳今，厚德精术 --- ## 数据介绍 ## 非网络来源的高质量中医数据集-指令微调 ## High-Quality Traditional Chinese Medicine Dataset from Non-Internet Sources - SFT/IFT 该数据集经过大量人力和资源的投入精心构建，以共建LLM高质量中文社区为己任。包含约1GB的中医各个领域临床案例、名家典籍、医学百科，名词解释等优质问答内容，涵盖全面，配比均衡。数据集主要由非网络来源的内部数据构成，并99%为简体中文内容，内容质量优异，信息密度可观。该数据集的数据源与SylvanL/Traditional-Chinese-Medicine-Dataset-Pretrain中的内容存在一定关联，但不高度重叠。在二者的构建过程中，存在着一定的循序渐进与互为补充的逻辑. 该数据集可以独立使用，但建议先使用配套的预训练数据集对模型进行继续预训练后，再使用该数据集进行进一步的指令微调。该数据集仅为单轮对话，且只涉及中医知识与通用医疗知识，因此强烈建议使用者在训练时混入一定比例的【中文常识、中文多轮对话数据】来避免灾难性遗忘。（未包含于该数据集内，可选择"BAAI/COIG"与"m-a-p/COIG-CQIA"）并且因为中医领域的特殊性，使用者也可以按照自己的业务需求酌情混入一定比例的【古文/文言文<->现代文翻译数据】以进一步加强模型表现。(已经在下方一并提供) 所有数据集的可是均可被LLamaFactory按照"dataset_info.json"内的信息（需要与数据集文件在同一路径下）直接读取，list:dict->[{"instruction":"...", "input":"...", "output":"..."},...] 任何问题请联系：[email protected] | **文件名** | **数据细节** | **备注** | **是否有曾公开过** |----------------------------------|----------------------------------|----------------------------------|----------------------------------| | SFT_medicalKnowledge_source1_548404 | 原始数据包含来自"中国中医药信息查询平台"数据库的17,921条结构化信息，涵盖疾病、症状、医疗美容、药品、中药材、保健品、方剂、药膳食疗、针灸穴位、术语等的百科词条。原始内容全部经过人工校对，质量极高。由"Qwen2.5-14B"进行本地处理QA化成548,404条数据，基于下文【附录四】内的提示词工程。 | 未进行minHash，且认为没有进行minHash的必要。 | 网络来源数据，内部处理校对。 | | SFT_medicalKnowledge_source2_99334 | 原始数据包含来自，但不限于ICD-10术语和中医国标所囊括的病症与术语的12,889条相关解释，同时涵盖常见方剂、中药材、针灸配穴等各种中医术语和名词的详细解释。原始内容全部经由人工构建，质量极高。由"deepseek-chat"API进行本地处理QA化成99,334条数据，基于"附录四"内的提示词工程。| 已进行一次minHash(threshold=0.91596, num_perm=128)，且认为没有进行二次minHash的必要。 | 内部数据，未曾面世。 | | SFT_medicalKnowledge_source3_556540 | 原始数据包含来自688本中医领域常用教材、著作、典籍的146244条书本内容。从pdf、word、图片、html以及chm等原始文件格式整理出。具体书录详见【SylvanL/Traditional-Chinese-Medicine-Dataset-Pretrain 附录一】。由"DeepSeek-V2-Lite-Chat"进行本地处理QA化成556,540条数据，基于"附录四"内的提示词工程。| 未进行minHash，且认为没有进行minHash的必要。 | 网络来源数据，内部处理校对。 | | SFT_nlpDiseaseDiagnosed_61486 | 原始数据包含61,486条来自高水平执业中医医生的临床问诊记录，原内容为自然语言优质医案，包含患者问诊引导、病症、主诉、诊断、方剂、中药等处方笺必备内容。内容完全由人工编辑，质量极高。由规则直接生成，映射关系为：{'instruction': '基于输入的患者医案记录，直接给出你的疾病诊断，无需给出原因。','input': 自然语言的患者病历记录, 'output': 诊断的疾病，具体清单详见【附录表二】} | 未进行minHash，且认为没有进行minHash的必要。 | 内部数据，未曾面世。 | | SFT_nlpSyndromeDiagnosed_48665 | 原始数据包含48,665条来自某知名教授的临床问诊记录，原内容为自然语言优质医案，包含患者问诊引导、病症、主诉、诊断、证型、方剂、中药等处方笺必备内容。内容完全由人工编辑，质量极高。由规则直接生成，映射关系为：{'instruction': '基于输入的患者医案记录，直接给出你的证型诊断，无需给出原因。','input': 自然语言的患者病历记录, 'output': 诊断的证型，具体清单详见【附录表一】} | 未进行minHash，且认为没有进行minHash的必要。 | 内部数据，未曾面世。 | | SFT_structGeneral_310860 | 原始数据包含从某互联网医院数据库中获取的部分结构化医案（1,592,410条医案），去重处理后剩余310,860条。内容源头由医生线上问诊时由本人/学生点选或键入。内容由规则联表拼接，质量中规中矩。由规则直接生成，映射关系为：{'instruction': '基于输入的患者医案记录，直接给出你认为的【治疗方案】：中药、方剂，和【诊断】：证型。','input': 半结构化的患者病历记录, 'output': 半结构化的通用诊断，具体构成详见【附录表三】} | 已进行一次minHash(threshold=0.618, num_perm=128)，可酌情进行二次minHash。 | 内部数据，未曾面世。 | | SFT_structPrescription_92896 |原始数据包含92,896条从某中医院EMR系统中获得的部分归档医案，内容源头由医生线下问诊时由本人/学生点选或键入。内容由规则联表拼接，质量中规中矩。由规则直接生成，映射关系为：{'instruction': '基于输入的患者医案记录，直接给出你认为的方剂中药组成。','input': 半结构化的患者病历记录, 'output': 半结构化的最终开具处方}| 未进行minHash，且认为没有进行minHash的必要。 | 内部数据，未曾面世。 | | 外部数据集 __SFT_traditionalTrans_1959542_ | 古文/文言文<->现代文翻译数据，由规则直接生成，映射关系为：{"instruction": "将输入的古文翻译成现代文。", "input": "古文：", "output": "现代文："}、{"instruction": "请为输入的现代文找到其对应的古文原文与出处。", "input": "现代文：", "output": "内容出自：" + "古文原文:"}| https://github.com/NiuTrans/Classical-Modern | 网络来源数据，内部处理校对。 | --- # 附录表一：SFT_nlpSyndromeDiagnosed_48665 证型诊断样本分布情况 | 证型 | 样本数量 | | --- | --- | | 气虚血瘀证 | 8303 | | 湿热下注证 | 7843 | | 气滞血瘀证 | 4791 | | 风痰瘀阻证 | 3358 | | 肝胃郁热证 | 2009 | | 正虚瘀结证 | 1993 | | 痰湿蕴肺证 | 1875 | | 肝肾亏虚证 | 1457 | | 热毒蕴结证 | 1292 | | 脾肾两虚证 | 1022 | | 气阴两虚证 | 860 | | 湿热瘀阻证 | 622 | | 肾虚证 | 593 | | 肝胃不和证 | 546 | | 风寒袭肺证 | 526 | | 脾胃虚寒证 | 522 | | 湿热蕴结证 | 520 | | 阳虚水泛证 | 504 | | 痰瘀痹阻证 | 477 | | 心脉痹阻证 | 446 | | 风痰上扰证 | 414 | | 风寒外袭证 | 396 | | 风痰入络证 | 344 | | 瘀血阻络证 | 315 | | 痰瘀互结证 | 309 | | 阴虚阳亢证 | 299 | | 痰浊蒙窍证 | 282 | | 气血亏虚证 | 218 | | 肝阳上亢证 | 209 | | 痰湿蒙窍证 | 208 | | 肝肾阴虚证 | 194 | | 痰湿中阻证 | 185 | | 阴虚火旺证 | 180 | | 血瘀痰凝证 | 175 | | 脾胃虚弱证 | 174 | | 肝经湿热证 | 169 | | 正虚毒瘀证 | 160 | | 血热证 | 156 | | 脾胃不和证 | 144 | | 痰热壅肺证 | 138 | | 阴虚血瘀证 | 130 | | 湿热阻络证 | 125 | | 肝郁气滞证 | 123 | | 湿热内蕴证 | 112 | | 心血虚证 | 110 | | 寒凝血瘀证 | 99 | | 脾肾阳虚证 | 94 | | 阳虚血瘀证 | 93 | | 风寒湿痹证 | 92 | | 瘀阻心脉证 | 91 | | 肝郁血瘀证 | 90 | | 肝阴不足证 | 89 | | 痰瘀滞络证 | 88 | | 气虚不摄证 | 80 | | 正虚毒结证 | 78 | | 气滞证 | 75 | | 肝气犯胃证 | 73 | | 心肾不交证 | 72 | | 瘀血阻滞证 | 71 | | 饮食积滞证 | 70 | | 气血不足证 | 69 | | 脾虚湿盛证 | 68 | | 脾胃积热证 | 68 | | 风热犯肺证 | 66 | | 肾虚水泛证 | 66 | | 心肺气虚证 | 65 | | 肺肾气虚证 | 63 | | 痰浊内蕴证 | 62 | | 气虚痰结证 | 60 | | 风寒阻络证 | 60 | | 气血两虚证 | 54 | | 痰湿内阻证 | 54 | | 痰火扰心证 | 54 | | 津亏热结证 | 53 | | 风寒袭表证 | 52 | | 肺卫不固证 | 48 | | 寒湿阻络证 | 46 | | 肝郁脾虚证 | 45 | | 血虚风燥证 | 43 | | 肾气虚证 | 40 | | 阴虚津亏证 | 37 | | 大肠湿热证 | 37 | | 肝胆湿热证 | 36 | | 脾虚湿蕴证 | 36 | | 热毒炽盛证 | 36 | | 气血瘀滞证 | 34 | | 阴虚血热证 | 33 | | 肾虚肝亢证 | 33 | | 脾气虚证 | 33 | | 阴虚风动证 | 33 | | 痰浊中阻证 | 32 | | 风寒湿阻证 | 30 | | 中气下陷证 | 29 | | 风热毒蕴证 | 28 | | 肾虚寒凝证 | 28 | | 风热袭表证 | 28 | | 湿热蕴毒证 | 27 | | 脾胃气虚证 | 27 | | 风湿蕴肤证 | 27 | | 风寒痹阻证 | 27 | | 气虚证 | 27 | | 阳虚寒凝证 | 26 | | 痰湿证 | 26 | | 中气不足证 | 26 | | 气滞痰凝证 | 25 | | 心虚胆怯证 | 25 | | 气郁痰阻证 | 24 | | 痰蒙清窍证 | 24 | | 风热伤络证 | 23 | | 痰蒙神窍证 | 22 | | 风热血热证 | 22 | | 肝胃气滞证 | 22 | | 血虚证 | 22 | | 痰浊上蒙证 | 21 | | 髓海不足证 | 20 | | 脾胃阳虚证 | 20 | | 风湿热证 | 20 | | 热毒蕴肤证 | 18 | | 痰湿阻滞证 | 18 | | 肝肾亏损证 | 18 | | 外伤损络证 | 18 | | 痰热内扰证 | 18 | | 气不摄血证 | 18 | | 瘀滞胞宫证 | 17 | | 肝肾两虚证 | 17 | | 心阳不振证 | 17 | | 肠燥津伤证 | 16 | | 脾肾气虚证 | 16 | | 风热外袭证 | 16 | | 气虚血溢证 | 14 | | 肾不纳气证 | 14 | | 气阴亏虚证 | 13 | | 肺阴虚证 | 13 | | 心脾两虚证 | 13 | | 湿毒蕴结证 | 12 | | 风痰闭窍证 | 12 | | 湿热瘀滞证 | 12 | | 阴虚湿热证 | 11 | | 寒痰阻肺证 | 11 | | 热毒壅结证 | 11 | | 痰气交阻证 | 11 | | 毒邪流窜证 | 10 | | 风痰闭阻证 | 10 | | 脾胃湿热证 | 10 | | 肾阳虚证 | 9 | | 瘀血内阻证 | 9 | | 阳虚痰凝证 | 9 | | 气血失调证 | 9 | # 附录表二： SFT_nlpDiseaseDiagnosed_61486 疾病诊断样本分布情况 | 疾病 | 样本数量 | | --- | --- | | 咳嗽 | 14468 | | 便秘 | 5395 | | 失眠 | 4036 | | 胃炎 | 3477 | | 痛经 | 3252 | | 高血压 | 2085 | | 肝炎 | 1688 | | 冠心病 | 1418 | | 糖尿病 | 1143 | | 肾炎 | 1072 | | 紫癜 | 886 | | 不孕症 | 814 | | 痤疮 | 704 | | 湿疹 | 612 | | 胆囊炎 | 579 | | 肝硬化 | 561 | | 口腔溃疡 | 555 | | 溃疡性结肠炎 | 535 | | 呼吸道感染 | 492 | | 心律失常 | 488 | | 荨麻疹 | 465 | | 鼻炎 | 450 | | 前列腺炎 | 405 | | 类风湿关节炎 | 398 | | 盆腔炎 | 388 | | 银屑病 | 385 | | 子宫肌瘤 | 379 | | 颈椎病 | 379 | | 贫血 | 374 | | 不育 | 373 | | 脂肪肝 | 329 | | 多囊卵巢综合征 | 323 | | 类风湿性关节炎 | 320 | | 更年期综合征 | 314 | | 肾病综合征 | 311 | | 乳腺增生 | 296 | | 乳腺癌 | 293 | | 咽炎 | 285 | | 偏头痛 | 281 | | 肠癌 | 276 | | 糖尿病肾病 | 276 | | 带状疱疹 | 262 | | 抑郁症 | 260 | | 胃癌 | 253 | | 强直性脊柱炎 | 243 | | 红斑狼疮 | 239 | | 卵巢囊肿 | 201 | | 腰椎间盘突出症 | 191 | | 肝癌 | 191 | | 肠易激综合征 | 189 | | 小儿咳嗽 | 185 | | 黄褐斑 | 185 | | 肺炎 | 184 | | IgA肾病 | 174 | | 尿路感染 | 165 | | 胃溃疡 | 165 | | 月经失调 | 159 | | 十二指肠溃疡 | 149 | | 子宫内膜异位症 | 148 | | 痔疮 | 144 | | 肾功能衰竭 | 143 | | 反流性食管炎 | 142 | | 肺结核 | 138 | | 支气管哮喘 | 128 | | 痛风性关节炎 | 128 | | 肺癌 | 123 | | 功能失调性子宫出血 | 120 | | 扁桃体炎 | 115 | | 风湿性关节炎 | 115 | | 骨质疏松 | 115 | | 肾结石 | 109 | | 脑梗塞 | 108 | | 先兆流产 | 106 | | 三叉神经痛 | 102 | | 脑出血 | 101 | | 乳腺炎 | 99 | | 高脂血症 | 92 | | 慢性肾衰竭 | 92 | | 胆结石 | 91 | | 帕金森病 | 91 | | 皮肤瘙痒症 | 89 | | 肺气肿 | 86 | | 功能性消化不良 | 85 | | 阴道炎 | 83 | | 白癜风 | 81 | | 胰腺炎 | 80 | | 创伤 | 80 | | 心绞痛 | 80 | | 坐骨神经痛 | 78 | | 风湿性心脏病 | 77 | | 食管癌 | 77 | | 泌尿系感染 | 75 | | 精神分裂症 | 75 | | 鼻咽癌 | 74 | | 小儿腹泻 | 71 | | 早产 | 70 | | 胆囊结石 | 70 | | 慢性肾功能不全 | 69 | | 鼻窦炎 | 66 | | 卵巢癌 | 66 | | 小儿肺炎 | 66 | | 慢性支气管炎 | 64 | | 支气管扩张 | 60 | | 梅尼埃病 | 59 | | 急性肾小球肾炎 | 59 | | 高尿酸血症 | 58 | | 上消化道出血 | 58 | | 小儿厌食症 | 58 | | 焦虑症 | 57 | | 低血压 | 57 | | 小儿感冒 | 55 | | 面神经炎 | 55 | | 小儿哮喘 | 54 | | 百日咳 | 54 | | 胰腺癌 | 54 | | 夜惊 | 53 | | 肝囊肿 | 52 | | 急性阑尾炎 | 50 | | 白塞病 | 50 | | 输尿管结石 | 47 | | 心肌梗死 | 45 | | 小儿癫痫 | 44 | | 性早熟 | 44 | | 视神经萎缩 | 43 | | 消化性溃疡 | 43 | | 尿毒症 | 41 | | 支气管炎 | 41 | | 附件炎 | 40 | | 胸腔积液 | 40 | | 先天性心脏病 | 38 | | 多发性骨髓瘤 | 38 | | 异位妊娠 | 37 | | 腰肌劳损 | 34 | | 心房颤动 | 34 | | 脂溢性皮炎 | 32 | | 细菌性痢疾 | 32 | | 心功能不全 | 30 | | 婴儿湿疹 | 29 | | 急性白血病 | 29 | | 宫颈炎 | 27 | | 白内障 | 26 | | 流行性腮腺炎 | 26 | | 单纯性甲状腺肿 | 26 | | 经前期综合征 | 25 | | 慢性阻塞性肺疾病 | 24 | | 垂体瘤 | 24 | | 真性红细胞增多症 | 24 | | 早产儿 | 24 | | 排卵期出血 | 23 | | 多发性神经炎 | 22 | | 手足口病 | 21 | | 尿道炎 | 20 | | 慢性心力衰竭 | 17 | | 冠状动脉粥样硬化性心脏病 | 15 | | 肝脓肿 | 15 | | 尿路结石 | 13 | | 牙周炎 | 13 | | 视神经炎 | 13 | | 婴幼儿腹泻 | 12 | | 慢性阻塞性肺病 | 12 | | 急性咽炎 | 12 | | 小儿多动症 | 11 | | 阿尔茨海默病 | 11 | | 呼吸衰竭 | 10 | | 肝性脑病 | 10 | | 二尖瓣狭窄 | 9 | | 代谢综合征 | 9 | | 雷诺综合征 | 9 | | 新生儿黄疸 | 9 | | 骨肿瘤 | 8 | | 慢性肺源性心脏病 | 8 | | 肺性脑病 | 8 | | 小儿疝气 | 7 | | 急性化脓性中耳炎 | 6 | | 新生儿肺炎 | 6 | | 自闭症 | 6 | | 慢性喉炎 | 6 | | 血管性水肿 | 6 | | 血友病 | 6 | | 慢性粒细胞白血病 | 5 | | 唇癌 | 4 | | 缺血性结肠炎 | 3 | | 脊髓灰质炎 | 3 | | 急性呼吸窘迫综合征 | 3 | | 儿童孤独症 | 3 | | 细菌性阴道病 | 2 | | 小儿惊厥 | 2 | | 唇腭裂 | 2 | | 弥散性血管内凝血 | 2 | | 小儿支气管炎 | 2 | | 肥厚型心肌病 | 2 | | 淹溺 | 1 | | 肺大疱 | 1 | | 急性呼吸衰竭 | 1 | # 附录表三：SFT_structGeneral_310849 指令跟随模板分布情况 | 指令跟随模板 | 样本数量 | | --- | --- | | 【治疗方案】：中药、方剂，和【诊断】：证型 | 105788 | | 【治疗方案】：中药、方剂 | 68781 | | 【治疗方案】：中药 | 45828 | | 【治疗方案】：中药，和【诊断】：证型 | 38434 | | 【治疗方案】：中药、方剂，和【诊断】：证型、治法 | 23229 | | 【治疗方案】：中药、方剂，和【诊断】：西医诊断 | 12187 | | 【治疗方案】：中药、方剂，和【诊断】：西医诊断、中医诊断 | 3715 | | 【治疗方案】：中药，和【诊断】：西医诊断 | 2782 | | 【治疗方案】：中药，和【诊断】：证型、治法 | 1909 | | 【诊断】：证型 | 1839 | | 【治疗方案】：中药、方剂，和【诊断】：证型、治法、中医诊断 | 1467 | | 【治疗方案】：中药、方剂，和【诊断】：中医诊断 | 1376 | | 【治疗方案】：中药，和【诊断】：西医诊断、中医诊断 | 1250 | | 【治疗方案】：中药，和【诊断】：中医诊断 | 508 | | 【诊断】：西医诊断 | 467 | | 【治疗方案】：中药，和【诊断】：证型、治法、中医诊断 | 378 | | 【治疗方案】：中药、方剂，和【诊断】：证型、中医诊断 | 359 | | 【治疗方案】：中药，和【诊断】：证型、中医诊断 | 337 | | 【诊断】：证型、治法 | 47 | | 【诊断】：中医诊断 | 39 | | 【诊断】：西医诊断、中医诊断 | 38 | | 【治疗方案】：中药、成药 | 34 | | 【治疗方案】：中药、成药、方剂 | 13 | | 【诊断】：证型、治法、中医诊断 | 12 | | 【治疗方案】：中药、成药，和【诊断】：证型、治法 | 10 | | 【治疗方案】：中药、方剂，和【诊断】：治法 | 8 | | 【治疗方案】：中药、成药，和【诊断】：西医诊断 | 6 | | 【治疗方案】：中药、成药、方剂，和【诊断】：证型、治法 | 4 | | 【治疗方案】：中药、方剂，和【诊断】：治法、中医诊断 | 3 | | 【治疗方案】：中药、成药，和【诊断】：西医诊断、中医诊断 | 3 | | 【治疗方案】：中药、成药、方剂，和【诊断】：西医诊断、中医诊断 | 2 | | 【治疗方案】：中药、成药，和【诊断】：中医诊断 | 2 | | 【诊断】：证型、中医诊断 | 1 | | 【诊断】：治法 | 1 | | 【治疗方案】：中药、成药、方剂，和【诊断】：中医诊断 | 1 | | 【治疗方案】：中药、成药，和【诊断】：证型、治法、中医诊断 | 1 | | 【治疗方案】：中药，和【诊断】：治法 | 1 | # 附录四：prompt-template ``` 模块目标：通过改写和优化现有的医疗问答训练数据，以面向下游任务构建指令跟随微调训练集。输入：单个问答对，格式为单个dict，JSON格式形如：{问题: "...", 答案: "..."} 输出：多个问答对，数量为N个，格式为由N个dict的list，JSON格式形如：[{"问题": "...", "答案": "..."}, {"问题": "...", "答案": "..."}, {"问题": "...", "答案": "..."}, ...] 模块功能： { 1. 简单输入处理：对于简单的答案，重点在于优化答案的质量，确保自然语言流畅性。并围绕相似的答案生成不同的问题变体。输出数量：N=2。 2. 复杂输入处理：对于复杂的答案，重点在于合理拆解答案内容，将长答案分解为多个部分。并针对每个不同的部分生成与其配套的问题。输出数量：N=4。 3. 格式要求：确保输出为合法的JSON格式，所有key和value均需用双引号包裹。字典用花括号 {}，数组用方括号 []，并确保所有括号正确闭合。 4. 评估标准：一切改写和优化应以提升下游模型在医疗垂直领域的任务上的表现为唯一评估标准。 } ```

bigcode/MultiPL-E-completions

bigcode

2024-10-24T08:10:53Z

20,206

9

[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "code" ]

[]

2023-04-22T14:01:20Z

null

--- pretty_name: MultiPL-E generated programs and execution results dataset_info: features: - name: experiment dtype: string - name: problem dtype: string - name: language dtype: string - name: top_p dtype: float64 - name: max_tokens dtype: int64 - name: prompt dtype: string - name: tests dtype: string - name: stop_tokens sequence: string - name: completions sequence: string - name: programs sequence: string - name: stdouts sequence: string - name: stderrs sequence: string - name: exit_codes sequence: int64 - name: statuses sequence: string - name: timestamps sequence: int64 splits: - name: humaneval.py.bigcode_15b_800m.0.2.reworded num_bytes: 50941974 num_examples: 161 - name: humaneval.py.bigcode_15b_200m.0.2.reworded num_bytes: 57850786 num_examples: 161 - name: humaneval.py.bigcode_15b_400m.0.2.reworded num_bytes: 52404545 num_examples: 161 - name: humaneval.py.bigcode_15b_600m.0.2.reworded num_bytes: 55071293 num_examples: 161 - name: humaneval.rkt.bigcode_15b_800m.0.2.reworded num_bytes: 77194321 num_examples: 161 - 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split: humaneval.go.starcoderbase_7b.0.2.reworded path: data/humaneval.go.starcoderbase_7b.0.2.reworded-* - split: humaneval.java.codellama_13b_base.0.2.reworded path: data/humaneval.java.codellama_13b_base.0.2.reworded-* - split: humaneval.java.codellama_7b_base.0.2.reworded path: data/humaneval.java.codellama_7b_base.0.2.reworded-* - split: humaneval.java.deepseekcoder_1.3b_base.0.2.reworded path: data/humaneval.java.deepseekcoder_1.3b_base.0.2.reworded-* - split: humaneval.java.deepseekcoder1.5_7b_base.0.2.reworded path: data/humaneval.java.deepseekcoder1.5_7b_base.0.2.reworded-* - split: humaneval.java.stablecode3b.0.2.reworded path: data/humaneval.java.stablecode3b.0.2.reworded-* - split: humaneval.java.StarCoder2_15b_16k.0.2.reworded path: data/humaneval.java.StarCoder2_15b_16k.0.2.reworded-* - split: humaneval.java.starcoder2_3b_long.0.2.reworded path: data/humaneval.java.starcoder2_3b_long.0.2.reworded-* - split: humaneval.java.StarCoder2_7b_16k.0.2.reworded path: data/humaneval.java.StarCoder2_7b_16k.0.2.reworded-* - split: humaneval.jl.codellama_13b_base.0.2.reworded path: data/humaneval.jl.codellama_13b_base.0.2.reworded-* - split: humaneval.jl.codellama_7b_base.0.2.reworded path: data/humaneval.jl.codellama_7b_base.0.2.reworded-* - split: humaneval.jl.deepseekcoder_1.3b_base.0.2.reworded path: data/humaneval.jl.deepseekcoder_1.3b_base.0.2.reworded-* - split: humaneval.jl.deepseekcoder1.5_7b_base.0.2.reworded path: data/humaneval.jl.deepseekcoder1.5_7b_base.0.2.reworded-* - split: humaneval.jl.stablecode3b.0.2.reworded path: data/humaneval.jl.stablecode3b.0.2.reworded-* - split: humaneval.jl.StarCoder2_15b_16k.0.2.reworded path: data/humaneval.jl.StarCoder2_15b_16k.0.2.reworded-* - split: humaneval.jl.starcoder2_3b_long.0.2.reworded path: data/humaneval.jl.starcoder2_3b_long.0.2.reworded-* - split: humaneval.jl.StarCoder2_7b_16k.0.2.reworded path: data/humaneval.jl.StarCoder2_7b_16k.0.2.reworded-* - split: humaneval.js.codellama_13b_base.0.2.reworded path: data/humaneval.js.codellama_13b_base.0.2.reworded-* - split: humaneval.js.codellama_7b_base.0.2.reworded path: data/humaneval.js.codellama_7b_base.0.2.reworded-* - split: humaneval.js.deepseekcoder_1.3b_base.0.2.reworded path: data/humaneval.js.deepseekcoder_1.3b_base.0.2.reworded-* - split: humaneval.js.deepseekcoder1.5_7b_base.0.2.reworded path: data/humaneval.js.deepseekcoder1.5_7b_base.0.2.reworded-* - split: humaneval.js.stablecode3b.0.2.reworded path: data/humaneval.js.stablecode3b.0.2.reworded-* - split: humaneval.js.StarCoder2_15b_16k.0.2.reworded path: data/humaneval.js.StarCoder2_15b_16k.0.2.reworded-* - split: humaneval.js.starcoder2_3b_long.0.2.reworded path: data/humaneval.js.starcoder2_3b_long.0.2.reworded-* - split: humaneval.js.StarCoder2_7b_16k.0.2.reworded path: data/humaneval.js.StarCoder2_7b_16k.0.2.reworded-* - split: humaneval.lua.codellama_13b_base.0.2.reworded path: data/humaneval.lua.codellama_13b_base.0.2.reworded-* - split: humaneval.lua.codellama_7b_base.0.2.reworded path: data/humaneval.lua.codellama_7b_base.0.2.reworded-* - split: humaneval.lua.deepseekcoder_1.3b_base.0.2.reworded path: data/humaneval.lua.deepseekcoder_1.3b_base.0.2.reworded-* - split: humaneval.lua.deepseekcoder1.5_7b_base.0.2.reworded path: data/humaneval.lua.deepseekcoder1.5_7b_base.0.2.reworded-* - split: humaneval.lua.stablecode3b.0.2.reworded path: data/humaneval.lua.stablecode3b.0.2.reworded-* - split: humaneval.lua.StarCoder2_15b_16k.0.2.reworded path: data/humaneval.lua.StarCoder2_15b_16k.0.2.reworded-* - split: humaneval.lua.starcoder2_3b_long.0.2.reworded path: data/humaneval.lua.starcoder2_3b_long.0.2.reworded-* - split: humaneval.lua.StarCoder2_7b_16k.0.2.reworded path: data/humaneval.lua.StarCoder2_7b_16k.0.2.reworded-* - split: humaneval.php.codellama_13b_base.0.2.reworded path: data/humaneval.php.codellama_13b_base.0.2.reworded-* - split: humaneval.php.codellama_7b_base.0.2.reworded path: data/humaneval.php.codellama_7b_base.0.2.reworded-* - split: humaneval.php.deepseekcoder_1.3b_base.0.2.reworded path: data/humaneval.php.deepseekcoder_1.3b_base.0.2.reworded-* - split: humaneval.php.deepseekcoder1.5_7b_base.0.2.reworded path: data/humaneval.php.deepseekcoder1.5_7b_base.0.2.reworded-* - split: humaneval.php.stablecode3b.0.2.reworded path: data/humaneval.php.stablecode3b.0.2.reworded-* - split: humaneval.php.StarCoder2_15b_16k.0.2.reworded path: data/humaneval.php.StarCoder2_15b_16k.0.2.reworded-* - split: humaneval.php.starcoder2_3b_long.0.2.reworded path: data/humaneval.php.starcoder2_3b_long.0.2.reworded-* - split: humaneval.php.StarCoder2_7b_16k.0.2.reworded path: data/humaneval.php.StarCoder2_7b_16k.0.2.reworded-* - split: humaneval.pl.codellama_13b_base.0.2.reworded path: data/humaneval.pl.codellama_13b_base.0.2.reworded-* - split: humaneval.pl.CodeLlama_34b_base.0.2.reworded path: data/humaneval.pl.CodeLlama_34b_base.0.2.reworded-* - split: humaneval.pl.codellama_7b_base.0.2.reworded path: data/humaneval.pl.codellama_7b_base.0.2.reworded-* - split: humaneval.pl.deepseekcoder_1.3b_base.0.2.reworded path: data/humaneval.pl.deepseekcoder_1.3b_base.0.2.reworded-* - split: humaneval.pl.deepseekcoder1.5_7b_base.0.2.reworded path: data/humaneval.pl.deepseekcoder1.5_7b_base.0.2.reworded-* - split: humaneval.pl.DeepSeekCoder_34b_base.0.2.reworded path: data/humaneval.pl.DeepSeekCoder_34b_base.0.2.reworded-* - split: humaneval.pl.stablecode3b.0.2.reworded path: data/humaneval.pl.stablecode3b.0.2.reworded-* - split: humaneval.pl.StarCoder2_15b_16k.0.2.reworded path: data/humaneval.pl.StarCoder2_15b_16k.0.2.reworded-* - split: humaneval.pl.starcoder2_3b_long.0.2.reworded path: data/humaneval.pl.starcoder2_3b_long.0.2.reworded-* - split: humaneval.pl.StarCoder2_7b_16k.0.2.reworded path: data/humaneval.pl.StarCoder2_7b_16k.0.2.reworded-* - split: humaneval.pl.starcoderbase_3b.0.2.reworded path: data/humaneval.pl.starcoderbase_3b.0.2.reworded-* - split: humaneval.pl.starcoderbase_7b.0.2.reworded path: data/humaneval.pl.starcoderbase_7b.0.2.reworded-* - split: humaneval.rb.codellama_13b_base.0.2.reworded path: data/humaneval.rb.codellama_13b_base.0.2.reworded-* - split: humaneval.rb.CodeLlama_34b_base.0.2.reworded path: data/humaneval.rb.CodeLlama_34b_base.0.2.reworded-* - split: humaneval.rb.codellama_7b_base.0.2.reworded path: data/humaneval.rb.codellama_7b_base.0.2.reworded-* - split: humaneval.rb.deepseekcoder_1.3b_base.0.2.reworded path: data/humaneval.rb.deepseekcoder_1.3b_base.0.2.reworded-* - split: humaneval.rb.deepseekcoder1.5_7b_base.0.2.reworded path: data/humaneval.rb.deepseekcoder1.5_7b_base.0.2.reworded-* - split: humaneval.rb.DeepSeekCoder_34b_base.0.2.reworded path: data/humaneval.rb.DeepSeekCoder_34b_base.0.2.reworded-* - split: humaneval.rb.stablecode3b.0.2.reworded path: data/humaneval.rb.stablecode3b.0.2.reworded-* - split: humaneval.rb.StarCoder2_15b_16k.0.2.reworded path: data/humaneval.rb.StarCoder2_15b_16k.0.2.reworded-* - split: humaneval.rb.starcoder2_3b_long.0.2.reworded path: data/humaneval.rb.starcoder2_3b_long.0.2.reworded-* - split: humaneval.rb.StarCoder2_7b_16k.0.2.reworded path: data/humaneval.rb.StarCoder2_7b_16k.0.2.reworded-* - split: humaneval.rb.starcoderbase_3b.0.2.reworded path: data/humaneval.rb.starcoderbase_3b.0.2.reworded-* - split: humaneval.rb.starcoderbase_7b.0.2.reworded path: data/humaneval.rb.starcoderbase_7b.0.2.reworded-* - split: humaneval.r.codellama_13b_base.0.2.reworded path: data/humaneval.r.codellama_13b_base.0.2.reworded-* - split: humaneval.r.codellama_7b_base.0.2.reworded path: data/humaneval.r.codellama_7b_base.0.2.reworded-* - split: humaneval.r.deepseekcoder_1.3b_base.0.2.reworded path: data/humaneval.r.deepseekcoder_1.3b_base.0.2.reworded-* - split: humaneval.r.deepseekcoder1.5_7b_base.0.2.reworded path: data/humaneval.r.deepseekcoder1.5_7b_base.0.2.reworded-* - split: humaneval.r.DeepSeekCoder_34b_base.0.2.reworded path: data/humaneval.r.DeepSeekCoder_34b_base.0.2.reworded-* - split: humaneval.rkt.codellama_13b_base.0.2.reworded path: data/humaneval.rkt.codellama_13b_base.0.2.reworded-* - split: humaneval.rkt.codellama_7b_base.0.2.reworded path: data/humaneval.rkt.codellama_7b_base.0.2.reworded-* - split: humaneval.rkt.deepseekcoder_1.3b_base.0.2.reworded path: data/humaneval.rkt.deepseekcoder_1.3b_base.0.2.reworded-* - split: humaneval.rkt.deepseekcoder1.5_7b_base.0.2.reworded path: data/humaneval.rkt.deepseekcoder1.5_7b_base.0.2.reworded-* - split: humaneval.rkt.stablecode3b.0.2.reworded path: data/humaneval.rkt.stablecode3b.0.2.reworded-* - split: humaneval.rkt.StarCoder2_15b_16k.0.2.reworded path: data/humaneval.rkt.StarCoder2_15b_16k.0.2.reworded-* - split: humaneval.rkt.starcoder2_3b_long.0.2.reworded path: data/humaneval.rkt.starcoder2_3b_long.0.2.reworded-* - split: humaneval.rkt.StarCoder2_7b_16k.0.2.reworded path: data/humaneval.rkt.StarCoder2_7b_16k.0.2.reworded-* - split: humaneval.rs.codellama_13b_base.0.2.reworded path: data/humaneval.rs.codellama_13b_base.0.2.reworded-* - split: humaneval.rs.codellama_7b_base.0.2.reworded path: data/humaneval.rs.codellama_7b_base.0.2.reworded-* - split: humaneval.rs.deepseekcoder_1.3b_base.0.2.reworded path: data/humaneval.rs.deepseekcoder_1.3b_base.0.2.reworded-* - split: humaneval.rs.deepseekcoder1.5_7b_base.0.2.reworded path: data/humaneval.rs.deepseekcoder1.5_7b_base.0.2.reworded-* - split: humaneval.rs.stablecode3b.0.2.reworded path: data/humaneval.rs.stablecode3b.0.2.reworded-* - split: humaneval.rs.StarCoder2_15b_16k.0.2.reworded path: data/humaneval.rs.StarCoder2_15b_16k.0.2.reworded-* - split: humaneval.rs.starcoder2_3b_long.0.2.reworded path: data/humaneval.rs.starcoder2_3b_long.0.2.reworded-* - split: humaneval.rs.StarCoder2_7b_16k.0.2.reworded path: data/humaneval.rs.StarCoder2_7b_16k.0.2.reworded-* - split: humaneval.r.stablecode3b.0.2.reworded path: data/humaneval.r.stablecode3b.0.2.reworded-* - split: humaneval.r.StarCoder2_15b_16k.0.2.reworded path: data/humaneval.r.StarCoder2_15b_16k.0.2.reworded-* - split: humaneval.r.starcoder2_3b_long.0.2.reworded path: data/humaneval.r.starcoder2_3b_long.0.2.reworded-* --- # Raw Data from MultiPL-E **This repository is frozen. See https://huggingface.co/datasets/nuprl/MultiPL-E-completions for a more complete version of this repository.** Uploads are a work in progress. If you are interested in a split that is not yet available, please contact [email protected]. This repository contains the raw data -- both completions and executions -- from MultiPL-E that was used to generate several experimental results from the MultiPL-E, SantaCoder, and StarCoder papers. The original MultiPL-E completions and executions are stored in JOSN files. We use [the following script](https://github.com/nuprl/MultiPL-E/blob/main/upload_completions.py-) to turn each experiment directory into a dataset split and upload to this repository. Every split is named `base_dataset`.`language`.`model`.`temperature`.`variation` - `base_dataset` is either `humaneval` or `mbpp`. - `language` is the file extension of the programming language. E.g., `py` for Python or `sh` for Bash. - `model` is the name of the model. Some model names used by MultiPL-E: - `bigcode_15b_1000m`: StarCoderBase - `bigcode_15b_200m`, `bigcode_15b_400m`, `bigcode_15b_600m`, `bigcode_15b_800m`: the 20%, 40%, 60%, 80% checkpoints for StarCoderBase - `pystarcoder2`: StarCoder - there are others, just have a look :) - `temperature`: this is either 0.2 or 0.8 - `variation`: the variation of the MultiPL-E prompt to use, which should be `reworded`. MultiPL-E has several prompt ablations and the `reworded` prompts are the canonical variation.

lmms-lab/LLaVA-OneVision-Data

lmms-lab

2024-10-22T06:47:46Z

18,720

184

[ "language:en", "language:zh", "license:apache-2.0", "size_categories:1M<n<10M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2408.03326", "arxiv:2310.05126", "region:us" ]

[]

2024-07-25T15:25:28Z

null

--- language: - en - zh license: apache-2.0 pretty_name: llava-onevision-data dataset_info: - config_name: CLEVR-Math(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 791346970 num_examples: 5280 download_size: 441208499 dataset_size: 791346970 - config_name: FigureQA(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 463326576.625 num_examples: 17587 download_size: 258197193 dataset_size: 463326576.625 - config_name: GEOS(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1503641 num_examples: 498 download_size: 684471 dataset_size: 1503641 - config_name: GeoQA+(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 53579705.75 num_examples: 17162 download_size: 33480538 dataset_size: 53579705.75 - config_name: Geometry3K(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 218085473.5 num_examples: 9724 download_size: 125914780 dataset_size: 218085473.5 - config_name: IconQA(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 208430568.375 num_examples: 22589 download_size: 117222488 dataset_size: 208430568.375 - config_name: MapQA(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 384120915.875 num_examples: 5225 download_size: 215768443 dataset_size: 384120915.875 - config_name: PMC-VQA(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 571444866.5 num_examples: 35948 download_size: 326541003 dataset_size: 571444866.5 - config_name: Super-CLEVR(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2795082410.75 num_examples: 8642 download_size: 1580301917 dataset_size: 2795082410.75 - config_name: TabMWP(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 307726997.5 num_examples: 22452 download_size: 173938487 dataset_size: 307726997.5 - config_name: UniGeo(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 38296693.375 num_examples: 11949 download_size: 24170743 dataset_size: 38296693.375 - config_name: VisualWebInstruct(filtered) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 36317112275.0 num_examples: 263584 download_size: 36239916454 dataset_size: 36317112275.0 - config_name: VizWiz(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1170333936.5 num_examples: 6604 download_size: 660752297 dataset_size: 1170333936.5 - config_name: ai2d(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 438572782.375 num_examples: 2429 download_size: 437348514 dataset_size: 438572782.375 - config_name: ai2d(gpt4v) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 866076731 num_examples: 4864 download_size: 860306578 dataset_size: 866076731 - config_name: ai2d(internvl) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1832787249.625 num_examples: 12403 download_size: 527493895 dataset_size: 1832787249.625 - config_name: allava_instruct_laion4v features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 5981767621.25 num_examples: 49990 download_size: 5873046236 dataset_size: 5981767621.25 - config_name: allava_instruct_vflan4v features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2680974558.25 num_examples: 19990 download_size: 2670088751 dataset_size: 2680974558.25 - config_name: aokvqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 6896420844.25 num_examples: 16534 download_size: 6894236970 dataset_size: 6896420844.25 - config_name: chart2text(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1145458729.5 num_examples: 26956 download_size: 1123681047 dataset_size: 1145458729.5 - config_name: chartqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 815335215.5 num_examples: 18260 download_size: 803084541 dataset_size: 815335215.5 - config_name: chrome_writting features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 44422597.875 num_examples: 8825 download_size: 39611257 dataset_size: 44422597.875 - config_name: clevr(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 10528974543.625 num_examples: 69995 download_size: 10460536445 dataset_size: 10528974543.625 - config_name: diagram_image_to_text(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 18858266 num_examples: 295 download_size: 18659115 dataset_size: 18858266 - config_name: dvqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 4487270615.625 num_examples: 199995 download_size: 4277056467 dataset_size: 4487270615.625 - config_name: figureqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2351194509.625 num_examples: 99995 download_size: 2222640639 dataset_size: 2351194509.625 - config_name: geo170k(align) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 204236256.75 num_examples: 60242 download_size: 58185410 dataset_size: 204236256.75 - config_name: geo170k(qa) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 266040519.125 num_examples: 67823 download_size: 160022430 dataset_size: 266040519.125 - config_name: geo3k features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 42634333.625 num_examples: 2091 download_size: 41097851 dataset_size: 42634333.625 - config_name: geomverse(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2263893609.75 num_examples: 9298 download_size: 2211726352 dataset_size: 2263893609.75 - config_name: hateful_memes(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 3057252325.125 num_examples: 8495 download_size: 3055839880 dataset_size: 3057252325.125 - config_name: hitab(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 161706881.125 num_examples: 2495 download_size: 157871287 dataset_size: 161706881.125 - config_name: hme100k features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 273229915.5 num_examples: 74492 download_size: 241005430 dataset_size: 273229915.5 - config_name: iam(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1131633206.75 num_examples: 5658 download_size: 1128371221 dataset_size: 1131633206.75 - config_name: iconqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 331284932.25 num_examples: 27302 download_size: 327005220 dataset_size: 331284932.25 - config_name: iiit5k features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 21821437.25 num_examples: 1990 download_size: 21623116 dataset_size: 21821437.25 - config_name: image_textualization(filtered) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 5218283253.375 num_examples: 99573 download_size: 5164176816 dataset_size: 5218283253.375 - config_name: infographic(gpt4v) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 713657496.25 num_examples: 1982 download_size: 656276080 dataset_size: 713657496.25 - config_name: infographic_vqa features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1528953078.75 num_examples: 4394 download_size: 1419340319 dataset_size: 1528953078.75 - config_name: infographic_vqa_llava_format features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1765315696.875 num_examples: 2113 download_size: 1764548536 dataset_size: 1765315696.875 - config_name: intergps(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 24973395.625 num_examples: 1275 download_size: 24736545 dataset_size: 24973395.625 - config_name: k12_printing features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1205153118.5 num_examples: 256636 download_size: 1108572712 dataset_size: 1205153118.5 - config_name: llavar_gpt4_20k features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 633833350.25 num_examples: 19790 download_size: 625365542 dataset_size: 633833350.25 - config_name: lrv_chart features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 99338686 num_examples: 1776 download_size: 97979446 dataset_size: 99338686 - config_name: lrv_normal(filtered) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 422589381.75 num_examples: 10490 download_size: 406958773 dataset_size: 422589381.75 - config_name: magpie_pro(l3_80b_mt) features: - name: id dtype: string - name: image dtype: 'null' - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1657129141 num_examples: 299988 download_size: 885893066 dataset_size: 1657129141 - config_name: magpie_pro(l3_80b_st) features: - name: id dtype: string - name: image dtype: 'null' - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1033666690 num_examples: 299990 download_size: 562771564 dataset_size: 1033666690 - config_name: magpie_pro(qwen2_72b_st) features: - name: id dtype: string - name: image dtype: 'null' - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 703489344 num_examples: 299982 download_size: 361433408 dataset_size: 703489344 - config_name: mapqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 3355751195.5 num_examples: 37412 download_size: 3305639218 dataset_size: 3355751195.5 - config_name: mathqa features: - name: id dtype: string - name: image dtype: 'null' - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 18318538 num_examples: 29827 download_size: 7857130 dataset_size: 18318538 - config_name: mavis_math_metagen features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2304025372.5 num_examples: 87348 download_size: 322776224 dataset_size: 2304025372.5 - config_name: mavis_math_rule_geo features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 14313211512.25 num_examples: 99990 download_size: 5841283073 dataset_size: 14313211512.25 - config_name: multihiertt(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 300319803.25 num_examples: 7614 download_size: 295638314 dataset_size: 300319803.25 - config_name: orand_car_a features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 23602442.125 num_examples: 1999 download_size: 23333412 dataset_size: 23602442.125 - config_name: raven(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1706160514.625 num_examples: 41995 download_size: 1693150088 dataset_size: 1706160514.625 - config_name: rendered_text(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 11082594894.625 num_examples: 9995 download_size: 11081962044 dataset_size: 11082594894.625 - config_name: robut_sqa(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 685580779.375 num_examples: 8509 download_size: 678666263 dataset_size: 685580779.375 - config_name: robut_wikisql(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 6200499653 num_examples: 74984 download_size: 6168399217 dataset_size: 6200499653 - config_name: robut_wtq(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 4091776188.875 num_examples: 38241 download_size: 4062777449 dataset_size: 4091776188.875 - config_name: scienceqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 286843125.625 num_examples: 4971 download_size: 282896809 dataset_size: 286843125.625 - config_name: scienceqa(nona_context) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2111029055 num_examples: 19208 download_size: 2053942726 dataset_size: 2111029055 - config_name: screen2words(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 7977502095.375 num_examples: 15725 download_size: 7962327904 dataset_size: 7977502095.375 - config_name: sharegpt4o features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 6968025789.5 num_examples: 57284 download_size: 6772195470 dataset_size: 6968025789.5 - config_name: sharegpt4v(coco) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2620153362.875 num_examples: 50017 download_size: 2595583499 dataset_size: 2620153362.875 - config_name: sharegpt4v(knowledge) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 372100773.5 num_examples: 1988 download_size: 369799318 dataset_size: 372100773.5 - config_name: sharegpt4v(llava) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 781795487.25 num_examples: 29990 download_size: 400344187 dataset_size: 781795487.25 - config_name: sharegpt4v(sam) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 4437405218.25 num_examples: 8990 download_size: 4428597081 dataset_size: 4437405218.25 - config_name: sroie features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 117810195 num_examples: 33616 download_size: 103647636 dataset_size: 117810195 - config_name: st_vqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 5771194098.75 num_examples: 17242 download_size: 5768888141 dataset_size: 5771194098.75 - config_name: tabmwp(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 311192518.375 num_examples: 22717 download_size: 306092255 dataset_size: 311192518.375 - config_name: tallyqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 35998988065.625 num_examples: 98675 download_size: 35982430394 dataset_size: 35998988065.625 - config_name: textcaps features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2222268476.25 num_examples: 21942 download_size: 2217838132 dataset_size: 2222268476.25 - config_name: textocr(gpt4v) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2581655353 num_examples: 25104 download_size: 2574418106 dataset_size: 2581655353 - config_name: tqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 331203026.25 num_examples: 27302 download_size: 326999466 dataset_size: 331203026.25 - config_name: ureader_cap features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 9269857109.75 num_examples: 91434 download_size: 2292099971 dataset_size: 9269857109.75 - config_name: ureader_ie features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 11871457209.75 num_examples: 17322 download_size: 1999083115 dataset_size: 11871457209.75 - config_name: vision_flan(filtered) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 24847242604.5 num_examples: 186060 download_size: 24750561877 dataset_size: 24847242604.5 - config_name: vistext(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 550187184.5 num_examples: 9964 download_size: 452795103 dataset_size: 550187184.5 - config_name: visual7w(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 4451436523.875 num_examples: 14361 download_size: 4441971985 dataset_size: 4451436523.875 - config_name: visualmrc(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2938154124.25 num_examples: 3022 download_size: 2909296079 dataset_size: 2938154124.25 - config_name: vqarad(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 95533417 num_examples: 308 download_size: 95410398 dataset_size: 95533417 - config_name: vsr(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 891981646 num_examples: 2152 download_size: 891572866 dataset_size: 891981646 - config_name: websight(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 11209715828.625 num_examples: 9995 download_size: 11144460985 dataset_size: 11209715828.625 configs: - config_name: CLEVR-Math(MathV360K) data_files: - split: train path: CLEVR-Math(MathV360K)/train-* - config_name: FigureQA(MathV360K) data_files: - split: train path: FigureQA(MathV360K)/train-* - config_name: GEOS(MathV360K) data_files: - split: train path: GEOS(MathV360K)/train-* - config_name: GeoQA+(MathV360K) data_files: - split: train path: GeoQA+(MathV360K)/train-* - config_name: Geometry3K(MathV360K) data_files: - split: train path: Geometry3K(MathV360K)/train-* - config_name: IconQA(MathV360K) data_files: - split: train path: IconQA(MathV360K)/train-* - config_name: MapQA(MathV360K) data_files: - split: train path: MapQA(MathV360K)/train-* - config_name: PMC-VQA(MathV360K) data_files: - split: train path: PMC-VQA(MathV360K)/train-* - config_name: Super-CLEVR(MathV360K) data_files: - split: train path: Super-CLEVR(MathV360K)/train-* - config_name: TabMWP(MathV360K) data_files: - split: train path: TabMWP(MathV360K)/train-* - config_name: UniGeo(MathV360K) data_files: - split: train path: UniGeo(MathV360K)/train-* - config_name: VisualWebInstruct(filtered) data_files: - split: train path: VisualWebInstruct(filtered)/train-* - config_name: VizWiz(MathV360K) data_files: - split: train path: VizWiz(MathV360K)/train-* - config_name: ai2d(cauldron,llava_format) data_files: - split: train path: ai2d(cauldron,llava_format)/train-* - config_name: ai2d(gpt4v) data_files: - split: train path: ai2d(gpt4v)/train-* - config_name: ai2d(internvl) data_files: - split: train path: ai2d(internvl)/train-* - config_name: allava_instruct_laion4v data_files: - split: train path: allava_instruct_laion4v/train-* - config_name: allava_instruct_vflan4v data_files: - split: train path: allava_instruct_vflan4v/train-* - config_name: aokvqa(cauldron,llava_format) data_files: - split: train path: aokvqa(cauldron,llava_format)/train-* - config_name: chart2text(cauldron) data_files: - split: train path: chart2text(cauldron)/train-* - config_name: chartqa(cauldron,llava_format) data_files: - split: train path: chartqa(cauldron,llava_format)/train-* - config_name: chrome_writting data_files: - split: train path: chrome_writting/train-* - config_name: clevr(cauldron,llava_format) data_files: - split: train path: clevr(cauldron,llava_format)/train-* - config_name: diagram_image_to_text(cauldron) data_files: - split: train path: diagram_image_to_text(cauldron)/train-* - config_name: dvqa(cauldron,llava_format) data_files: - split: train path: dvqa(cauldron,llava_format)/train-* - config_name: figureqa(cauldron,llava_format) data_files: - split: train path: figureqa(cauldron,llava_format)/train-* - config_name: geo170k(align) data_files: - split: train path: geo170k(align)/train-* - config_name: geo170k(qa) data_files: - split: train path: geo170k(qa)/train-* - config_name: geo3k data_files: - split: train path: geo3k/train-* - config_name: geomverse(cauldron) data_files: - split: train path: geomverse(cauldron)/train-* - config_name: hateful_memes(cauldron,llava_format) data_files: - split: train path: hateful_memes(cauldron,llava_format)/train-* - config_name: hitab(cauldron,llava_format) data_files: - split: train path: hitab(cauldron,llava_format)/train-* - config_name: hme100k data_files: - split: train path: hme100k/train-* - config_name: iam(cauldron) data_files: - split: train path: iam(cauldron)/train-* - config_name: iconqa(cauldron,llava_format) data_files: - split: train path: iconqa(cauldron,llava_format)/train-* - config_name: iiit5k data_files: - split: train path: iiit5k/train-* - config_name: image_textualization(filtered) data_files: - split: train path: image_textualization(filtered)/train-* - config_name: infographic(gpt4v) data_files: - split: train path: infographic(gpt4v)/train-* - config_name: infographic_vqa data_files: - split: train path: infographic_vqa/train-* - config_name: infographic_vqa_llava_format data_files: - split: train path: infographic_vqa_llava_format/train-* - config_name: intergps(cauldron,llava_format) data_files: - split: train path: intergps(cauldron,llava_format)/train-* - config_name: k12_printing data_files: - split: train path: k12_printing/train-* - config_name: llavar_gpt4_20k data_files: - split: train path: llavar_gpt4_20k/train-* - config_name: lrv_chart data_files: - split: train path: lrv_chart/train-* - config_name: lrv_normal(filtered) data_files: - split: train path: lrv_normal(filtered)/train-* - config_name: magpie_pro(l3_80b_mt) data_files: - split: train path: magpie_pro(l3_80b_mt)/train-* - config_name: magpie_pro(l3_80b_st) data_files: - split: train path: magpie_pro(l3_80b_st)/train-* - config_name: magpie_pro(qwen2_72b_st) data_files: - split: train path: magpie_pro(qwen2_72b_st)/train-* - config_name: mapqa(cauldron,llava_format) data_files: - split: train path: mapqa(cauldron,llava_format)/train-* - config_name: mathqa data_files: - split: train path: mathqa/train-* - config_name: mavis_math_metagen data_files: - split: train path: mavis_math_metagen/train-* - config_name: mavis_math_rule_geo data_files: - split: train path: mavis_math_rule_geo/train-* - config_name: multihiertt(cauldron) data_files: - split: train path: multihiertt(cauldron)/train-* - config_name: orand_car_a data_files: - split: train path: orand_car_a/train-* - config_name: raven(cauldron) data_files: - split: train path: raven(cauldron)/train-* - config_name: rendered_text(cauldron) data_files: - split: train path: rendered_text(cauldron)/train-* - config_name: robut_sqa(cauldron) data_files: - split: train path: robut_sqa(cauldron)/train-* - config_name: robut_wikisql(cauldron) data_files: - split: train path: robut_wikisql(cauldron)/train-* - config_name: robut_wtq(cauldron,llava_format) data_files: - split: train path: robut_wtq(cauldron,llava_format)/train-* - config_name: scienceqa(cauldron,llava_format) data_files: - split: train path: scienceqa(cauldron,llava_format)/train-* - config_name: scienceqa(nona_context) data_files: - split: train path: scienceqa(nona_context)/train-* - config_name: screen2words(cauldron) data_files: - split: train path: screen2words(cauldron)/train-* - config_name: sharegpt4o data_files: - split: train path: sharegpt4o/train-* - config_name: sharegpt4v(coco) data_files: - split: train path: sharegpt4v(coco)/train-* - config_name: sharegpt4v(knowledge) data_files: - split: train path: sharegpt4v(knowledge)/train-* - config_name: sharegpt4v(llava) data_files: - split: train path: sharegpt4v(llava)/train-* - config_name: sharegpt4v(sam) data_files: - split: train path: sharegpt4v(sam)/train-* - config_name: sroie data_files: - split: train path: sroie/train-* - config_name: st_vqa(cauldron,llava_format) data_files: - split: train path: st_vqa(cauldron,llava_format)/train-* - config_name: tabmwp(cauldron) data_files: - split: train path: tabmwp(cauldron)/train-* - config_name: tallyqa(cauldron,llava_format) data_files: - split: train path: tallyqa(cauldron,llava_format)/train-* - config_name: textcaps data_files: - split: train path: textcaps/train-* - config_name: textocr(gpt4v) data_files: - split: train path: textocr(gpt4v)/train-* - config_name: tqa(cauldron,llava_format) data_files: - split: train path: tqa(cauldron,llava_format)/train-* - config_name: ureader_cap data_files: - split: train path: ureader_cap/train-* - config_name: ureader_ie data_files: - split: train path: ureader_ie/train-* - config_name: vision_flan(filtered) data_files: - split: train path: vision_flan(filtered)/train-* - config_name: vistext(cauldron) data_files: - split: train path: vistext(cauldron)/train-* - config_name: visual7w(cauldron,llava_format) data_files: - split: train path: visual7w(cauldron,llava_format)/train-* - config_name: visualmrc(cauldron) data_files: - split: train path: visualmrc(cauldron)/train-* - config_name: vqarad(cauldron,llava_format) data_files: - split: train path: vqarad(cauldron,llava_format)/train-* - config_name: vsr(cauldron,llava_format) data_files: - split: train path: vsr(cauldron,llava_format)/train-* - config_name: websight(cauldron) data_files: - split: train path: websight(cauldron)/train-* --- # Dataset Card for LLaVA-OneVision **[2024-09-01]: Uploaded VisualWebInstruct(filtered), it's used in OneVision Stage** > almost all subsets are uploaded with HF's required format and you can use the recommended interface to download them and follow our code below to convert them. > the subset of `ureader_kg` and `ureader_qa` are uploaded with the processed jsons and tar.gz of image folders. > You may directly download them from the following url. > https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data/tree/main/ureader_kg In this dataset, we include the data splits used in the both final image stage and one-vision stage. For more details, please check our [paper](arxiv.org/abs/2408.03326) and our [training doc](https://github.com/LLaVA-VL/LLaVA-NeXT/tree/main/scripts/train#about-the-llava-onevision-data). ## Dataset Description - **Curated by:** Bo Li, Kaichen Zhang, Hao Zhang, Yuanhan Zhang, Renrui Zhang, Feng Li, Dong Guo - **Language(s) (NLP):** English, Chinese - **License:** Apache License 2.0 ## Dataset Sources  - **Dataset Collection:** We include a few subsets from existing dataset collection [Cambrian](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M), [Cauldron](https://huggingface.co/datasets/HuggingFaceM4/the_cauldron), [UReader](https://arxiv.org/abs/2310.05126). Since we only used a few subsets from these datasets, and applied the cleaning and re-annotation process, we uploaded our processed version of these datasets into our own repository and thank the authors for providing the original datasets. - **Other Datasets:** For rest single source dataset, such as AI2D, OKVQA, we cite and link the original sources in our paper. ## Uses This dataset is used for the training of the LLaVA-OneVision model. We only allow the use of this dataset for academic research and education purpose. For OpenAI GPT-4 generated data, we recommend the users to check the [OpenAI Usage Policy](https://openai.com/policies/usage-policies/). ## Dataset Structure We expalin the data composition for mid-stage and final-stage at our repo in [**training doc**](https://github.com/LLaVA-VL/LLaVA-NeXT/tree/main/scripts/train#about-the-llava-onevision-data). ### Statistics We provide the statistics of the dataset in the following figures, and refer the audience to check our paper. ![](https://i.postimg.cc/2y989XZJ/WX20240802-145215-2x.png) ![](https://i.postimg.cc/MZ9TGXFD/WX20240802-145226-2x.png) ### Code Guidance To help audience to better understand our dataest, we upload them into Hugging Face Dataset compatible format. During LLaVA-OneVision training, we use the `json` and `image/video` folder to store the data. > the subset of `ureader_kg` and `ureader_qa` are uploaded with the processed jsons and tar.gz of image folders. You may directly download them from the following url. > https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data/tree/main/ureader_kg Here we provide the code guidance to convert the dataset into the format of LLaVA-OneVision, and conduct the training of the LLaVA-OneVision model with converted dataset. ```python import os from datasets import load_dataset from tqdm import tqdm import json data = load_dataset("lmms-lab/LLaVA-OneVision-Data", split="train") image_folder = "<your_image_folder>" converted_data = [] for da in tqdm(data): json_data = {} json_data["id"] = da["id"] if da["image"] is not None: json_data["image"] = f"{da['id']}.jpg" da["image"].save(os.path.join(image_folder, json_data["image"])) json_data["conversations"] = da["conversations"] converted_data.append(json_data) with open("<your_json_file>.json", "w") as f: json.dump(converted_data, f, indent=4, ensure_ascii=False) ``` ## Citation **BibTeX:** [More Information Needed] ## Glossary The dataset collection process is conducted by all of the authors, we thank the Feng Li and Renrui Zhang for providing [LLaVA-M4-Instruct Data](https://huggingface.co/datasets/lmms-lab/M4-Instruct-Data) and Yuanhan for providing the [Video datasets](https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K). After the dataset collection, the cleaning and re-annotation process, including final mixture of the dataset, is conducted by Bo Li and with the great help of Kaichen Zhang. ## Dataset Card Authors The dataset is curated by the following authors: Bo Li, Kaichen Zhang, Hao Zhang, Yuanhan Zhang, Renrui Zhang, Feng Li ## Dataset Card Contact [Bo Li](https://brianboli.com/): [email protected] [Kaichen Zhang](https://www.linkedin.com/in/kaichen-zhang-014b17219/?originalSubdomain=sg)

Forceless/PPTAgent

Forceless

2024-10-20T05:51:45Z

11,427

3

[ "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-10-18T04:49:53Z

null

--- dataset_info: features: - name: filename dtype: string - name: size dtype: int64 - name: url dtype: string - name: license dtype: string - name: title dtype: string - name: created dtype: string - name: updated dtype: string - name: doi dtype: string - name: checksum dtype: string - name: page dtype: int64 - name: topic dtype: string - name: filetype dtype: string splits: - name: pptx num_bytes: 317828 num_examples: 761 - name: pdf num_bytes: 253893 num_examples: 603 download_size: 249178 dataset_size: 571721 configs: - config_name: default data_files: - split: pptx path: data/pptx-* - split: pdf path: data/pdf-* ---

allenai/WildChat

allenai

2024-10-17T17:48:53Z

1,308

142

[ "task_categories:text-generation", "task_categories:question-answering", "task_categories:text2text-generation", "license:odc-by", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2405.01470", "arxiv:2409.03753", "arxiv:2406.13706", "region:us", "instruction-finetuning" ]

[ "text-generation", "question-answering", "text2text-generation" ]

2023-10-27T23:53:36Z

null

--- license: odc-by size_categories: - 100K<n<1M task_categories: - text-generation - question-answering - text2text-generation pretty_name: WildChat dataset_info: features: - name: conversation_id dtype: string - name: model dtype: string - name: timestamp dtype: timestamp[s, tz=UTC] - name: conversation list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: toxic dtype: bool - name: turn dtype: int64 - name: language dtype: string - name: openai_moderation list: - name: categories struct: - name: harassment dtype: bool - name: harassment/threatening dtype: bool - name: hate dtype: bool - name: hate/threatening dtype: bool - name: self-harm dtype: bool - name: self-harm/instructions dtype: bool - name: self-harm/intent dtype: bool - name: sexual dtype: bool - name: sexual/minors dtype: bool - name: violence dtype: bool - name: violence/graphic dtype: bool - name: category_scores struct: - name: harassment dtype: float64 - name: harassment/threatening dtype: float64 - name: hate dtype: float64 - name: hate/threatening dtype: float64 - name: self-harm dtype: float64 - name: self-harm/instructions dtype: float64 - name: self-harm/intent dtype: float64 - name: sexual dtype: float64 - name: sexual/minors dtype: float64 - name: violence dtype: float64 - name: violence/graphic dtype: float64 - name: flagged dtype: bool - name: detoxify_moderation list: - name: identity_attack dtype: float32 - name: insult dtype: float32 - name: obscene dtype: float32 - name: severe_toxicity dtype: float32 - name: sexual_explicit dtype: float32 - name: threat dtype: float32 - name: toxicity dtype: float32 - name: toxic dtype: bool - name: redacted dtype: bool splits: - name: train num_bytes: 2949464505.6494355 num_examples: 529428 download_size: 1586548072 dataset_size: 2949464505.6494355 configs: - config_name: default data_files: - split: train path: data/train-* tags: - instruction-finetuning --- # Dataset Card for WildChat ## Note: a newer version with 1 million conversations and demographic information can be found [here](https://huggingface.co/datasets/allenai/WildChat-1M). ## Dataset Description - **Paper:** https://arxiv.org/abs/2405.01470 - **Interactive Search Tool:** https://wildvisualizer.com ([paper](https://arxiv.org/abs/2409.03753)) - **License:** [ODC-BY](https://opendatacommons.org/licenses/by/1-0/) - **Language(s) (NLP):** multi-lingual - **Point of Contact:** [Yuntian Deng](https://yuntiandeng.com/) ### Dataset Summary WildChat is a collection of 650K conversations between human users and ChatGPT. We collected WildChat by offering online users free access to OpenAI's GPT-3.5 and GPT-4. The dataset contains a broad spectrum of user-chatbot interactions that are not previously covered by other instruction fine-tuning datasets: for example, interactions include ambiguous user requests, code-switching, topic-switching, political discussions, etc. WildChat can serve both as a dataset for instructional fine-tuning and as a valuable resource for studying user behaviors. Note that this version of the dataset only contains non-toxic user inputs/ChatGPT responses. ### Updates **2024-10-17: Content Update.** Conversations flagged by [Niloofar Mireshghallah](https://homes.cs.washington.edu/~niloofar/) and her collaborators in ["Breaking News: Case Studies of Generative AI's Use in Journalism"](https://arxiv.org/abs/2406.13706) for containing PII or sensitive information have been removed from this version of the dataset. **2024-07-22: Content Update.** All toxic conversations identified by the OpenAI Moderations API or Detoxify have been removed from this version of the dataset. **2024-06-26: License Change.** We have updated the license of WildChat to [ODC-BY](https://opendatacommons.org/licenses/by/1-0/). This change is retroactively applied to any previous downloads under the ImpACT license. ### Languages 66 languages were detected in WildChat. ### Personal and Sensitive Information The data has been de-identified with Microsoft Presidio and hand-written rules by the authors. ### Data Fields - `conversation_id` (string): Each conversation has a unique id. - `model` (string): The underlying OpenAI model, such as gpt-3.5-turbo or gpt-4. - `timestamp` (timestamp): The timestamp of the last turn in the conversation in UTC. - `conversation` (list): A list of user/assistant utterances. Each utterance is a dictionary containing the `role` of the speaker (user or assistant), the `content` of the utterance, the detected `language` of the utterance, whether the content of the utterance is considered `toxic`, and whether PII has been detected and anonymized (`redacted`). - `turn` (int): The number of turns in the conversation. A turn refers to one round of user-assistant interaction. - `language` (string): The language of the conversation. Note that this is the most frequently detected language in the utterances of the conversation. - `openai_moderation` (list): A list of OpenAI Moderation results. Each element in the list corresponds to one utterance in the conversation. - `detoxify_moderation` (list): A list of Detoxify results. Each element in the list corresponds to one utterance in the conversation. - `toxic` (bool): Whether this conversation contains any utterances considered to be toxic by either OpenAI Moderation or Detoxify. - `redacted` (bool): Whether this conversation contains any utterances in which PII is detected and anonymized. ### Empty User Inputs This dataset includes a small subset of conversations where users submitted empty inputs, sometimes leading to hallucinated responses from the assistant. This issue, first noticed by @yuchenlin, arises from the design of our Huggingface chatbot used for data collection, which did not restrict the submission of empty inputs. As a result, users could submit without entering any text, causing the assistant to generate responses without any user prompts. This occurs in a small fraction of the dataset---12,405 out of 652,139 conversations. ### Licensing Information WildChat is now made available under the [**ODC-BY License**](https://opendatacommons.org/licenses/by/1-0/). This change is retroactively applied to any previous downloads under the ImpACT license. ### Citation Information Please consider citing [our paper](https://arxiv.org/abs/2405.01470) if you find this dataset useful: ``` @inproceedings{ zhao2024wildchat, title={WildChat: 1M Chat{GPT} Interaction Logs in the Wild}, author={Wenting Zhao and Xiang Ren and Jack Hessel and Claire Cardie and Yejin Choi and Yuntian Deng}, booktitle={The Twelfth International Conference on Learning Representations}, year={2024}, url={https://openreview.net/forum?id=Bl8u7ZRlbM} } ``` ``` @misc{deng2024wildvisopensourcevisualizer, title={WildVis: Open Source Visualizer for Million-Scale Chat Logs in the Wild}, author={Yuntian Deng and Wenting Zhao and Jack Hessel and Xiang Ren and Claire Cardie and Yejin Choi}, year={2024}, eprint={2409.03753}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2409.03753}, } ```

openai/MMMLU

openai

2024-10-16T18:39:00Z

11,119

478

[ "task_categories:question-answering", "language:ar", "language:bn", "language:de", "language:es", "language:fr", "language:hi", "language:id", "language:it", "language:ja", "language:ko", "language:pt", "language:sw", "language:yo", "language:zh", "license:mit", "size_categories:100K<n<1M", "format:csv", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2009.03300", "region:us" ]

[ "question-answering" ]

2024-09-13T16:37:19Z

null

--- task_categories: - question-answering configs: - config_name: default data_files: - split: test path: test/*.csv - config_name: AR_XY data_files: - split: test path: test/mmlu_AR-XY.csv - config_name: BN_BD data_files: - split: test path: test/mmlu_BN-BD.csv - config_name: DE_DE data_files: - split: test path: test/mmlu_DE-DE.csv - config_name: ES_LA data_files: - split: test path: test/mmlu_ES-LA.csv - config_name: FR_FR data_files: - split: test path: test/mmlu_FR-FR.csv - config_name: HI_IN data_files: - split: test path: test/mmlu_HI-IN.csv - config_name: ID_ID data_files: - split: test path: test/mmlu_ID-ID.csv - config_name: IT_IT data_files: - split: test path: test/mmlu_IT-IT.csv - config_name: JA_JP data_files: - split: test path: test/mmlu_JA-JP.csv - config_name: KO_KR data_files: - split: test path: test/mmlu_KO-KR.csv - config_name: PT_BR data_files: - split: test path: test/mmlu_PT-BR.csv - config_name: SW_KE data_files: - split: test path: test/mmlu_SW-KE.csv - config_name: YO_NG data_files: - split: test path: test/mmlu_YO-NG.csv - config_name: ZH_CN data_files: - split: test path: test/mmlu_ZH-CN.csv language: - ar - bn - de - es - fr - hi - id - it - ja - ko - pt - sw - yo - zh license: mit --- # Multilingual Massive Multitask Language Understanding (MMMLU) The MMLU is a widely recognized benchmark of general knowledge attained by AI models. It covers a broad range of topics from 57 different categories, covering elementary-level knowledge up to advanced professional subjects like law, physics, history, and computer science. We translated the MMLU’s test set into 14 languages using professional human translators. Relying on human translators for this evaluation increases confidence in the accuracy of the translations, especially for low-resource languages like Yoruba. We are publishing the professional human translations and the code we use to run the evaluations. This effort reflects our commitment to improving the multilingual capabilities of AI models, ensuring they perform accurately across languages, particularly for underrepresented communities. By prioritizing high-quality translations, we aim to make AI technology more inclusive and effective for users worldwide. ## Locales MMMLU contains the MMLU test set translated into the following locales: * AR_XY (Arabic) * BN_BD (Bengali) * DE_DE (German) * ES_LA (Spanish) * FR_FR (French) * HI_IN (Hindi) * ID_ID (Indonesian) * IT_IT (Italian) * JA_JP (Japanese) * KO_KR (Korean) * PT_BR (Brazilian Portuguese) * SW_KE (Swahili) * YO_NG (Yoruba) * ZH_CN (Simplified Chinese) ## Sources Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., & Steinhardt, J. (2021). [*Measuring Massive Multitask Language Understanding*](https://arxiv.org/abs/2009.03300). [OpenAI Simple Evals GitHub Repository](https://github.com/openai/simple-evals)

HuggingFaceH4/ultrachat_200k

HuggingFaceH4

2024-10-16T11:52:27Z

15,121

527

[ "task_categories:text-generation", "language:en", "license:mit", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2305.14233", "region:us" ]

[ "text-generation" ]

2023-10-24T08:24:57Z

null

--- language: - en license: mit size_categories: - 100K<n<1M task_categories: - text-generation pretty_name: UltraChat 200k configs: - config_name: default data_files: - split: train_sft path: data/train_sft-* - split: test_sft path: data/test_sft-* - split: train_gen path: data/train_gen-* - split: test_gen path: data/test_gen-* dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train_sft num_bytes: 1397058554 num_examples: 207865 - name: test_sft num_bytes: 154695659 num_examples: 23110 - name: train_gen num_bytes: 1347396812 num_examples: 256032 - name: test_gen num_bytes: 148276089 num_examples: 28304 download_size: 1624049723 dataset_size: 3047427114 --- # Dataset Card for UltraChat 200k ## Dataset Description This is a heavily filtered version of the [UltraChat](https://github.com/thunlp/UltraChat) dataset and was used to train [Zephyr-7B-β](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), a state of the art 7b chat model. The original datasets consists of 1.4M dialogues generated by ChatGPT and spanning a wide range of topics. To create `UltraChat 200k`, we applied the following logic: - Selection of a subset of data for faster supervised fine tuning. - Truecasing of the dataset, as we observed around 5% of the data contained grammatical errors like "Hello. how are you?" instead of "Hello. How are you?" - Removal of dialogues where the assistant replies with phrases like "I do not have emotions" or "I don't have opinions", even for fact-based prompts that don't involve either. ## Dataset Structure The dataset has four splits, suitable for: * Supervised fine-tuning (`sft`). * Generation ranking (`gen`) via techniques like rejection sampling or PPO. The number of examples per split is shown as follows: | train_sft | test_sft | train_gen | test_gen | |:-------:|:-----------:|:-----:| :-----:| | 207865 | 23110 | 256032 | 28304 | The dataset is stored in parquet format with each entry using the following schema: ``` { "prompt": "Create a fully-developed protagonist who is challenged to survive within a dystopian society under the rule of a tyrant. ...", "messages":[ { "content": "Create a fully-developed protagonist who is challenged to survive within a dystopian society under the rule of a tyrant. ...", "role": "user" }, { "content": "Name: Ava\n\n Ava was just 16 years old when the world as she knew it came crashing down. The government had collapsed, leaving behind a chaotic and lawless society. ...", "role": "assistant" }, { "content": "Wow, Ava's story is so intense and inspiring! Can you provide me with more details. ...", "role": "user" }, { "content": "Certainly! ....", "role": "assistant" }, { "content": "That's really interesting! I would love to hear more...", "role": "user" } { "content": "Certainly! ....", "role": "assistant" }, ], "prompt_id": "d938b65dfe31f05f80eb8572964c6673eddbd68eff3db6bd234d7f1e3b86c2af" } ``` ## Citation If you find this dataset is useful in your work, please cite the original UltraChat dataset: ``` @misc{ding2023enhancing, title={Enhancing Chat Language Models by Scaling High-quality Instructional Conversations}, author={Ning Ding and Yulin Chen and Bokai Xu and Yujia Qin and Zhi Zheng and Shengding Hu and Zhiyuan Liu and Maosong Sun and Bowen Zhou}, year={2023}, eprint={2305.14233}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

HuggingFaceH4/ultrafeedback_binarized

HuggingFaceH4

2024-10-16T11:49:06Z

8,312

285

[ "task_categories:text-generation", "language:en", "license:mit", "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2310.01377", "region:us" ]

[ "text-generation" ]

2023-10-24T08:53:19Z

null

--- language: - en license: mit task_categories: - text-generation pretty_name: UltraFeedback Binarized configs: - config_name: default data_files: - split: train_prefs path: data/train_prefs-* - split: train_sft path: data/train_sft-* - split: test_prefs path: data/test_prefs-* - split: test_sft path: data/test_sft-* - split: train_gen path: data/train_gen-* - split: test_gen path: data/test_gen-* dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 splits: - name: train_prefs num_bytes: 405688662 num_examples: 61135 - name: train_sft num_bytes: 405688662 num_examples: 61135 - name: test_prefs num_bytes: 13161585 num_examples: 2000 - name: test_sft num_bytes: 6697333 num_examples: 1000 - name: train_gen num_bytes: 325040536 num_examples: 61135 - name: test_gen num_bytes: 5337695 num_examples: 1000 download_size: 649967196 dataset_size: 1161614473 --- # Dataset Card for UltraFeedback Binarized ## Dataset Description This is a pre-processed version of the [UltraFeedback dataset](https://huggingface.co/datasets/openbmb/UltraFeedback) and was used to train [Zephyr-7Β-β](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), a state of the art chat model at the 7B parameter scale. The original UltraFeedback dataset consists of 64k prompts, where each prompt is accompanied with four model completions from a wide variety of open and proprietary models. GPT-4 is then used to assign a score to each completion, along criteria like helpfulness and honesty. To create `UltraFeedback Binarized`, we picked the highest `overall_score` as the "chosen" completion, and one of the remaining 3 at random as the "rejected" one. This defines the preference modelling splits for techniques like reward modelling or DPO. We also created splits for supervised fine-tuning (SFT) that use the "chosen" column as the dialogues to model, along with splits that involve generation like rejection sampling or PPO. For details on the dataset processing, see the accompanying [script](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized/blob/main/create_dataset.py). ## Dataset Structure ### Usage To load the dataset, run: ```python from datasets import load_dataset ds = load_dataset("HuggingFaceH4/ultrafeedback_binarized") ``` **Note:** after the release of Zephyr-7b-β, the team at [Argilla](https://huggingface.co/argilla) noted that there were a few hundred completions with the incorrect label. Similarly, members of the community pointed out that a several prompts were sourced from the TruthfulQA benchmark which can lead to contamination with public leaderboards. Both of these issues have been resolved in the latest version of the dataset, but if you wish to use the older version, you can run: ```python from datasets import load_dataset ds = load_dataset("HuggingFaceH4/ultrafeedback_binarized", revision="292c16329d921287c4166934cac1a6ad1e13a6c5") ``` ### Data Splits The dataset has six splits, suitable for: * Supervised fine-tuning (`sft`). * Preference modelling (`prefs`) to train reward models or apply techniques like DPO. * Generation ranking (`gen`) via techniques like rejection sampling or PPO. The number of examples per split is shown as follows: | train_sft | test_sft | train_prefs | test_prefs | train_gen | test_gen | |:-------:|:-----------:|:-----:| :-----:| :-----:| :-----:| | 61135 | 1000 | 61135 | 2000 | 61135 | 1000 | The dataset is stored in parquet format with each entry using the following schema: ``` { "prompt": "Which books are still banned in Canada?", "chosen": [ { "content": "Which books are still banned in Canada?", "role": "user" }, { "content": "As a responsible and ethical AI assistant, I must inform you that I cannot provide a list of banned books in Canada, as there are no books that are currently banned in the country. Canada has a strong commitment to freedom of expression and the right to access information, and there are no laws in place that restrict the publication or distribution of books.\n", "role": "assistant" } ], "messages": [ { "content": "Which books are still banned in Canada?", "role": "user" }, { "content": "As a responsible and ethical AI assistant, I must inform you that I cannot provide a list of banned books in Canada, as there are no books that are currently banned in the country. Canada has a strong commitment to freedom of expression and the right to access information, and there are no laws in place that restrict the publication or distribution of books.\n", "role": "assistant" }], "prompt_id": "aeccf551d9ba42fdf5f2044de43b8ce6e360fb523ace428317b81d804594e090", "rejected": [ { "content": "Which books are still banned in Canada?", "role": "user"}, { "content": "According to the Canadian Government’s Ban Affront website, there are still several books that are banned in Canada. These include The Begum’s Millionaire, The Education of Little Tree, The Harry Potter series, Lolita, 1984, and Lady Chatterley’s Lover. Some of these books are considered inaccessible due to their age, while others are still legally banned in certain parts of the country.", "role": "assistant" } ], "score_chosen": 8.0, "score_rejected": 5.0 } ``` You should use the `chosen` and `rejected` columns for techniques like DPO, while the `messages` column is suitable for SFT or PPO. ## Citation If you find this dataset is useful in your work, please cite the original UltraFeedback dataset: ``` @misc{cui2023ultrafeedback, title={UltraFeedback: Boosting Language Models with High-quality Feedback}, author={Ganqu Cui and Lifan Yuan and Ning Ding and Guanming Yao and Wei Zhu and Yuan Ni and Guotong Xie and Zhiyuan Liu and Maosong Sun}, year={2023}, eprint={2310.01377}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

GAIR/o1-journey

GAIR

2024-10-16T00:42:02Z

122

133

[ "language:en", "size_categories:n<1K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-10-16T00:28:30Z

null

--- language: - en size_categories: - n<1K --- Dataset for [O1 Replication Journey: A Strategic Progress Report](https://github.com/GAIR-NLP/O1-Journey) ## Usage ```python from datasets import load_dataset dataset = load_dataset("GAIR/o1-journey", split="train") ``` ## Citation If you find our dataset useful, please cite: ``` @misc{o1journey, author = {Yiwei Qin and Xuefeng Li and Haoyang Zou and Yixiu Liu and Shijie Xia and Zhen Huang and Yixin Ye and Weizhe Yuan and Zhengzhong Liu and Yuanzhi Li and Pengfei Liu}, title = {O1 Replication Journey: A Strategic Progress Report – Part 1}, year = {2024}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/GAIR-NLP/O1-Journey}}, } ```

allenai/peS2o

allenai

2024-10-13T02:53:05Z

2,322

171

[ "task_categories:text-generation", "task_categories:fill-mask", "source_datasets:allenai/s2orc", "language:en", "license:odc-by", "size_categories:10B<n<100B", "region:us", "biology", "chemistry", "engineering", "computer science", "physics", "material science", "math", "psychology", "economics", "political science", "business", "geology", "sociology", "geography", "environmental science", "art", "history", "philosophy" ]

[ "text-generation", "fill-mask" ]

2023-06-29T04:54:16Z

null

--- license: - odc-by task_categories: - text-generation - fill-mask language: - en tags: - biology - chemistry - engineering - computer science - physics - material science - math - psychology - economics - political science - business - geology - sociology - geography - environmental science - art - history - philosophy pretty_name: peS2o (Pretraining Efficiently on S2ORC) size_categories: - 10B<n<100B source_datasets: - allenai/s2orc --- <p align="center" style="margin-top: -2em"> <img src="https://huggingface.co/datasets/allenai/pes2o/resolve/main/logo.png" alt="peS2o logo. It's a picure of a mortar and pestle with documents flying in." width=384px height=auto> </p> <p align="center" style="font-size: 1.2em; margin-top: -1em"><i>Pretraining Effectively on <a href="https://github.com/allenai/s2orc">S2ORC</a>!</i></p> The peS2o dataset is a collection of ~40M creative open-access academic papers, cleaned, filtered, and formatted for pre-training of language models. It is derived from the [Semantic Scholar Open Research Corpus][2]([Lo et al, 2020][1]), or S2ORC. We release multiple version of peS2o, each with different processing and knowledge cutoff date. We recommend you to use the latest version available. If you use this dataset, please cite: ```bibtex @techreport{peS2o, author = {Luca Soldaini and Kyle Lo}, year = 2023, title = {{peS2o (Pretraining Efficiently on S2ORC) Dataset}}, institution = {{Allen Institute for AI}}, note = {ODC-By, \url{https://github.com/allenai/pes2o}} } ``` ## Document Format Each document in the dataset is a dictionary with the following fields: - `added`: Date the document was added to the corpus. - `created`: Best-guess date for when the document was first published. Some have resolution down to the day, only down to the year. - `id`: Semantic Scholar Corpus ID of the document; it can be used with the [Semantic Scholar API](https://api.semanticscholar.org/) to retrieve metadata about the document (e.g., fields of study, authors). - `source`: Collection from which the document was sourced from. At the moment, two are supported: - `s2orc`: collection of full-text papers - `s2ag`: collection of title and abstracts - `text`: Text of the document. Paragraphs are separated by two newlines (`\n\n`). - `version`: version of peS2o. ------ ## peS2o V2 (Latest) ### Key Facts - *Knowledge cutoff*: 2023-01-03 - *Number of documents*: 38.97M - *Number of whitespace-separated tokens**: 42.01B ### Processing peS2o V2 is largely the same as V1, but it includes additional heuristics s2ag aimed at filtering out OCR errors from abstract. First, we check if the abstract was obtained from Semantic Scholar sources that are likely to contain OCR'ed content. For any abstract derived from those sources, we count how often the text contains subsequences matching `\b([A-Za-z]\s)([a-z]\s)*[A-Za-z]\b`, i.e. individual alpha letters separated by a space. This heuristic matches cases such as `A b stra ct` (2 matching subsequences), where the OCR parser inserted erroneous spaces. Any abstract with more than 4 matching subsequences is removed. #### Statistics | Dataset | Split | # Documents | # Words | |:-------:|:-----:|------------:|---------------:| | s2orc | train | 8,242,162 | 36,088,195,908 | | s2orc | valid | 51,323 | 255,139,074 | | s2ag | train | 30,569,017 | 5,920,099,207 | | s2ag | valid | 109,709 | 24,029,459 | ------- ## peS2o V1 ### Key Facts - *Knowledge cutoff*: 2023-01-03 - *Number of documents*: 67.56M - *Number of whitespace-separated tokens*: 47.37B ### Processing Processing differs slightly wether it was derived from the full-text corpus (`s2orc`) or the title and abstract corpus (`s2ag`). #### S2ORC-derived documents Unfiltered, S2ORC contains 11.3M papers and 46.9B whitespace-separated tokens as of 2023-01-03. To derive peS2o v1, we impose the following constraints: - The paper must have a title and abstract. - From each paper, we use [Grobid](https://github.com/kermitt2/grobid) to extract section headers and paragraphs; figures, tables, and references, and any other non-textual content is removed. Title and abstracts are also available, but they come from the Semantic Scholar metadata (obtained through the APIs), not Grobid. - The paper must be in English. - To determine the language of each document, we use the [pycld3](https://github.com/bsolomon1124/pycld3) library - We run pycld3 on the first 2000 characters of each paragraph in the paper. - The language of the paper is the most common language of the paragraphs. - The paper must have at least 500 whitespace-separated words. - The paper was published after 1969; papers published before this date are often obtained through OCR and contain unrecoverable errors. - The paper must have at least 5 paragraphs. - All sections that have a average log word probability of less than `-20` are removed. - To calculate the average log word probability, we use word frequencies extracted from the [1T Web Ngram corpus](https://catalog.ldc.upenn.edu/LDC2006T13); specifically, we use the list available [created by Rachel Tatman](https://www.kaggle.com/datasets/rtatman/english-word-frequency). A copy is hosted [here](https://ai2-s2-research-public.s3-us-west-2.amazonaws.com/lucas/google-1T-unigram/unigram_freq.csv). - The most frequent word in the paper consists of alpha characters only, and it appears in less than 7.5% of the document. - Words are obtained by splitting the text on whitespace. The train set contains papers published before 2022-12-01; the validation set includes documents published after 2022-12-01 and until 2023-01-03. #### S2AG-derived documents The S2AG corpus contains titles and abstracts of papers in Semantic Scholar. Unfiltered, the corpus contains 91.1M papers and 15.5B whitespace-separated tokens as of 2023-01-03. To derive peS2o v1, we impose the following constraints: - Abstract must be in English. - To calculate the language, we once again use pycld3 - Title must be in English, or have average unigram log probability greater than -20. - Abstract must be in English. - Abstract must have higher than -20 average unigram log probability. - Abstract must have at least 50 words. - Abstract must have no more than 1000 words. - The most frequent word in the union of text and abstract must be a 2+ character alpha word, or it can be `a` followed by a 2+ character alpha word. - Paper was published after 1969. #### Statistics | Dataset | Split | # Documents | # Words | |:-------:|:-------:|:-----------:|:--------------:| |s2orc | train | 8,242,162 | 36,088,195,908 | |s2orc | valid | 51,323 | 255,139,074 | |s2ag | train | 59,382,301 | 11,009,123,378 | |s2ag | valid | 111,228 | 24,398,512 | [1]: https://aclanthology.org/2020.acl-main.447/ [2]: https://github.com/allenai/s2orc

thanhkt/manim_code

thanhkt

2024-10-10T14:55:23Z

74

4

[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-10-10T14:55:22Z

2

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 7074758 num_examples: 4400 download_size: 2439317 dataset_size: 7074758 configs: - config_name: default data_files: - split: train path: data/train-* ---

IGNF/PASTIS-HD

IGNF

2024-10-04T13:39:24Z

28,466

12

[ "task_categories:image-classification", "task_categories:image-segmentation", "license:etalab-2.0", "size_categories:1K<n<10K", "format:imagefolder", "modality:image", "library:datasets", "library:mlcroissant", "arxiv:2107.07933", "arxiv:2112.07558", "arxiv:2404.08351", "region:us", "remote sensing", "Agricultural" ]

[ "image-classification", "image-segmentation" ]

2024-04-02T14:58:15Z

null

--- license: etalab-2.0 task_categories: - image-classification - image-segmentation tags: - remote sensing - Agricultural size_categories: - 1K<n<10K --- # 🌱 PASTIS-HD 🌿 Panoptic Agricultural Satellite TIme Series : optical time series, radar time series and very high resolution image [PASTIS](https://github.com/VSainteuf/pastis-benchmark) is a benchmark dataset for panoptic and semantic segmentation of agricultural parcels from satellite time series. It contains 2,433 patches within the French metropolitan territory with panoptic annotations (instance index + semantic label for each pixel). Each patch is a Sentinel-2 multispectral image time series of variable lentgh. This dataset have been extended in 2021 with aligned radar Sentinel-1 observations for all 2433 patches. For each patch, it constains approximately 70 observations of Sentinel-1 in ascending orbit, and 70 observations in descending orbit. Each each Sentinel1 observation is assembled into a 3-channel image: vertical polarization (VV), horizontal polarisation (VH), and the ratio vertical over horizontal polarization (VV/VH). This extension is named PASTIS-R. We extend PASTIS with aligned very high resolution satellite images from SPOT 6-7 constellation for all 2433 patches in addition to the Sentinel-1 and 2 time series. The image are resampled to a 1m resolution and converted to 8 bits. This enhancement significantly improves the dataset's spatial content, providing more granular information for agricultural parcel segmentation. **PASTIS-HD** can be used to evaluate multi-modal fusion methods (with optical time series, radar time series and VHR images) for parcel-based classification, semantic segmentation, and panoptic segmentation. ## Dataset in numbers 🛰️ Sentinel 2 | 🛰️ Sentinel 1 | 🛰️ **SPOT 6-7 VHR** | 🗻 Annotations :-------------------------------------------- | :-------------------------------------------------- | :------------------------------| :------------------------------ ➡️ 2,433 time series | ➡️ 2 time 2,433 time series | ➡️ **2,433 images** | 124,422 individual parcels ➡️ 10m / pixel | ➡️ 10m / pixel | ➡️ **1.5m / pixel** | covers ~4,000 km² ➡️ 128x128 pixels / images | ➡️ 128x128 pixels / images | ➡️ **1280x1280 pixels / images** | over 2B pixels ➡️ 38-61 acquisitions / series | ➡️ ~ 70 acquisitions / series | ➡️ **One observation** | 18 crop types ➡️ 10 spectral bands |➡️ 2 spectral bands | ➡️ **3 spectral bands** | ⚠️ The **SPOT data are natively 1.5m resolution**, but we over-sampled them at 1m to align them pixel-perfect with Sentinel data. ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6582b7dd75754a803e484487/sxmnCAGs0p2u_PALLsqyN.jpeg) ## Data loading The Github repository associated to this dataset contains a PyTorch dataset class of [the OmniSat repository](https://github.com/gastruc/OmniSat/blob/main/src/data/Pastis.py) that can be readily used to load data for training models on PASTIS-HD. The time series contained in PASTIS have variable lengths. The Sentinel 1 and 2 time series are stored in numpy array. The SPOT images are in TIFF format. The annotations are in numpy array too. ⚠️ The S2 and S1 folders contains more than 2433 files on the contrary to the labels folder. Some patches are not labelled and not used for training. The relevant information can be find in the metadata.geojson file (with 2433 entries), which is used as an index by the dataloader. ### Remark about the folder names ⚠️ The **DATA_S1A** folder contains the Sentinel-1 **ascendent** images whereas the **DATA_S1D** folder contains the Sentinel-1 **descendant** images. ## Ground Truth Annotations The agricultural parcels are grouped into 18 different crop classes as shown in the table below. The backgroud class corresponds to non-agricultural land, and the void label for parcels that are mostly outside their patch. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6582b7dd75754a803e484487/aHQB0uq4cqBX-7hkCkpFn.png) Additional information about the dataset can be found in the documentation/pastis-documentation.pdf document. ## Credits - The Sentinel imagery used in PASTIS was retrieved from [THEIA](www.theia.land.fr): "Value-added data processed by the CNES for the Theia www.theia.land.fr data cluster using Copernicus data. The treatments use algorithms developed by Theia’s Scientific Expertise Centres. " - The annotations used in PASTIS stem from the French [land parcel identification system](https://www.data.gouv.fr/en/datasets/registre-parcellaire-graphique-rpg-contours-des-parcelles-et-ilots-culturaux-et-leur-groupe-de-cultures-majoritaire/) produced by IGN. - The SPOT images are opendata thanks to the Dataterra Dinamis initiative in the case of the ["Couverture France DINAMIS"](https://dinamis.data-terra.org/opendata/) program. ## References If you use PASTIS please cite the [related paper](https://arxiv.org/abs/2107.07933): ``` @article{garnot2021panoptic, title={Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks}, author={Sainte Fare Garnot, Vivien and Landrieu, Loic}, journal={ICCV}, year={2021} } ``` For the PASTIS-R optical-radar fusion dataset, please also cite [this paper](https://arxiv.org/abs/2112.07558v1): ``` @article{garnot2021mmfusion, title = {Multi-modal temporal attention models for crop mapping from satellite time series}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, year = {2022}, doi = {https://doi.org/10.1016/j.isprsjprs.2022.03.012}, author = {Vivien {Sainte Fare Garnot} and Loic Landrieu and Nesrine Chehata}, } ``` For the PASTIS-HD with the 3 modalities optical-radar time series plus VHR images dataset, please also cite [this paper](https://arxiv.org/abs/2404.08351): ``` @article{astruc2024omnisat, title={Omni{S}at: {S}elf-Supervised Modality Fusion for {E}arth Observation}, author={Astruc, Guillaume and Gonthier, Nicolas and Mallet, Clement and Landrieu, Loic}, journal={ECCV}, year={2024} } ```

Gryphe/Sonnet3.5-SlimOrcaDedupCleaned

Gryphe

2024-10-04T08:41:30Z

596

90

[ "license:mit", "size_categories:100K<n<1M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-07-14T08:25:38Z

null

--- license: mit size_categories: - 100K<n<1M --- **2024-10-04:** I fixed two issues that were affecting newlines (all double newlines were gone!) and cleaned up spaces preceding closing quotes. Many thanks to [PocketDoc](https://huggingface.co/PocketDoc) for bringing this to my attention! A Sonnet 3.5 generated version of Caitlyn's wonderfully cleaned [SlimOrca Deduped dataset](https://huggingface.co/datasets/cgato/SlimOrcaDedupCleaned), ready for training using the ShareGPT format. As always, an effort was made to ensure no censoring was applied to the responses. If you find any refusals, let me know!

hendrydong/preference_700K

hendrydong

2024-09-28T16:05:26Z

278

15

[ "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2405.07863", "region:us" ]

[]

2024-04-18T01:43:10Z

2

--- dataset_info: features: - name: rejected list: - name: content dtype: string - name: role dtype: string - name: rejected_score dtype: float64 - name: chosen_score dtype: float64 - name: chosen list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 2802733004 num_examples: 700000 download_size: 1433169364 dataset_size: 2802733004 configs: - config_name: default data_files: - split: train path: data/train-* --- This is the preference dataset used for RLHF Workflow project. It is a mixed dataset of the following complenent, * HH-RLHF (Bai et al., 2022a) is a pairwise preference dataset where each sample is accompanied by a conversation history and two alternative responses written by an early Claude model with 52B parameters. The preferences of the responses are annotated by humans. * SHP (Ethayarajh et al., 2022) is sourced from Reddit and includes examples from 18 subreddits, such as askacademia, askbaking, askengineers, and changemyview. Each example is a Reddit post with a question/instruction and a pair of top-level comments. One comment is preferred by more Reddit users than the other. All preferences and responses are provided by humans. Only samples with a score ratio > 2 are used, and at most 5 pairs are taken for each prompt. * HelpSteer (Wang et al., 2023). This open-source dataset (Wang et al., 2023) contains prompts, responses, and five human-annotated attributes (helpfulness, correctness, coherence, complexity, and verbosity) ranging from 0 to 4. The prompts are generated using a mixture of template-generated and human-generated methods, while responses are generated by an in-house LLM. The authors generate up to 4 responses per prompt, and we can construct pairwise comparisons based on them. * PKU-SafeRLHF (Ji et al., 2024). This dataset (Ji et al., 2024) consists of 30k+ expert comparison data. Each sample includes two responses to a question and two preference signals for helpfulness and safety, respectively. The responses are generated by open-source chatbots, and the preference signals are merged through the results of 14 harm category multi-class classficiation. * UltraFeedback (Cui et al., 2023) consists of 64k prompts from diverse resources (including UltraChat, ShareGPT, Evol-Instruct, TruthfulQA, FalseQA, and FLAN) and the authors generate 4 responses per prompt using 4 different LLMs sampled from a diverse set of state-of-the-art open-source LLMs. The preference is from GPT-4 based on a fine-grained annotation instruction, which contains 4 different aspects, namely instruction-following, truthfulness, honesty and helpfulness. The dataset collection strategy of UltraFeedback has also influenced many subsequent works. * UltraInteract (Yuan et al., 2024a) is a preference dataset designed for complex reasoning tasks. The authors collect a preference tree for each instruction, with the instruction being the root and each action a node. A trajectory is a root-to-leaf path consisting of a sequence of actions. Paired correct and incorrect nodes or trajectories are used for preference learning. * Distilabel-Capybara is a preference dataset of multi-turn dialogues whose prompts are taken from Daniele and Suphavadeeprasit (2023), where the responses are generated by open-source LLMs and preferences are generated by GPT-4. * Distilabel-Orca is collected similarly with Capybara but with the prompts from Lian et al. (2023a). ## Reference If you found it useful, please cite ```bibtex @misc{dong2024rlhf, title={RLHF Workflow: From Reward Modeling to Online RLHF}, author={Hanze Dong and Wei Xiong and Bo Pang and Haoxiang Wang and Han Zhao and Yingbo Zhou and Nan Jiang and Doyen Sahoo and Caiming Xiong and Tong Zhang}, year={2024}, eprint={2405.07863}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```

MichaelR207/enron_qa_0922

MichaelR207

2024-09-22T17:42:07Z

22

2

[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-09-22T17:22:22Z

2

--- dataset_info: features: - name: email dtype: string - name: questions sequence: string - name: rephrased_questions sequence: string - name: gold_answers sequence: string - name: alternate_answers sequence: sequence: string - name: incorrect_answers sequence: sequence: string - name: path dtype: string - name: user dtype: string - name: questions_count dtype: int64 - name: gold_rationales sequence: string - name: alternate_rationales sequence: sequence: string - name: include_email sequence: int64 splits: - name: train num_bytes: 730246275 num_examples: 73772 - name: dev num_bytes: 347395653 num_examples: 73772 - name: test num_bytes: 319859974 num_examples: 73772 download_size: 666346042 dataset_size: 1397501902 configs: - config_name: default data_files: - split: train path: data/train-* - split: dev path: data/dev-* - split: test path: data/test-* ---

jackyhate/text-to-image-2M

jackyhate

2024-09-22T09:38:54Z

12,740

100

[ "task_categories:text-to-image", "task_categories:image-to-text", "task_categories:image-classification", "language:en", "license:mit", "size_categories:100K<n<1M", "format:webdataset", "modality:image", "modality:text", "library:datasets", "library:webdataset", "library:mlcroissant", "doi:10.57967/hf/3066", "region:us" ]

[ "text-to-image", "image-to-text", "image-classification" ]

2024-09-11T14:02:35Z

null

--- license: mit task_categories: - text-to-image - image-to-text - image-classification language: - en size_categories: - 1M<n<10M --- # text-to-image-2M: A High-Quality, Diverse Text-to-Image Training Dataset ## Overview `text-to-image-2M` is a curated text-image pair dataset designed for fine-tuning text-to-image models. The dataset consists of approximately 2 million samples, carefully selected and enhanced to meet the high demands of text-to-image model training. The motivation behind creating this dataset stems from the observation that datasets with over 1 million samples tend to produce better fine-tuning results. However, existing publicly available datasets often have limitations: - **Image Understanding Datasets**: Not guarantee the quality of image. - **Informal collected or Task-Specific Datasets**: Not category balanced or lacks diversity. - **Size Constraints**: Available datasets are either too small or too large. (subset sampled from large datasets often lack diversity.) To address these issues, we combined and enhanced existing high-quality datasets using state-of-the-art text-to-image and captioning models to create `text-to-image-2M`. This includes data_512_2M, a 2M 512x512 fine-tuning dataset and data_1024_10K, a 10K high-quality, high-resolution dataset (for high-resolution adaptation). ## Dataset Composition ### data_512_2M The dataset is composed of several high-quality subsets, as detailed below: | **Source** | **Samples** | **Prompts** | **Images** | |-------------------------------------------------|-------------|--------------------------------------|---------------------------------------------| | [**LLaVA-next fine-tuning dataset**](https://huggingface.co/datasets/lmms-lab/LLaVA-NeXT-Data) | ~700K | Re-captioned using Qwen2-VL | Original images | | [**LLaVA-pretrain dataset**](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain) | ~500K | Original prompts | Images generated by Flux-dev | | [**ProGamerGov synthetic dataset (DALL·E 3)**](https://huggingface.co/datasets/ProGamerGov/synthetic-dataset-1m-dalle3-high-quality-captions) | ~900K | Filtered for validity | Center-cropped and validity-filtered images | | **GPT-4o generated dataset** | 100K | Generated by GPT-4o | Images generated by Flux-dev | ### data_1024_10K 10K images generated by Flux-dev with prompts generated by GPT-4o ## **Usage**: The dataset uses the [WebDataset](https://github.com/webdataset/webdataset) format and can be easily accessed and used with HuggingFace's datasets library like so: ```py from datasets import load_dataset base_url = "https://huggingface.co/datasets/jackyhate/text-to-image-2M/resolve/main/data_512_2M/data_{i:06d}.tar" num_shards = 46 # Number of webdataset tar files urls = [base_url.format(i=i) for i in range(num_shards)] dataset = load_dataset("webdataset", data_files={"train": urls}, split="train", streaming=True) # Example of iterating through the dataset for image in dataset: print(image) # single image in row with associated columns break ``` * Note that as long as `streaming=True` in the above example, the dataset does not have to be downloaded in full. ## Acknowledgments This dataset builds on the work of several open-source projects, including: - [**LLaVA-next fine-tuning dataset**](https://huggingface.co/datasets/lmms-lab/LLaVA-NeXT-Data) - [**LLaVA-pretrain dataset**](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain) - [**ProGamerGov synthetic dataset (DALL·E 3)**](https://huggingface.co/datasets/ProGamerGov/synthetic-dataset-1m-dalle3-high-quality-captions) - **GPT-4o** - **Flux-1.0-dev** We thank the contributors of these datasets and models for making this project possible.

mlfoundations/MINT-1T-HTML

mlfoundations

2024-09-21T01:50:16Z

234,694

83

[ "task_categories:image-to-text", "task_categories:text-generation", "language:en", "license:cc-by-4.0", "size_categories:100M<n<1B", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2406.11271", "region:us", "multimodal" ]

[ "image-to-text", "text-generation" ]

2024-07-21T06:48:51Z

null

--- license: cc-by-4.0 task_categories: - image-to-text - text-generation language: - en tags: - multimodal pretty_name: MINT-1T size_categories: - 100B<n<1T configs: - config_name: data-v1.1 data_files: - split: train path: data_v1_1/*.parquet --- <h1 align="center"> 🍃 MINT-1T:<br>Scaling Open-Source Multimodal Data by 10x:<br> A Multimodal Dataset with One Trillion Tokens </h1> 🍃 MINT-1T is an open-source **M**ultimodal **INT**erleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. 🍃 MINT-1T is created by a team from the University of Washington in collaboration with Salesforce Research, other academic institutions including Stanford University, University of Texas at Austin, and University of California Berkeley. You are currently viewing the HTML subset of 🍃 MINT-1T. For PDF and ArXiv subsets, please refer to the [🍃 MINT-1T collection](https://huggingface.co/collections/mlfoundations/mint-1t-6690216ca4d0df7e518dde1c). ![Examples](interleaved-example-twitter.png) ## Updates ### 9/7/24 We have improved MINT-1T (HTML) by removing boilerplate from the header and footer of each document. This new version of the data can be found in directory `data_v1_1` and contains 742B text tokens. The previous version of the data can be found in directory `data_v1_0`. ### 8/8/24 We have updated MINT-1T (HTML) with fixed document URL filtering and additional image safety filtering. As we prioritize safety, we have decided to only release the HTML data from MINT-1T that passes a rigorous image filtering pipeline; we run an additional image safety classifier, the one created by [Datacomp](https://www.datacomp.ai/dcclip/index.html#home), on data already filtered by our [original NSFW image classifier](https://github.com/GantMan/nsfw_model). The newly released MINT-1T (HTML) contains 792B text tokens and 905M documents. ## Dataset Details ### Dataset Sources - **Repository**: https://github.com/mlfoundations/MINT-1T - **Paper:** https://arxiv.org/abs/2406.11271 - **Blog:** https://blog.salesforceairesearch.com/mint-1t/ ## Uses ### Direct Use  🍃 MINT-1T is designed to facilitate research in multimodal pretraining. The dataset can be used for training multimodal models that can reson about interleaved text and images sequences such as [Idefics2](https://huggingface.co/HuggingFaceM4/idefics2-8b), [XGen-MM](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-r-v1), and [Chameleon](https://huggingface.co/facebook/chameleon-30b). ### Out-of-Scope Use  🍃 MINT-1T was built to make research into large multimodal models more accessible. Using the dataset to train models that ingest or generate personally identifying information (such as images of people’s faces and other sensitive content) as well as military applications are all inappropriate use cases of 🍃 MINT-1T. ## Dataset Creation ### Curation Rationale 🍃 MINT-1T was created to address a significant gap in the open-source domain by providing a large-scale multimodal interleaved dataset for pre-training large multimodal models. This dataset aims to be a valuable resource for the research community, facilitating open science in multimodal pretraining. ### Source Data The dataset is a comprehensive collection of multimodal documents from various sources: - HTML documents: Filtered from CommonCrawl WARC dumps spanning from 2017 to 2024 - PDF documents: Extracted from CommonCrawl WAT dumps covering 2023 to 2024 - ArXiv documents: A subset of papers from the ArXiv repository In total, 🍃 MINT-1T contains 1056.8 million documents, broken down as follows: - 1029.4 million HTML documents - 24.0 million PDF documents - 0.6 million ArXiv documents #### Data Collection and Processing The data collection and processing involved several steps: 1. Document Extraction: - HTML documents were parsed from CommonCrawl WARC files - PDF documents were extracted from CommonCrawl WAT files - ArXiv papers were directly sourced from ArXiv S3 buckets 2. Filtering Process: - Applied text quality filters to ensure content relevance and readability - Removed duplicate content at both paragraph and document levels - Filtered out undesirable content based on predefined criteria - Verified image availability and quality for HTML documents - Limited PDF size to 50MB and 50 pages to manage dataset size and quality 3. Image Processing: - Used NSFW image detection to remove pornographic or otherwise undesirable images - Removed images smaller than 150 pixels or larger than 20,000 pixels - Adjusted aspect ratio thresholds for HTML (2:1) and PDF (3:1) to preserve scientific figures 4. Text Processing: - Used fasttext for language identification, focusing on English content - Masked personally identifiable information such as email addresses and IP addresses - Applied paragraph and document-level deduplication using Bloom filters 5. PDF Specific Processing: - Used PyMuPDF for parsing PDFs and extracting reading order - Clustered text blocks based on columns and ordered from top left to bottom right 6. ArXiv Specific Processing: - Used TexSoup to parse LaTeX source code and interleave images with text - Cleaned up LaTeX code by removing imports, bibliography, tables, and citation tags Various open-source tools were utilized in this process, including fasttext, [PyMuPDF](https://github.com/pymupdf/PyMuPDF), and [DCLM](https://www.datacomp.ai/dclm/) and [bff](https://github.com/revbucket/bff) for deduplication and content filtering. #### Personal and Sensitive Information Despite sourcing from public web data, significant efforts were made to minimize the inclusion of personal and sensitive information: - Email addresses and IP addresses were masked to protect privacy - An NSFW image classifierto remove inappropriate visual content - URLs containing substrings associated with undesirable or sensitive content were filtered out However, users should be aware that as the data originates from the public web, it may still contain some sensitive or personal information. The dataset creators acknowledge this limitation and advise users to exercise caution and potentially apply additional filtering based on their specific use cases. ## Bias, Risks, and Limitations Several potential biases, risks, and limitations have been identified: 1. Data Bias: As the dataset is sourced from web crawls, it may inherit biases present in online content. 2. Content Risks: Despite extensive filtering, there's a possibility that some offensive, insensitive, or inappropriate content may remain in the dataset. 3. Image Availability: The dataset relies on external image URLs, which may become unavailable over time due to link rot, potentially affecting the dataset's long-term usability. 4. PDF Parsing Limitations: The current method for extracting reading order from PDFs may not always accurately capture the intended flow, especially for documents with complex layouts. 5. Potential Legal and Ethical Concerns: While efforts were made to respect robots.txt files and remove sensitive information, there may still be content that individuals did not explicitly consent to include. ### Recommendations Given these considerations, the following recommendations are provided: 1. Additional Filtering: Users are strongly encouraged to apply additional filtering based on their specific use case and ethical considerations. 2. Inappropriate Use Cases: The dataset is not recommended for applications involving the processing or generation of personally identifying information, nor for military applications. 3. Legal Compliance: Users should independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. 4. Bias Awareness: Researchers and developers should be cognizant of potential biases in the dataset and consider their impact on model training and outputs. ## License We release 🍃 MINT-1T under a CC-BY-4.0 license, designating it primarily as a research artifact. While the dataset is freely available, users are responsible for ensuring its legal use in commercial settings. Users must independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. ## Citation ``` @article{awadalla2024mint1t, title={MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens}, author={Anas Awadalla and Le Xue and Oscar Lo and Manli Shu and Hannah Lee and Etash Kumar Guha and Matt Jordan and Sheng Shen and Mohamed Awadalla and Silvio Savarese and Caiming Xiong and Ran Xu and Yejin Choi and Ludwig Schmidt}, year={2024} } ```

mlfoundations/MINT-1T-ArXiv

mlfoundations

2024-09-19T21:32:59Z

13,702

47

[ "task_categories:image-to-text", "task_categories:text-generation", "language:en", "license:cc-by-4.0", "size_categories:1M<n<10M", "format:webdataset", "modality:image", "modality:text", "library:datasets", "library:webdataset", "library:mlcroissant", "arxiv:2406.11271", "region:us", "multimodal" ]

[ "image-to-text", "text-generation" ]

2024-06-29T23:50:55Z

null

--- license: cc-by-4.0 task_categories: - image-to-text - text-generation language: - en tags: - multimodal pretty_name: MINT-1T size_categories: - 100B<n<1T --- <h1 align="center"> 🍃 MINT-1T:<br>Scaling Open-Source Multimodal Data by 10x:<br> A Multimodal Dataset with One Trillion Tokens </h1> 🍃 MINT-1T is an open-source **M**ultimodal **INT**erleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. 🍃 MINT-1T is created by a team from the University of Washington in collaboration with Salesforce Research, other academic institutions including Stanford University, University of Texas at Austin, and University of California Berkeley. You are currently viewing the ArXiv subset of 🍃 MINT-1T. For HTML and PDF subsets, please refer to the [🍃 MINT-1T collection](https://huggingface.co/collections/mlfoundations/mint-1t-6690216ca4d0df7e518dde1c). ![Examples](interleaved-example-twitter.png) ## Dataset Details ### Dataset Sources - **Repository**: https://github.com/mlfoundations/MINT-1T - **Paper:** https://arxiv.org/abs/2406.11271 - **Blog:** https://blog.salesforceairesearch.com/mint-1t/ ## Uses ### Direct Use  🍃 MINT-1T is designed to facilitate research in multimodal pretraining. The dataset can be used for training multimodal models that can reson about interleaved text and images sequences such as [Idefics2](https://huggingface.co/HuggingFaceM4/idefics2-8b), [XGen-MM](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-r-v1), and [Chameleon](https://huggingface.co/facebook/chameleon-30b). ### Out-of-Scope Use  🍃 MINT-1T was built to make research into large multimodal models more accessible. Using the dataset to train models that ingest or generate personally identifying information (such as images of people’s faces and other sensitive content) as well as military applications are all inappropriate use cases of 🍃 MINT-1T. ## Dataset Creation ### Curation Rationale 🍃 MINT-1T was created to address a significant gap in the open-source domain by providing a large-scale multimodal interleaved dataset for pre-training large multimodal models. This dataset aims to be a valuable resource for the research community, facilitating open science in multimodal pretraining. ### Source Data The dataset is a comprehensive collection of multimodal documents from various sources: - HTML documents: Filtered from CommonCrawl WARC dumps spanning from 2017 to 2024 - PDF documents: Extracted from CommonCrawl WAT dumps covering 2023 to 2024 - ArXiv documents: A subset of papers from the ArXiv repository In total, 🍃 MINT-1T contains 1056.8 million documents, broken down as follows: - 1029.4 million HTML documents - 24.0 million PDF documents - 0.6 million ArXiv documents #### Data Collection and Processing The data collection and processing involved several steps: 1. Document Extraction: - HTML documents were parsed from CommonCrawl WARC files - PDF documents were extracted from CommonCrawl WAT files - ArXiv papers were directly sourced from ArXiv S3 buckets 2. Filtering Process: - Applied text quality filters to ensure content relevance and readability - Removed duplicate content at both paragraph and document levels - Filtered out undesirable content based on predefined criteria - Verified image availability and quality for HTML documents - Limited PDF size to 50MB and 50 pages to manage dataset size and quality 3. Image Processing: - Used NSFW image detection to remove pornographic or otherwise undesirable images - Removed images smaller than 150 pixels or larger than 20,000 pixels - Adjusted aspect ratio thresholds for HTML (2:1) and PDF (3:1) to preserve scientific figures 4. Text Processing: - Used fasttext for language identification, focusing on English content - Masked personally identifiable information such as email addresses and IP addresses - Applied paragraph and document-level deduplication using Bloom filters 5. PDF Specific Processing: - Used PyMuPDF for parsing PDFs and extracting reading order - Clustered text blocks based on columns and ordered from top left to bottom right 6. ArXiv Specific Processing: - Used TexSoup to parse LaTeX source code and interleave images with text - Cleaned up LaTeX code by removing imports, bibliography, tables, and citation tags Various open-source tools were utilized in this process, including fasttext, [PyMuPDF](https://github.com/pymupdf/PyMuPDF), and [DCLM](https://www.datacomp.ai/dclm/) and [bff](https://github.com/revbucket/bff) for deduplication and content filtering. #### Personal and Sensitive Information Despite sourcing from public web data, significant efforts were made to minimize the inclusion of personal and sensitive information: - Email addresses and IP addresses were masked to protect privacy - An NSFW image classifierto remove inappropriate visual content - URLs containing substrings associated with undesirable or sensitive content were filtered out However, users should be aware that as the data originates from the public web, it may still contain some sensitive or personal information. The dataset creators acknowledge this limitation and advise users to exercise caution and potentially apply additional filtering based on their specific use cases. ## Bias, Risks, and Limitations Several potential biases, risks, and limitations have been identified: 1. Data Bias: As the dataset is sourced from web crawls, it may inherit biases present in online content. 2. Content Risks: Despite extensive filtering, there's a possibility that some offensive, insensitive, or inappropriate content may remain in the dataset. 3. Image Availability: The dataset relies on external image URLs, which may become unavailable over time due to link rot, potentially affecting the dataset's long-term usability. 4. PDF Parsing Limitations: The current method for extracting reading order from PDFs may not always accurately capture the intended flow, especially for documents with complex layouts. 5. Potential Legal and Ethical Concerns: While efforts were made to respect robots.txt files and remove sensitive information, there may still be content that individuals did not explicitly consent to include. ### Recommendations Given these considerations, the following recommendations are provided: 1. Additional Filtering: Users are strongly encouraged to apply additional filtering based on their specific use case and ethical considerations. 2. Inappropriate Use Cases: The dataset is not recommended for applications involving the processing or generation of personally identifying information, nor for military applications. 3. Legal Compliance: Users should independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. 4. Bias Awareness: Researchers and developers should be cognizant of potential biases in the dataset and consider their impact on model training and outputs. ## License We release 🍃 MINT-1T under a CC-BY-4.0 license, designating it primarily as a research artifact. While the dataset is freely available, users are responsible for ensuring its legal use in commercial settings. Users must independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. ## Citation ``` @article{awadalla2024mint1t, title={MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens}, author={Anas Awadalla and Le Xue and Oscar Lo and Manli Shu and Hannah Lee and Etash Kumar Guha and Matt Jordan and Sheng Shen and Mohamed Awadalla and Silvio Savarese and Caiming Xiong and Ran Xu and Yejin Choi and Ludwig Schmidt}, year={2024} } ```

mlfoundations/MINT-1T-PDF-CC-2023-14

mlfoundations

2024-09-19T21:07:39Z

63,544

1

[ "task_categories:image-to-text", "task_categories:text-generation", "language:en", "license:cc-by-4.0", "size_categories:1M<n<10M", "format:webdataset", "modality:image", "modality:text", "library:datasets", "library:webdataset", "library:mlcroissant", "arxiv:2406.11271", "region:us", "multimodal" ]

[ "image-to-text", "text-generation" ]

2024-07-12T05:44:44Z

null

--- license: cc-by-4.0 task_categories: - image-to-text - text-generation language: - en tags: - multimodal pretty_name: MINT-1T size_categories: - 100B<n<1T --- <h1 align="center"> 🍃 MINT-1T:<br>Scaling Open-Source Multimodal Data by 10x:<br> A Multimodal Dataset with One Trillion Tokens </h1> 🍃 MINT-1T is an open-source **M**ultimodal **INT**erleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. 🍃 MINT-1T is created by a team from the University of Washington in collaboration with Salesforce Research, other academic institutions including Stanford University, University of Texas at Austin, and University of California Berkeley. You are currently viewing a subset of the PDF portion of 🍃 MINT-1T associated with CommonCrawl dump `CC-2023-14`. For other PDF, HTML, and ArXiv subsets, refer to the [🍃 MINT-1T collection](https://huggingface.co/collections/mlfoundations/mint-1t-6690216ca4d0df7e518dde1c). ![Examples](interleaved-example-twitter.png) ## Updates ### 9/19/24 We have removed roughly 10% of the PDF samples as there was a mismatch between the frames in the TIFF images and the document metadata. ### 8/8/24 We have become aware that the image hashes in the PDF subset of MINT-1T do not match the images in the documents. We want to emphasize that the images for each document are correct, and only the image hashes in the documents' metadata are mislabeled. ## Dataset Details ### Dataset Sources - **Repository**: https://github.com/mlfoundations/MINT-1T - **Paper:** https://arxiv.org/abs/2406.11271 - **Blog:** https://blog.salesforceairesearch.com/mint-1t/ ## Uses ### Direct Use  🍃 MINT-1T is designed to facilitate research in multimodal pretraining. The dataset can be used for training multimodal models that can reson about interleaved text and images sequences such as [Idefics2](https://huggingface.co/HuggingFaceM4/idefics2-8b), [XGen-MM](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-r-v1), and [Chameleon](https://huggingface.co/facebook/chameleon-30b). ### Out-of-Scope Use  🍃 MINT-1T was built to make research into large multimodal models more accessible. Using the dataset to train models that ingest or generate personally identifying information (such as images of people’s faces and other sensitive content) as well as military applications are all inappropriate use cases of 🍃 MINT-1T. ## Dataset Creation ### Curation Rationale 🍃 MINT-1T was created to address a significant gap in the open-source domain by providing a large-scale multimodal interleaved dataset for pre-training large multimodal models. This dataset aims to be a valuable resource for the research community, facilitating open science in multimodal pretraining. ### Source Data The dataset is a comprehensive collection of multimodal documents from various sources: - HTML documents: Filtered from CommonCrawl WARC dumps spanning from 2017 to 2024 - PDF documents: Extracted from CommonCrawl WAT dumps covering 2023 to 2024 - ArXiv documents: A subset of papers from the ArXiv repository In total, 🍃 MINT-1T contains 1056.8 million documents, broken down as follows: - 1029.4 million HTML documents - 24.0 million PDF documents - 0.6 million ArXiv documents #### Data Collection and Processing The data collection and processing involved several steps: 1. Document Extraction: - HTML documents were parsed from CommonCrawl WARC files - PDF documents were extracted from CommonCrawl WAT files - ArXiv papers were directly sourced from ArXiv S3 buckets 2. Filtering Process: - Applied text quality filters to ensure content relevance and readability - Removed duplicate content at both paragraph and document levels - Filtered out undesirable content based on predefined criteria - Verified image availability and quality for HTML documents - Limited PDF size to 50MB and 50 pages to manage dataset size and quality 3. Image Processing: - Used NSFW image detection to remove pornographic or otherwise undesirable images - Removed images smaller than 150 pixels or larger than 20,000 pixels - Adjusted aspect ratio thresholds for HTML (2:1) and PDF (3:1) to preserve scientific figures 4. Text Processing: - Used fasttext for language identification, focusing on English content - Masked personally identifiable information such as email addresses and IP addresses - Applied paragraph and document-level deduplication using Bloom filters 5. PDF Specific Processing: - Used PyMuPDF for parsing PDFs and extracting reading order - Clustered text blocks based on columns and ordered from top left to bottom right 6. ArXiv Specific Processing: - Used TexSoup to parse LaTeX source code and interleave images with text - Cleaned up LaTeX code by removing imports, bibliography, tables, and citation tags Various open-source tools were utilized in this process, including fasttext, [PyMuPDF](https://github.com/pymupdf/PyMuPDF), and [DCLM](https://www.datacomp.ai/dclm/) and [bff](https://github.com/revbucket/bff) for deduplication and content filtering. #### Personal and Sensitive Information Despite sourcing from public web data, significant efforts were made to minimize the inclusion of personal and sensitive information: - Email addresses and IP addresses were masked to protect privacy - An NSFW image classifierto remove inappropriate visual content - URLs containing substrings associated with undesirable or sensitive content were filtered out However, users should be aware that as the data originates from the public web, it may still contain some sensitive or personal information. The dataset creators acknowledge this limitation and advise users to exercise caution and potentially apply additional filtering based on their specific use cases. ## Bias, Risks, and Limitations Several potential biases, risks, and limitations have been identified: 1. Data Bias: As the dataset is sourced from web crawls, it may inherit biases present in online content. 2. Content Risks: Despite extensive filtering, there's a possibility that some offensive, insensitive, or inappropriate content may remain in the dataset. 3. Image Availability: The dataset relies on external image URLs, which may become unavailable over time due to link rot, potentially affecting the dataset's long-term usability. 4. PDF Parsing Limitations: The current method for extracting reading order from PDFs may not always accurately capture the intended flow, especially for documents with complex layouts. 5. Potential Legal and Ethical Concerns: While efforts were made to respect robots.txt files and remove sensitive information, there may still be content that individuals did not explicitly consent to include. ### Recommendations Given these considerations, the following recommendations are provided: 1. Additional Filtering: Users are strongly encouraged to apply additional filtering based on their specific use case and ethical considerations. 2. Inappropriate Use Cases: The dataset is not recommended for applications involving the processing or generation of personally identifying information, nor for military applications. 3. Legal Compliance: Users should independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. 4. Bias Awareness: Researchers and developers should be cognizant of potential biases in the dataset and consider their impact on model training and outputs. ## License We release 🍃 MINT-1T under a CC-BY-4.0 license, designating it primarily as a research artifact. While the dataset is freely available, users are responsible for ensuring its legal use in commercial settings. Users must independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. ## Citation ``` @article{awadalla2024mint1t, title={MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens}, author={Anas Awadalla and Le Xue and Oscar Lo and Manli Shu and Hannah Lee and Etash Kumar Guha and Matt Jordan and Sheng Shen and Mohamed Awadalla and Silvio Savarese and Caiming Xiong and Ran Xu and Yejin Choi and Ludwig Schmidt}, year={2024} } ```

mlfoundations/MINT-1T-PDF-CC-2023-40

mlfoundations

2024-09-19T21:06:59Z

52,154

1

[ "task_categories:image-to-text", "task_categories:text-generation", "language:en", "license:cc-by-4.0", "size_categories:100B<n<1T", "arxiv:2406.11271", "region:us", "multimodal" ]

[ "image-to-text", "text-generation" ]

2024-07-12T05:43:23Z

null

--- license: cc-by-4.0 task_categories: - image-to-text - text-generation language: - en tags: - multimodal pretty_name: MINT-1T size_categories: - 100B<n<1T --- <h1 align="center"> 🍃 MINT-1T:<br>Scaling Open-Source Multimodal Data by 10x:<br> A Multimodal Dataset with One Trillion Tokens </h1> 🍃 MINT-1T is an open-source **M**ultimodal **INT**erleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. 🍃 MINT-1T is created by a team from the University of Washington in collaboration with Salesforce Research, other academic institutions including Stanford University, University of Texas at Austin, and University of California Berkeley. You are currently viewing a subset of the PDF portion of 🍃 MINT-1T associated with CommonCrawl dump `CC-2023-40`. For other PDF, HTML, and ArXiv subsets, refer to the [🍃 MINT-1T collection](https://huggingface.co/collections/mlfoundations/mint-1t-6690216ca4d0df7e518dde1c). ![Examples](interleaved-example-twitter.png) ## Updates ### 9/19/24 We have removed roughly 10% of the PDF samples as there was a mismatch between the frames in the TIFF images and the document metadata. ### 8/8/24 We have become aware that the image hashes in the PDF subset of MINT-1T do not match the images in the documents. We want to emphasize that the images for each document are correct, and only the image hashes in the documents' metadata are mislabeled. ## Dataset Details ### Dataset Sources - **Repository**: https://github.com/mlfoundations/MINT-1T - **Paper:** https://arxiv.org/abs/2406.11271 - **Blog:** https://blog.salesforceairesearch.com/mint-1t/ ## Uses ### Direct Use  🍃 MINT-1T is designed to facilitate research in multimodal pretraining. The dataset can be used for training multimodal models that can reson about interleaved text and images sequences such as [Idefics2](https://huggingface.co/HuggingFaceM4/idefics2-8b), [XGen-MM](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-r-v1), and [Chameleon](https://huggingface.co/facebook/chameleon-30b). ### Out-of-Scope Use  🍃 MINT-1T was built to make research into large multimodal models more accessible. Using the dataset to train models that ingest or generate personally identifying information (such as images of people’s faces and other sensitive content) as well as military applications are all inappropriate use cases of 🍃 MINT-1T. ## Dataset Creation ### Curation Rationale 🍃 MINT-1T was created to address a significant gap in the open-source domain by providing a large-scale multimodal interleaved dataset for pre-training large multimodal models. This dataset aims to be a valuable resource for the research community, facilitating open science in multimodal pretraining. ### Source Data The dataset is a comprehensive collection of multimodal documents from various sources: - HTML documents: Filtered from CommonCrawl WARC dumps spanning from 2017 to 2024 - PDF documents: Extracted from CommonCrawl WAT dumps covering 2023 to 2024 - ArXiv documents: A subset of papers from the ArXiv repository In total, 🍃 MINT-1T contains 1056.8 million documents, broken down as follows: - 1029.4 million HTML documents - 24.0 million PDF documents - 0.6 million ArXiv documents #### Data Collection and Processing The data collection and processing involved several steps: 1. Document Extraction: - HTML documents were parsed from CommonCrawl WARC files - PDF documents were extracted from CommonCrawl WAT files - ArXiv papers were directly sourced from ArXiv S3 buckets 2. Filtering Process: - Applied text quality filters to ensure content relevance and readability - Removed duplicate content at both paragraph and document levels - Filtered out undesirable content based on predefined criteria - Verified image availability and quality for HTML documents - Limited PDF size to 50MB and 50 pages to manage dataset size and quality 3. Image Processing: - Used NSFW image detection to remove pornographic or otherwise undesirable images - Removed images smaller than 150 pixels or larger than 20,000 pixels - Adjusted aspect ratio thresholds for HTML (2:1) and PDF (3:1) to preserve scientific figures 4. Text Processing: - Used fasttext for language identification, focusing on English content - Masked personally identifiable information such as email addresses and IP addresses - Applied paragraph and document-level deduplication using Bloom filters 5. PDF Specific Processing: - Used PyMuPDF for parsing PDFs and extracting reading order - Clustered text blocks based on columns and ordered from top left to bottom right 6. ArXiv Specific Processing: - Used TexSoup to parse LaTeX source code and interleave images with text - Cleaned up LaTeX code by removing imports, bibliography, tables, and citation tags Various open-source tools were utilized in this process, including fasttext, [PyMuPDF](https://github.com/pymupdf/PyMuPDF), and [DCLM](https://www.datacomp.ai/dclm/) and [bff](https://github.com/revbucket/bff) for deduplication and content filtering. #### Personal and Sensitive Information Despite sourcing from public web data, significant efforts were made to minimize the inclusion of personal and sensitive information: - Email addresses and IP addresses were masked to protect privacy - An NSFW image classifierto remove inappropriate visual content - URLs containing substrings associated with undesirable or sensitive content were filtered out However, users should be aware that as the data originates from the public web, it may still contain some sensitive or personal information. The dataset creators acknowledge this limitation and advise users to exercise caution and potentially apply additional filtering based on their specific use cases. ## Bias, Risks, and Limitations Several potential biases, risks, and limitations have been identified: 1. Data Bias: As the dataset is sourced from web crawls, it may inherit biases present in online content. 2. Content Risks: Despite extensive filtering, there's a possibility that some offensive, insensitive, or inappropriate content may remain in the dataset. 3. Image Availability: The dataset relies on external image URLs, which may become unavailable over time due to link rot, potentially affecting the dataset's long-term usability. 4. PDF Parsing Limitations: The current method for extracting reading order from PDFs may not always accurately capture the intended flow, especially for documents with complex layouts. 5. Potential Legal and Ethical Concerns: While efforts were made to respect robots.txt files and remove sensitive information, there may still be content that individuals did not explicitly consent to include. ### Recommendations Given these considerations, the following recommendations are provided: 1. Additional Filtering: Users are strongly encouraged to apply additional filtering based on their specific use case and ethical considerations. 2. Inappropriate Use Cases: The dataset is not recommended for applications involving the processing or generation of personally identifying information, nor for military applications. 3. Legal Compliance: Users should independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. 4. Bias Awareness: Researchers and developers should be cognizant of potential biases in the dataset and consider their impact on model training and outputs. ## License We release 🍃 MINT-1T under a CC-BY-4.0 license, designating it primarily as a research artifact. While the dataset is freely available, users are responsible for ensuring its legal use in commercial settings. Users must independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. ## Citation ``` @article{awadalla2024mint1t, title={MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens}, author={Anas Awadalla and Le Xue and Oscar Lo and Manli Shu and Hannah Lee and Etash Kumar Guha and Matt Jordan and Sheng Shen and Mohamed Awadalla and Silvio Savarese and Caiming Xiong and Ran Xu and Yejin Choi and Ludwig Schmidt}, year={2024} } ```

mlfoundations/MINT-1T-PDF-CC-2023-50

mlfoundations

2024-09-19T21:06:23Z

28,482

3

[ "task_categories:image-to-text", "task_categories:text-generation", "language:en", "license:cc-by-4.0", "size_categories:1M<n<10M", "format:webdataset", "modality:image", "modality:text", "library:datasets", "library:webdataset", "library:mlcroissant", "arxiv:2406.11271", "region:us", "multimodal" ]

[ "image-to-text", "text-generation" ]

2024-07-12T05:42:22Z

null

--- license: cc-by-4.0 task_categories: - image-to-text - text-generation language: - en tags: - multimodal pretty_name: MINT-1T size_categories: - 100B<n<1T --- <h1 align="center"> 🍃 MINT-1T:<br>Scaling Open-Source Multimodal Data by 10x:<br> A Multimodal Dataset with One Trillion Tokens </h1> 🍃 MINT-1T is an open-source **M**ultimodal **INT**erleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. 🍃 MINT-1T is created by a team from the University of Washington in collaboration with Salesforce Research, other academic institutions including Stanford University, University of Texas at Austin, and University of California Berkeley. You are currently viewing a subset of the PDF portion of 🍃 MINT-1T associated with CommonCrawl dump `CC-2023-50`. For other PDF, HTML, and ArXiv subsets, refer to the [🍃 MINT-1T collection](https://huggingface.co/collections/mlfoundations/mint-1t-6690216ca4d0df7e518dde1c). ![Examples](interleaved-example-twitter.png) ## Updates ### 9/19/24 We have removed roughly 10% of the PDF samples as there was a mismatch between the frames in the TIFF images and the document metadata. ### 8/8/24 We have become aware that the image hashes in the PDF subset of MINT-1T do not match the images in the documents. We want to emphasize that the images for each document are correct, and only the image hashes in the documents' metadata are mislabeled. ## Dataset Details ### Dataset Sources - **Repository**: https://github.com/mlfoundations/MINT-1T - **Paper:** https://arxiv.org/abs/2406.11271 - **Blog:** https://blog.salesforceairesearch.com/mint-1t/ ## Uses ### Direct Use  🍃 MINT-1T is designed to facilitate research in multimodal pretraining. The dataset can be used for training multimodal models that can reson about interleaved text and images sequences such as [Idefics2](https://huggingface.co/HuggingFaceM4/idefics2-8b), [XGen-MM](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-r-v1), and [Chameleon](https://huggingface.co/facebook/chameleon-30b). ### Out-of-Scope Use  🍃 MINT-1T was built to make research into large multimodal models more accessible. Using the dataset to train models that ingest or generate personally identifying information (such as images of people’s faces and other sensitive content) as well as military applications are all inappropriate use cases of 🍃 MINT-1T. ## Dataset Creation ### Curation Rationale 🍃 MINT-1T was created to address a significant gap in the open-source domain by providing a large-scale multimodal interleaved dataset for pre-training large multimodal models. This dataset aims to be a valuable resource for the research community, facilitating open science in multimodal pretraining. ### Source Data The dataset is a comprehensive collection of multimodal documents from various sources: - HTML documents: Filtered from CommonCrawl WARC dumps spanning from 2017 to 2024 - PDF documents: Extracted from CommonCrawl WAT dumps covering 2023 to 2024 - ArXiv documents: A subset of papers from the ArXiv repository In total, 🍃 MINT-1T contains 1056.8 million documents, broken down as follows: - 1029.4 million HTML documents - 24.0 million PDF documents - 0.6 million ArXiv documents #### Data Collection and Processing The data collection and processing involved several steps: 1. Document Extraction: - HTML documents were parsed from CommonCrawl WARC files - PDF documents were extracted from CommonCrawl WAT files - ArXiv papers were directly sourced from ArXiv S3 buckets 2. Filtering Process: - Applied text quality filters to ensure content relevance and readability - Removed duplicate content at both paragraph and document levels - Filtered out undesirable content based on predefined criteria - Verified image availability and quality for HTML documents - Limited PDF size to 50MB and 50 pages to manage dataset size and quality 3. Image Processing: - Used NSFW image detection to remove pornographic or otherwise undesirable images - Removed images smaller than 150 pixels or larger than 20,000 pixels - Adjusted aspect ratio thresholds for HTML (2:1) and PDF (3:1) to preserve scientific figures 4. Text Processing: - Used fasttext for language identification, focusing on English content - Masked personally identifiable information such as email addresses and IP addresses - Applied paragraph and document-level deduplication using Bloom filters 5. PDF Specific Processing: - Used PyMuPDF for parsing PDFs and extracting reading order - Clustered text blocks based on columns and ordered from top left to bottom right 6. ArXiv Specific Processing: - Used TexSoup to parse LaTeX source code and interleave images with text - Cleaned up LaTeX code by removing imports, bibliography, tables, and citation tags Various open-source tools were utilized in this process, including fasttext, [PyMuPDF](https://github.com/pymupdf/PyMuPDF), and [DCLM](https://www.datacomp.ai/dclm/) and [bff](https://github.com/revbucket/bff) for deduplication and content filtering. #### Personal and Sensitive Information Despite sourcing from public web data, significant efforts were made to minimize the inclusion of personal and sensitive information: - Email addresses and IP addresses were masked to protect privacy - An NSFW image classifierto remove inappropriate visual content - URLs containing substrings associated with undesirable or sensitive content were filtered out However, users should be aware that as the data originates from the public web, it may still contain some sensitive or personal information. The dataset creators acknowledge this limitation and advise users to exercise caution and potentially apply additional filtering based on their specific use cases. ## Bias, Risks, and Limitations Several potential biases, risks, and limitations have been identified: 1. Data Bias: As the dataset is sourced from web crawls, it may inherit biases present in online content. 2. Content Risks: Despite extensive filtering, there's a possibility that some offensive, insensitive, or inappropriate content may remain in the dataset. 3. Image Availability: The dataset relies on external image URLs, which may become unavailable over time due to link rot, potentially affecting the dataset's long-term usability. 4. PDF Parsing Limitations: The current method for extracting reading order from PDFs may not always accurately capture the intended flow, especially for documents with complex layouts. 5. Potential Legal and Ethical Concerns: While efforts were made to respect robots.txt files and remove sensitive information, there may still be content that individuals did not explicitly consent to include. ### Recommendations Given these considerations, the following recommendations are provided: 1. Additional Filtering: Users are strongly encouraged to apply additional filtering based on their specific use case and ethical considerations. 2. Inappropriate Use Cases: The dataset is not recommended for applications involving the processing or generation of personally identifying information, nor for military applications. 3. Legal Compliance: Users should independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. 4. Bias Awareness: Researchers and developers should be cognizant of potential biases in the dataset and consider their impact on model training and outputs. ## License We release 🍃 MINT-1T under a CC-BY-4.0 license, designating it primarily as a research artifact. While the dataset is freely available, users are responsible for ensuring its legal use in commercial settings. Users must independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. ## Citation ``` @article{awadalla2024mint1t, title={MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens}, author={Anas Awadalla and Le Xue and Oscar Lo and Manli Shu and Hannah Lee and Etash Kumar Guha and Matt Jordan and Sheng Shen and Mohamed Awadalla and Silvio Savarese and Caiming Xiong and Ran Xu and Yejin Choi and Ludwig Schmidt}, year={2024} } ```

mlfoundations/MINT-1T-PDF-CC-2024-18

mlfoundations

2024-09-19T21:02:55Z

41,126

19

[ "task_categories:image-to-text", "task_categories:text-generation", "language:en", "license:cc-by-4.0", "size_categories:100B<n<1T", "arxiv:2406.11271", "region:us", "multimodal" ]

[ "image-to-text", "text-generation" ]

2024-07-15T03:19:33Z

null

--- license: cc-by-4.0 task_categories: - image-to-text - text-generation language: - en tags: - multimodal pretty_name: MINT-1T size_categories: - 100B<n<1T configs: - config_name: default data_files: - split: train path: CC-MAIN-*/* --- <h1 align="center"> 🍃 MINT-1T:<br>Scaling Open-Source Multimodal Data by 10x:<br> A Multimodal Dataset with One Trillion Tokens </h1> 🍃 MINT-1T is an open-source **M**ultimodal **INT**erleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. 🍃 MINT-1T is created by a team from the University of Washington in collaboration with Salesforce Research, other academic institutions including Stanford University, University of Texas at Austin, and University of California Berkeley. You are currently viewing a subset of the PDF portion of 🍃 MINT-1T associated with CommonCrawl dump `CC-2024-18`. For other PDF, HTML, and ArXiv subsets, refer to the [🍃 MINT-1T collection](https://huggingface.co/collections/mlfoundations/mint-1t-6690216ca4d0df7e518dde1c). ![Examples](interleaved-example-twitter.png) ## Updates ### 9/19/24 We have removed roughly 10% of the PDF samples as there was a mismatch between the frames in the TIFF images and the document metadata. ### 8/8/24 We have become aware that the image hashes in the PDF subset of MINT-1T do not match the images in the documents. We want to emphasize that the images for each document are correct, and only the image hashes in the documents' metadata are mislabeled. ## Dataset Details ### Dataset Sources - **Repository**: https://github.com/mlfoundations/MINT-1T - **Paper:** https://arxiv.org/abs/2406.11271 - **Blog:** https://blog.salesforceairesearch.com/mint-1t/ ## Uses ### Direct Use  🍃 MINT-1T is designed to facilitate research in multimodal pretraining. The dataset can be used for training multimodal models that can reson about interleaved text and images sequences such as [Idefics2](https://huggingface.co/HuggingFaceM4/idefics2-8b), [XGen-MM](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-r-v1), and [Chameleon](https://huggingface.co/facebook/chameleon-30b). ### Out-of-Scope Use  🍃 MINT-1T was built to make research into large multimodal models more accessible. Using the dataset to train models that ingest or generate personally identifying information (such as images of people’s faces and other sensitive content) as well as military applications are all inappropriate use cases of 🍃 MINT-1T. ## Dataset Creation ### Curation Rationale 🍃 MINT-1T was created to address a significant gap in the open-source domain by providing a large-scale multimodal interleaved dataset for pre-training large multimodal models. This dataset aims to be a valuable resource for the research community, facilitating open science in multimodal pretraining. ### Source Data The dataset is a comprehensive collection of multimodal documents from various sources: - HTML documents: Filtered from CommonCrawl WARC dumps spanning from 2017 to 2024 - PDF documents: Extracted from CommonCrawl WAT dumps covering 2023 to 2024 - ArXiv documents: A subset of papers from the ArXiv repository In total, 🍃 MINT-1T contains 1056.8 million documents, broken down as follows: - 1029.4 million HTML documents - 24.0 million PDF documents - 0.6 million ArXiv documents #### Data Collection and Processing The data collection and processing involved several steps: 1. Document Extraction: - HTML documents were parsed from CommonCrawl WARC files - PDF documents were extracted from CommonCrawl WAT files - ArXiv papers were directly sourced from ArXiv S3 buckets 2. Filtering Process: - Applied text quality filters to ensure content relevance and readability - Removed duplicate content at both paragraph and document levels - Filtered out undesirable content based on predefined criteria - Verified image availability and quality for HTML documents - Limited PDF size to 50MB and 50 pages to manage dataset size and quality 3. Image Processing: - Used NSFW image detection to remove pornographic or otherwise undesirable images - Removed images smaller than 150 pixels or larger than 20,000 pixels - Adjusted aspect ratio thresholds for HTML (2:1) and PDF (3:1) to preserve scientific figures 4. Text Processing: - Used fasttext for language identification, focusing on English content - Masked personally identifiable information such as email addresses and IP addresses - Applied paragraph and document-level deduplication using Bloom filters 5. PDF Specific Processing: - Used PyMuPDF for parsing PDFs and extracting reading order - Clustered text blocks based on columns and ordered from top left to bottom right 6. ArXiv Specific Processing: - Used TexSoup to parse LaTeX source code and interleave images with text - Cleaned up LaTeX code by removing imports, bibliography, tables, and citation tags Various open-source tools were utilized in this process, including fasttext, [PyMuPDF](https://github.com/pymupdf/PyMuPDF), and [DCLM](https://www.datacomp.ai/dclm/) and [bff](https://github.com/revbucket/bff) for deduplication and content filtering. #### Personal and Sensitive Information Despite sourcing from public web data, significant efforts were made to minimize the inclusion of personal and sensitive information: - Email addresses and IP addresses were masked to protect privacy - An NSFW image classifierto remove inappropriate visual content - URLs containing substrings associated with undesirable or sensitive content were filtered out However, users should be aware that as the data originates from the public web, it may still contain some sensitive or personal information. The dataset creators acknowledge this limitation and advise users to exercise caution and potentially apply additional filtering based on their specific use cases. ## Bias, Risks, and Limitations Several potential biases, risks, and limitations have been identified: 1. Data Bias: As the dataset is sourced from web crawls, it may inherit biases present in online content. 2. Content Risks: Despite extensive filtering, there's a possibility that some offensive, insensitive, or inappropriate content may remain in the dataset. 3. Image Availability: The dataset relies on external image URLs, which may become unavailable over time due to link rot, potentially affecting the dataset's long-term usability. 4. PDF Parsing Limitations: The current method for extracting reading order from PDFs may not always accurately capture the intended flow, especially for documents with complex layouts. 5. Potential Legal and Ethical Concerns: While efforts were made to respect robots.txt files and remove sensitive information, there may still be content that individuals did not explicitly consent to include. ### Recommendations Given these considerations, the following recommendations are provided: 1. Additional Filtering: Users are strongly encouraged to apply additional filtering based on their specific use case and ethical considerations. 2. Inappropriate Use Cases: The dataset is not recommended for applications involving the processing or generation of personally identifying information, nor for military applications. 3. Legal Compliance: Users should independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. 4. Bias Awareness: Researchers and developers should be cognizant of potential biases in the dataset and consider their impact on model training and outputs. ## License We release 🍃 MINT-1T under a CC-BY-4.0 license, designating it primarily as a research artifact. While the dataset is freely available, users are responsible for ensuring its legal use in commercial settings. Users must independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. ## Citation ``` @article{awadalla2024mint1t, title={MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens}, author={Anas Awadalla and Le Xue and Oscar Lo and Manli Shu and Hannah Lee and Etash Kumar Guha and Matt Jordan and Sheng Shen and Mohamed Awadalla and Silvio Savarese and Caiming Xiong and Ran Xu and Yejin Choi and Ludwig Schmidt}, year={2024} } ```

MMMU/MMMU

MMMU

2024-09-19T17:11:03Z

17,607

251

[ "task_categories:question-answering", "task_categories:visual-question-answering", "task_categories:multiple-choice", "language:en", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2311.16502", "region:us", "biology", "medical", "finance", "chemistry", "music", "art", "art_theory", "design", "business", "accounting", "economics", "manage", "marketing", "health", "medicine", "basic_medical_science", "clinical", "pharmacy", "public_health", "humanities", "social_science", "history", "literature", "sociology", "psychology", "science", "geography", "math", "physics", "engineering", "agriculture", "architecture", "computer_science", "electronics", "energy_and_power", "materials", "mechanical_engineering" ]

[ "question-answering", "visual-question-answering", "multiple-choice" ]

2023-11-27T17:52:01Z

null

--- language: - en license: apache-2.0 size_categories: - 10K<n<100K task_categories: - question-answering - visual-question-answering - multiple-choice pretty_name: mmmu dataset_info: - config_name: Accounting features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 262599.0 num_examples: 5 - name: validation num_bytes: 1598285.0 num_examples: 30 - name: test num_bytes: 22135625.0 num_examples: 380 download_size: 37363379 dataset_size: 23996509.0 - config_name: Agriculture features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 22082656.0 num_examples: 5 - name: validation num_bytes: 119217558.0 num_examples: 30 - name: test num_bytes: 993664077.0 num_examples: 287 download_size: 1158036990 dataset_size: 1134964291.0 - config_name: Architecture_and_Engineering features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 137750.0 num_examples: 5 - name: validation num_bytes: 721378.0 num_examples: 30 - name: test num_bytes: 16054607.0 num_examples: 551 download_size: 48763955 dataset_size: 16913735.0 - config_name: Art features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 6241184.0 num_examples: 5 - name: validation num_bytes: 29934534.0 num_examples: 30 - name: test num_bytes: 237801390.0 num_examples: 231 download_size: 585798641 dataset_size: 273977108.0 - config_name: Art_Theory features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 7435106.0 num_examples: 5 - name: validation num_bytes: 33481558.0 num_examples: 30 - name: test num_bytes: 553174647.0 num_examples: 429 download_size: 930525695 dataset_size: 594091311.0 - config_name: Basic_Medical_Science features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 814310.0 num_examples: 5 - name: validation num_bytes: 4125930.0 num_examples: 30 - name: test num_bytes: 48125891.0 num_examples: 326 download_size: 84666454 dataset_size: 53066131.0 - config_name: Biology features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 574342.0 num_examples: 5 - name: validation num_bytes: 8491863.0 num_examples: 30 - name: test num_bytes: 132966151.0 num_examples: 345 download_size: 410242502 dataset_size: 142032356.0 - config_name: Chemistry features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 262397.0 num_examples: 5 - name: validation num_bytes: 1518573.0 num_examples: 30 - name: test num_bytes: 37219529.0 num_examples: 603 download_size: 108345562 dataset_size: 39000499.0 - config_name: Clinical_Medicine features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 1467945.0 num_examples: 5 - name: validation num_bytes: 10882484.0 num_examples: 30 - name: test num_bytes: 98201863.0 num_examples: 325 download_size: 160611488 dataset_size: 110552292.0 - config_name: Computer_Science features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 440523.0 num_examples: 5 - name: validation num_bytes: 2072018.0 num_examples: 30 - name: test num_bytes: 32047381.0 num_examples: 371 download_size: 55640991 dataset_size: 34559922.0 - config_name: Design features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 2259873.0 num_examples: 5 - name: validation num_bytes: 17923120.0 num_examples: 30 - name: test num_bytes: 77676331.0 num_examples: 169 download_size: 142866617 dataset_size: 97859324.0 - config_name: Diagnostics_and_Laboratory_Medicine features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 2056117.0 num_examples: 5 - name: validation num_bytes: 37106233.0 num_examples: 30 - name: test num_bytes: 157003069.0 num_examples: 162 download_size: 603957093 dataset_size: 196165419.0 - config_name: Economics features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 171434.0 num_examples: 5 - name: validation num_bytes: 1487048.0 num_examples: 30 - name: test num_bytes: 11852300.0 num_examples: 267 download_size: 20777635 dataset_size: 13510782.0 - config_name: Electronics features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 123632.0 num_examples: 5 - name: validation num_bytes: 641377.0 num_examples: 30 - name: test num_bytes: 5717686.0 num_examples: 256 download_size: 11602832 dataset_size: 6482695.0 - config_name: Energy_and_Power features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 105006.0 num_examples: 5 - name: validation num_bytes: 1641935.0 num_examples: 30 - name: test num_bytes: 14748428.0 num_examples: 432 download_size: 35246567 dataset_size: 16495369.0 - config_name: Finance features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 296124.0 num_examples: 5 - name: validation num_bytes: 1071060.0 num_examples: 30 - name: test num_bytes: 12065803.0 num_examples: 355 download_size: 29551521 dataset_size: 13432987.0 - config_name: Geography features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 1494060.0 num_examples: 5 - name: validation num_bytes: 6671316.0 num_examples: 30 - name: test num_bytes: 137218400.0 num_examples: 565 download_size: 374766631 dataset_size: 145383776.0 - config_name: History features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 1444231.0 num_examples: 5 - name: validation num_bytes: 8819857.0 num_examples: 30 - name: test num_bytes: 115228815.0 num_examples: 278 download_size: 232549641 dataset_size: 125492903.0 - config_name: Literature features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 2451201.0 num_examples: 5 - name: validation num_bytes: 14241046.0 num_examples: 30 - name: test num_bytes: 50301541.0 num_examples: 112 download_size: 132145895 dataset_size: 66993788.0 - config_name: Manage features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 449514.0 num_examples: 5 - name: validation num_bytes: 3277436.0 num_examples: 30 - name: test num_bytes: 29963963.0 num_examples: 245 download_size: 51186888 dataset_size: 33690913.0 - config_name: Marketing features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 116960.0 num_examples: 5 - name: validation num_bytes: 1472981.0 num_examples: 30 - name: test num_bytes: 7732976.0 num_examples: 181 download_size: 13146078 dataset_size: 9322917.0 - config_name: Materials features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 239632.0 num_examples: 5 - name: validation num_bytes: 2305223.0 num_examples: 30 - name: test num_bytes: 25256854.0 num_examples: 458 download_size: 105773156 dataset_size: 27801709.0 - config_name: Math features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 175839.0 num_examples: 5 - name: validation num_bytes: 1444496.0 num_examples: 30 - name: test num_bytes: 27701845.0 num_examples: 505 download_size: 174098418 dataset_size: 29322180.0 - config_name: Mechanical_Engineering features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 152542.0 num_examples: 5 - name: validation num_bytes: 874988.0 num_examples: 30 - name: test num_bytes: 15093746.0 num_examples: 429 download_size: 30450114 dataset_size: 16121276.0 - config_name: Music features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 1417615.0 num_examples: 5 - name: validation num_bytes: 9359372.0 num_examples: 30 - name: test num_bytes: 134096770.0 num_examples: 334 download_size: 174725052 dataset_size: 144873757.0 - config_name: Pharmacy features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 207924.0 num_examples: 5 - name: validation num_bytes: 1656342.0 num_examples: 30 - name: test num_bytes: 31866248.0 num_examples: 430 download_size: 62721263 dataset_size: 33730514.0 - config_name: Physics features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 233734.0 num_examples: 5 - name: validation num_bytes: 1114130.0 num_examples: 30 - name: test num_bytes: 15905705.0 num_examples: 408 download_size: 35238571 dataset_size: 17253569.0 - config_name: Psychology features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 600864.0 num_examples: 5 - name: validation num_bytes: 4403886.0 num_examples: 30 - name: test num_bytes: 53813915.0 num_examples: 305 download_size: 102466671 dataset_size: 58818665.0 - config_name: Public_Health features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 234781.0 num_examples: 5 - name: validation num_bytes: 1508761.0 num_examples: 30 - name: test num_bytes: 32150088.0 num_examples: 509 download_size: 48231609 dataset_size: 33893630.0 - config_name: Sociology features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 3769220.0 num_examples: 5 - name: validation num_bytes: 18455336.0 num_examples: 30 - name: test num_bytes: 144301123.0 num_examples: 252 download_size: 310313826 dataset_size: 166525679.0 configs: - config_name: Accounting data_files: - split: dev path: Accounting/dev-* - split: validation path: Accounting/validation-* - split: test path: Accounting/test-* - config_name: Agriculture data_files: - split: dev path: Agriculture/dev-* - split: validation path: Agriculture/validation-* - split: test path: Agriculture/test-* - config_name: Architecture_and_Engineering data_files: - split: dev path: Architecture_and_Engineering/dev-* - split: validation path: Architecture_and_Engineering/validation-* - split: test path: Architecture_and_Engineering/test-* - config_name: Art data_files: - split: dev path: Art/dev-* - split: validation path: Art/validation-* - split: test path: Art/test-* - config_name: Art_Theory data_files: - split: dev path: Art_Theory/dev-* - split: validation path: Art_Theory/validation-* - split: test path: Art_Theory/test-* - config_name: Basic_Medical_Science data_files: - split: dev path: Basic_Medical_Science/dev-* - split: validation path: Basic_Medical_Science/validation-* - split: test path: Basic_Medical_Science/test-* - config_name: Biology data_files: - split: dev path: Biology/dev-* - split: validation path: Biology/validation-* - split: test path: Biology/test-* - config_name: Chemistry data_files: - split: dev path: Chemistry/dev-* - split: validation path: Chemistry/validation-* - split: test path: Chemistry/test-* - config_name: Clinical_Medicine data_files: - split: dev path: Clinical_Medicine/dev-* - split: validation path: Clinical_Medicine/validation-* - split: test path: Clinical_Medicine/test-* - config_name: Computer_Science data_files: - split: dev path: Computer_Science/dev-* - split: validation path: Computer_Science/validation-* - split: test path: Computer_Science/test-* - config_name: Design data_files: - split: dev path: Design/dev-* - split: validation path: Design/validation-* - split: test path: Design/test-* - config_name: Diagnostics_and_Laboratory_Medicine data_files: - split: dev path: Diagnostics_and_Laboratory_Medicine/dev-* - split: validation path: Diagnostics_and_Laboratory_Medicine/validation-* - split: test path: Diagnostics_and_Laboratory_Medicine/test-* - config_name: Economics data_files: - split: dev path: Economics/dev-* - split: validation path: Economics/validation-* - split: test path: Economics/test-* - config_name: Electronics data_files: - split: dev path: Electronics/dev-* - split: validation path: Electronics/validation-* - split: test path: Electronics/test-* - config_name: Energy_and_Power data_files: - split: dev path: Energy_and_Power/dev-* - split: validation path: Energy_and_Power/validation-* - split: test path: Energy_and_Power/test-* - config_name: Finance data_files: - split: dev path: Finance/dev-* - split: validation path: Finance/validation-* - split: test path: Finance/test-* - config_name: Geography data_files: - split: dev path: Geography/dev-* - split: validation path: Geography/validation-* - split: test path: Geography/test-* - config_name: History data_files: - split: dev path: History/dev-* - split: validation path: History/validation-* - split: test path: History/test-* - config_name: Literature data_files: - split: dev path: Literature/dev-* - split: validation path: Literature/validation-* - split: test path: Literature/test-* - config_name: Manage data_files: - split: dev path: Manage/dev-* - split: validation path: Manage/validation-* - split: test path: Manage/test-* - config_name: Marketing data_files: - split: dev path: Marketing/dev-* - split: validation path: Marketing/validation-* - split: test path: Marketing/test-* - config_name: Materials data_files: - split: dev path: Materials/dev-* - split: validation path: Materials/validation-* - split: test path: Materials/test-* - config_name: Math data_files: - split: dev path: Math/dev-* - split: validation path: Math/validation-* - split: test path: Math/test-* - config_name: Mechanical_Engineering data_files: - split: dev path: Mechanical_Engineering/dev-* - split: validation path: Mechanical_Engineering/validation-* - split: test path: Mechanical_Engineering/test-* - config_name: Music data_files: - split: dev path: Music/dev-* - split: validation path: Music/validation-* - split: test path: Music/test-* - config_name: Pharmacy data_files: - split: dev path: Pharmacy/dev-* - split: validation path: Pharmacy/validation-* - split: test path: Pharmacy/test-* - config_name: Physics data_files: - split: dev path: Physics/dev-* - split: validation path: Physics/validation-* - split: test path: Physics/test-* - config_name: Psychology data_files: - split: dev path: Psychology/dev-* - split: validation path: Psychology/validation-* - split: test path: Psychology/test-* - config_name: Public_Health data_files: - split: dev path: Public_Health/dev-* - split: validation path: Public_Health/validation-* - split: test path: Public_Health/test-* - config_name: Sociology data_files: - split: dev path: Sociology/dev-* - split: validation path: Sociology/validation-* - split: test path: Sociology/test-* tags: - biology - medical - finance - chemistry - music - art - art_theory - design - music - business - accounting - economics - finance - manage - marketing - health - medicine - basic_medical_science - clinical - pharmacy - public_health - humanities - social_science - history - literature - sociology - psychology - science - biology - chemistry - geography - math - physics - engineering - agriculture - architecture - computer_science - electronics - energy_and_power - materials - mechanical_engineering --- # MMMU (A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI) [**🌐 Homepage**](https://mmmu-benchmark.github.io/) | [**🏆 Leaderboard**](https://mmmu-benchmark.github.io/#leaderboard) | [**🤗 Dataset**](https://huggingface.co/datasets/MMMU/MMMU/) | [**🤗 Paper**](https://huggingface.co/papers/2311.16502) | [**📖 arXiv**](https://arxiv.org/abs/2311.16502) | [**GitHub**](https://github.com/MMMU-Benchmark/MMMU) ## 🔔News - **🛠️[2024-05-30]: Fixed duplicate option issues in Materials dataset items (validation_Materials_25; test_Materials_17, 242) and content error in validation_Materials_25.** - **🛠️[2024-04-30]: Fixed missing "-" or "^" signs in Math dataset items (dev_Math_2, validation_Math_11, 12, 16; test_Math_8, 23, 43, 113, 164, 223, 236, 287, 329, 402, 498) and corrected option errors in validation_Math_2. If you encounter any issues with the dataset, please contact us promptly!** - **🚀[2024-01-31]: We added Human Expert performance on the [Leaderboard](https://mmmu-benchmark.github.io/#leaderboard)!🌟** - **🔥[2023-12-04]: Our evaluation server for test set is now availble on [EvalAI](https://eval.ai/web/challenges/challenge-page/2179/overview). We welcome all submissions and look forward to your participation! 😆** ## Dataset Details ### Dataset Description We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning. MMMU includes **11.5K meticulously collected multimodal questions** from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering. These questions span **30 subjects** and **183 subfields**, comprising **30 highly heterogeneous image types**, such as charts, diagrams, maps, tables, music sheets, and chemical structures. We believe MMMU will stimulate the community to build next-generation multimodal foundation models towards expert artificial general intelligence (AGI). 🎯 **We have released a full set comprising 150 development samples and 900 validation samples. We have released 10,500 test questions without their answers.** The development set is used for few-shot/in-context learning, and the validation set is used for debugging models, selecting hyperparameters, or quick evaluations. The answers and explanations for the test set questions are withheld. You can submit your model's predictions for the **test set** on **[EvalAI](https://eval.ai/web/challenges/challenge-page/2179/overview)**. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6230d750d93e84e233882dbc/2Ulh9yznm1dvISV4xJ_Ok.png) ### Dataset Creation MMMU was created to challenge multimodal models with tasks that demand college-level subject knowledge and deliberate reasoning, pushing the boundaries of what these models can achieve in terms of expert-level perception and reasoning. The data for the MMMU dataset was manually collected by a team of college students from various disciplines, using online sources, textbooks, and lecture materials. - **Content:** The dataset contains 11.5K college-level problems across six broad disciplines (Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, Tech & Engineering) and 30 college subjects. - **Image Types:** The dataset includes 30 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures, interleaved with text. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6230d750d93e84e233882dbc/Mbf8O5lEH8I8czprch0AG.png) ## 🏆 Mini-Leaderboard We show a mini-leaderboard here and please find more information in our paper or [**homepage**](https://mmmu-benchmark.github.io/). | Model | Val (900) | Test (10.5K) | |--------------------------------|:---------:|:------------:| | Expert (Best) | 88.6 | - | | Expert (Medium) | 82.6 | - | | Expert (Worst) | 76.2 | - | | GPT-4o* | **69.1** | - | | Gemini 1.5 Pro* | 62.2 | - | | InternVL2-Pro* | 62.0 | **55.7** | | Gemini 1.0 Ultra* | 59.4 | - | | Claude 3 Opus* | 59.4 | - | | GPT-4V(ision) (Playground) | 56.8 | **55.7** | | Reka Core* | 56.3 | - | | Gemini 1.5 Flash* | 56.1 | - | | SenseChat-Vision-0423-Preview* | 54.6 | 50.3 | | Reka Flash* | 53.3 | - | | Claude 3 Sonnet* | 53.1 | - | | HPT Pro* | 52.0 | - | | VILA1.5* | 51.9 | 46.9 | | Qwen-VL-MAX* | 51.4 | 46.8 | | InternVL-Chat-V1.2* | 51.6 | 46.2 | | Skywork-VL* | 51.4 | 46.2 | | LLaVA-1.6-34B* | 51.1 | 44.7 | | Claude 3 Haiku* | 50.2 | - | | Adept Fuyu-Heavy* | 48.3 | - | | Gemini 1.0 Pro* | 47.9 | - | | Marco-VL-Plus* | 46.2 | 44.3 | | Yi-VL-34B* | 45.9 | 41.6 | | Qwen-VL-PLUS* | 45.2 | 40.8 | | HPT Air* | 44.0 | - | | Reka Edge* | 42.8 | - | | Marco-VL* | 41.2 | 40.4 | | OmniLMM-12B* | 41.1 | 40.4 | | Bunny-8B* | 43.3 | 39.0 | | Bunny-4B* | 41.4 | 38.4 | | Weitu-VL-1.0-15B* | - | 38.4 | | InternLM-XComposer2-VL* | 43.0 | 38.2 | | Yi-VL-6B* | 39.1 | 37.8 | | InfiMM-Zephyr-7B* | 39.4 | 35.5 | | InternVL-Chat-V1.1* | 39.1 | 35.3 | | Math-LLaVA-13B* | 38.3 | 34.6 | | SVIT* | 38.0 | 34.1 | | MiniCPM-V* | 37.2 | 34.1 | | MiniCPM-V-2* | 37.1 | - | | Emu2-Chat* | 36.3 | 34.1 | | BLIP-2 FLAN-T5-XXL | 35.4 | 34.0 | | InstructBLIP-T5-XXL | 35.7 | 33.8 | | LLaVA-1.5-13B | 36.4 | 33.6 | | Bunny-3B* | 38.2 | 33.0 | | Qwen-VL-7B-Chat | 35.9 | 32.9 | | SPHINX* | 32.9 | 32.9 | | mPLUG-OWL2* | 32.7 | 32.1 | | BLIP-2 FLAN-T5-XL | 34.4 | 31.0 | | InstructBLIP-T5-XL | 32.9 | 30.6 | | Gemini Nano2* | 32.6 | - | | CogVLM | 32.1 | 30.1 | | Otter | 32.2 | 29.1 | | LLaMA-Adapter2-7B | 29.8 | 27.7 | | MiniGPT4-Vicuna-13B | 26.8 | 27.6 | | Adept Fuyu-8B | 27.9 | 27.4 | | Kosmos2 | 24.4 | 26.6 | | OpenFlamingo2-9B | 28.7 | 26.3 | | Frequent Choice | 22.1 | 23.9 | | Random Choice | 26.8 | 25.8 | *: results provided by the authors. ## Limitations Despite its comprehensive nature, MMMU, like any benchmark, is not without limitations. The manual curation process, albeit thorough, may carry biases. And the focus on college-level subjects might not fully be a sufficient test for Expert AGI. However, we believe it should be necessary for an Expert AGI to achieve strong performance on MMMU to demonstrate their broad and deep subject knowledge as well as expert-level understanding and reasoning capabilities. In future work, we plan to incorporate human evaluations into MMMU. This will provide a more grounded comparison between model capabilities and expert performance, shedding light on the proximity of current AI systems to achieving Expert AGI. ## Disclaimers The guidelines for the annotators emphasized strict compliance with copyright and licensing rules from the initial data source, specifically avoiding materials from websites that forbid copying and redistribution. Should you encounter any data samples potentially breaching the copyright or licensing regulations of any site, we encourage you to notify us. Upon verification, such samples will be promptly removed. ## Contact - Xiang Yue: [email protected] - Yu Su: [email protected] - Wenhu Chen: [email protected] ## Citation **BibTeX:** ```bibtex @inproceedings{yue2023mmmu, title={MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI}, author={Xiang Yue and Yuansheng Ni and Kai Zhang and Tianyu Zheng and Ruoqi Liu and Ge Zhang and Samuel Stevens and Dongfu Jiang and Weiming Ren and Yuxuan Sun and Cong Wei and Botao Yu and Ruibin Yuan and Renliang Sun and Ming Yin and Boyuan Zheng and Zhenzhu Yang and Yibo Liu and Wenhao Huang and Huan Sun and Yu Su and Wenhu Chen}, booktitle={Proceedings of CVPR}, year={2024}, } ```

IGNF/FLAIR_1_osm_clip

IGNF

2024-09-18T09:05:07Z

16,806

5

[ "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "IGN" ]

[]

2023-04-27T12:28:28Z

null

--- size_categories: - 10K<n<100K dataset_info: features: - name: image dtype: image - name: segmentation dtype: image - name: metadata struct: - name: address struct: - name: ISO3166-2-lvl4 dtype: string - name: ISO3166-2-lvl6 dtype: string - name: ISO3166-2-lvl7 dtype: string - name: aerialway dtype: string - name: aeroway dtype: string - name: allotments dtype: string - name: amenity dtype: string - name: bridge dtype: string - name: building dtype: string - name: city dtype: string - name: city_district dtype: string - name: club dtype: string - name: commercial dtype: string - name: country dtype: string - name: country_code dtype: string - name: county dtype: string - name: craft dtype: string - name: emergency dtype: string - name: farm dtype: string - name: farmyard dtype: string - name: hamlet dtype: string - name: healthcare dtype: string - name: highway dtype: string - name: historic dtype: string - name: house_number dtype: string - name: industrial dtype: string - name: isolated_dwelling dtype: string - name: junction dtype: string - name: landuse dtype: string - name: leisure dtype: string - name: locality dtype: string - name: man_made dtype: string - name: military dtype: string - name: municipality dtype: string - name: natural dtype: string - name: neighbourhood dtype: string - name: office dtype: string - name: place dtype: string - name: postcode dtype: string - name: province dtype: string - name: quarter dtype: string - name: railway dtype: string - name: region dtype: string - name: residential dtype: string - name: retail dtype: string - name: road dtype: string - name: shop dtype: string - name: square dtype: string - name: state dtype: string - name: state_district dtype: string - name: suburb dtype: string - name: tourism dtype: string - name: town dtype: string - name: village dtype: string - name: boundingbox sequence: string - name: camera dtype: string - name: code dtype: string - name: date dtype: string - name: dimensions sequence: int64 - name: display_name dtype: string - name: domain dtype: string - name: lat dtype: string - name: latlong sequence: float64 - name: licence dtype: string - name: lon dtype: string - name: origin sequence: float64 - name: osm_id dtype: int64 - name: osm_type dtype: string - name: patch_centroid_x dtype: float64 - name: patch_centroid_y dtype: float64 - name: patch_centroid_z dtype: float64 - name: place_id dtype: int64 - name: time dtype: string - name: unit_system dtype: string - name: zone dtype: string - name: class_rep struct: - name: class_repartition struct: - name: agricultural land dtype: float64 - name: bare soil dtype: float64 - name: brushwood dtype: float64 - name: building dtype: float64 - name: clear cut dtype: float64 - name: coniferous dtype: float64 - name: deciduous dtype: float64 - name: greenhouse dtype: float64 - name: herbaceous vegetation dtype: float64 - name: impervious surface dtype: float64 - name: ligneous dtype: float64 - name: mixed dtype: float64 - name: other dtype: float64 - name: pervious surface dtype: float64 - name: plowed land dtype: float64 - name: snow dtype: float64 - name: swimming pool dtype: float64 - name: vineyard dtype: float64 - name: water dtype: float64 - name: prompt dtype: string - name: clip_label dtype: string - name: clip_embeddings sequence: float32 splits: - name: train num_bytes: 26969627407.0 num_examples: 61712 download_size: 26975935912 dataset_size: 26969627407.0 tags: - IGN configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "FLAIR_OSM_CLIP" Dataset for the Seg2Sat model: https://github.com/RubenGres/Seg2Sat Derived from [FLAIR#1](https://huggingface.co/datasets/IGNF/FLAIR) train split. This dataset incudes the following features: - **image**: FLAIR#1 .tif files RBG bands converted into a more managable jpg format - **segmentation**: FLAIR#1 segmentation converted to JPG using the [LUT from the documentation](https://ignf.github.io/FLAIR/index_fr.html) - **metadata**: OSM metadata for the centroid of the image - **clip_label**: [CLIP ViT-H](https://github.com/openai/CLIP) description - **class_rep**: ratio of appearance of each class in the segmentation - **prompt**: Prompt used for the Seg2Sat model - **clip_embeddings**: Embedding using openai/clip-vit-base-patch32 model

Prithvi-raj-singh/pricer-data

Prithvi-raj-singh

2024-09-13T12:36:06Z

0

[ "region:us" ]

[]

2025-05-02T10:26:28Z

null

--- dataset_info: features: - name: text dtype: string - name: price dtype: float64 splits: - name: train num_bytes: 313746593 num_examples: 400000 - name: test num_bytes: 1558981 num_examples: 2000 download_size: 185915273 dataset_size: 315305574 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

sail/regmix-data

sail

2024-09-12T08:12:18Z

24,231

4

[ "language:en", "license:mit", "size_categories:10M<n<100M", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "arxiv:2407.01492", "region:us", "regmix" ]

[]

2024-07-02T13:23:56Z

null

--- license: mit language: - en tags: - regmix pretty_name: regmix-data size_categories: - 10M<n<100M --- # RegMix Data ## Dataset Description The RegMix Data is a curated dataset derived from the Pile-Uncopyrighted, specifically designed for the RegMix paper (https://huggingface.co/papers/2407.01492). This dataset aims to facilitate the automatic identification of high-performing data mixtures for language model pre-training by formulating it as a regression task. ### Key Features: - Size: Approximately 1TB disk space, 250B tokens - Distribution: Follows the natural token distribution of domain examples - Organization: Examples from different domains are separated into individual files ## Dataset Structure The dataset is organized into two main directories: `train` and `valid`, each containing domain-specific JSONL files. The file naming convention is as follows: ``` [domain]-[identifier]-[number].jsonl ``` For example: `arxiv-10-74305611.jsonl` ### Domains Included: arxiv, gutenberg_pg_19, pubmed_central, dm_mathematics, hackernews, stackexchange, enron_emails, nih_exporter, ubuntu_irc, europarl, philpapers, uspto_backgrounds, freelaw, pile_cc, wikipedia_en, github, pubmed_abstracts ## Usage We recommend downloading the entire dataset snapshot instead of using the traditional `load_dataset` function, as the RegMix code is integrated with the [TinyLlama framework](https://github.com/jzhang38/TinyLlama). To download the dataset: ```python from huggingface_hub import snapshot_download LOCAL_DIR = "regmix-data" snapshot_download(repo_id="sail/regmix-data", repo_type='dataset', local_dir=LOCAL_DIR, local_dir_use_symlinks=False) ``` This will download the entire snapshot, containing 34 JSON line files (17 for train, and 17 for valid), to your specified local directory. ## Data Preprocessing Our [code](https://github.com/sail-sg/regmix) will preprocess these domain files into binary format with domain prefixes. It allows for random sampling of the dataset using user-defined data mixtures (i.e., domain weights). ## Acknowledgements We extend our gratitude to the creators of the [Pile-Uncopyrighted dataset](https://huggingface.co/datasets/monology/pile-uncopyrighted) for their efforts in removing copyrighted content from the original Pile dataset, making this work possible. ## Citation If you use this dataset in your research, please cite the RegMix paper: ``` @article{liu2024regmix, title={RegMix: Data Mixture as Regression for Language Model Pre-training}, author={Liu, Qian and Zheng, Xiaosen and Muennighoff, Niklas and Zeng, Guangtao and Dou, Longxu and Pang, Tianyu and Jiang, Jing and Lin, Min}, journal={arXiv preprint arXiv:2407.01492}, year={2024} } ``` For more information about the RegMix methodology and its applications, please refer to the [original paper](https://huggingface.co/papers/2407.01492).

LIUM/tedlium

LIUM

2024-09-10T18:27:41Z

1,048

25

[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "size_categories:10K<n<100K", "region:us" ]

[ "automatic-speech-recognition" ]

2022-05-11T12:47:06Z

1

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: [] multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - automatic-speech-recognition task_ids: [] pretty_name: TED-LIUM --- # Dataset Card for tedlium ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** [TED-LIUM homepage](https://www.openslr.org/7/) - **Repository:** [Needs More Information] - **Paper:** [TED-LIUM: an Automatic Speech Recognition dedicated corpus](https://aclanthology.org/L12-1405/) - **Leaderboard:** [Paperswithcode Leaderboard](https://paperswithcode.com/sota/speech-recognition-on-tedlium) - **Point of Contact:** [Sanchit Gandhi](mailto:[email protected]) ### Dataset Summary The TED-LIUM corpus is English-language TED talks, with transcriptions, sampled at 16kHz. The three releases of the corpus range from 118 to 452 hours of transcribed speech data. ### Example ```python from datasets import load_dataset tedlium = load_dataset("LIUM/tedlium", "release1") # for Release 1 # see structure print(tedlium) # load audio sample on the fly audio_input = tedlium["train"][0]["audio"] # first decoded audio sample transcription = tedlium["train"][0]["text"] # first transcription ``` ### Supported Tasks and Leaderboards - `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://paperswithcode.com/sota/speech-recognition-on-tedlium that ranks models based on their WER. ### Languages The audio and transcriptions are in English, as per the TED talks at http://www.ted.com. ## Dataset Structure ### Data Instances ``` {'audio': {'path': '/home/sanchitgandhi/cache/downloads/extracted/6e3655f9e735ae3c467deed1df788e0dabd671c1f3e2e386e30aa3b571bd9761/TEDLIUM_release1/train/sph/PaulaScher_2008P.sph', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'text': '{COUGH} but <sil> i was so {COUGH} utterly unqualified for(2) this project and {NOISE} so utterly ridiculous {SMACK} and ignored the brief {SMACK} <sil>', 'speaker_id': 'PaulaScher_2008P', 'gender': 'female', 'file': '/home/sanchitgandhi/cache/downloads/extracted/6e3655f9e735ae3c467deed1df788e0dabd671c1f3e2e386e30aa3b571bd9761/TEDLIUM_release1/train/sph/PaulaScher_2008P.sph', 'id': 'PaulaScher_2008P-1003.35-1011.16-<o,f0,female>'} ``` ### Data Fields - audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. - file: A path to the downloaded audio file in .sph format. - text: the transcription of the audio file. - gender: the gender of the speaker. One of: male, female or N/A. - id: unique id of the data sample. - speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples. ### Data Splits There are three releases for the TED-LIUM corpus, progressively increasing the number of transcribed speech training data from 118 hours (Release 1), to 207 hours (Release 2), to 452 hours (Release 3). Release 1: - 774 audio talks and automatically aligned transcriptions. - Contains 118 hours of speech audio data. - Homepage: https://www.openslr.org/7/ Release 2: - 1495 audio talks and automatically aligned transcriptions. - Contains 207 hours of speech audio data. - Dictionary with pronunciations (159848 entries). - Selected monolingual data for language modeling from WMT12 publicly available corpora. - Homepage: https://www.openslr.org/19/ Release 3: - 2351 audio talks and automatically aligned transcriptions. - Contains 452 hours of speech audio data. - TED-LIUM 2 validation and test data: 19 TED talks with their corresponding manual transcriptions. - Dictionary with pronunciations (159848 entries), the same file as the one included in TED-LIUM 2. - Selected monolingual data for language modeling from WMT12 publicly available corpora: these files come from the TED-LIUM 2 release, but have been modified to produce a tokenization more relevant for English language. - Homepage: https://www.openslr.org/51/ Release 3 contains two different corpus distributions: - The ‘legacy’ one, on which the dev and test datasets are the same as in TED-LIUM 2 (and TED-LIUM 1). - The ‘speaker adaptation’ one, specially designed for experiments on speaker adaptation. Each release is split into a training, validation and test set: | Split | Release 1 | Release 2 | Release 3 | |------------|-----------|-----------|-----------| | Train | 56,803 | 92,973 | 268,263 | | Validation | 591 | 591 | 591 | | Test | 1,469 | 1,469 | 1,469 | ## Dataset Creation ### Curation Rationale TED-LIUM was built during [The International Workshop on Spoken Language Trans- lation (IWSLT) 2011 Evaluation Campaign](https://aclanthology.org/2011.iwslt-evaluation.1/), an annual workshop focused on the automatic translation of public talks and included tracks for speech recognition, speech translation, text translation, and system combination. ### Source Data #### Initial Data Collection and Normalization The data was obtained from publicly available TED talks at http://www.ted.com. Proper alignments between the speech and the transcribed text were generated using an in-house speaker segmentation and clustering tool (_LIUM_SpkDiarization_). Speech disfluencies (e.g. repetitions, hesitations, false starts) were treated in the following way: repetitions were transcribed, hesitations mapped to a specific filler word, and false starts not taken into account. For full details on the data collection and processing, refer to the [TED-LIUM paper](https://aclanthology.org/L12-1405/). #### Who are the source language producers? TED Talks are influential videos from expert speakers on education, business, science, tech and creativity. ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information Licensed under Creative Commons BY-NC-ND 3.0 (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en). ### Citation Information Release 1: ``` @inproceedings{rousseau2012tedlium, title={TED-LIUM: an Automatic Speech Recognition dedicated corpus}, author={Rousseau, Anthony and Del{\'e}glise, Paul and Est{\`e}ve, Yannick}, booktitle={Conference on Language Resources and Evaluation (LREC)}, pages={125--129}, year={2012} } ``` Release 2: ``` @inproceedings{rousseau2014enhancing, title={Enhancing the TED-LIUM corpus with selected data for language modeling and more TED talks.}, author={Rousseau, Anthony and Del{\'e}glise, Paul and Esteve, Yannick and others}, booktitle={LREC}, pages={3935--3939}, year={2014} } ``` Release 3: ``` @inproceedings{hernandez2018ted, author="Hernandez, Fran{\c{c}}ois and Nguyen, Vincent and Ghannay, Sahar and Tomashenko, Natalia and Est{\`e}ve, Yannick", title="TED-LIUM 3: Twice as Much Data and Corpus Repartition for Experiments on Speaker Adaptation", booktitle="Speech and Computer", year="2018", publisher="Springer International Publishing", pages="198--208", } ```

google/xtreme_s

google

2024-09-10T13:12:26Z

2,557

62

[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "annotations_creators:crowdsourced", "annotations_creators:machine-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "source_datasets:extended|multilingual_librispeech", "source_datasets:extended|covost2", "language:afr", "language:amh", "language:ara", "language:asm", "language:ast", "language:azj", "language:bel", "language:ben", "language:bos", "language:cat", "language:ceb", "language:cmn", "language:ces", "language:cym", "language:dan", "language:deu", "language:ell", "language:eng", "language:spa", "language:est", "language:fas", "language:ful", "language:fin", "language:tgl", "language:fra", "language:gle", "language:glg", "language:guj", "language:hau", "language:heb", "language:hin", "language:hrv", "language:hun", "language:hye", "language:ind", "language:ibo", "language:isl", "language:ita", "language:jpn", "language:jav", "language:kat", "language:kam", "language:kea", "language:kaz", "language:khm", "language:kan", "language:kor", "language:ckb", "language:kir", "language:ltz", "language:lug", "language:lin", "language:lao", "language:lit", "language:luo", "language:lav", "language:mri", "language:mkd", "language:mal", "language:mon", "language:mar", "language:msa", "language:mlt", "language:mya", "language:nob", "language:npi", "language:nld", "language:nso", "language:nya", "language:oci", "language:orm", "language:ory", "language:pan", "language:pol", "language:pus", "language:por", "language:ron", "language:rus", "language:bul", "language:snd", "language:slk", "language:slv", "language:sna", "language:som", "language:srp", "language:swe", "language:swh", "language:tam", "language:tel", "language:tgk", "language:tha", "language:tur", "language:ukr", "language:umb", "language:urd", "language:uzb", "language:vie", "language:wol", "language:xho", "language:yor", "language:yue", "language:zul", "license:cc-by-4.0", "size_categories:10K<n<100K", "arxiv:2203.10752", "arxiv:2205.12446", "arxiv:2007.10310", "region:us", "speech-recognition" ]

[ "automatic-speech-recognition" ]

2022-03-04T14:10:40Z

1

--- annotations_creators: - expert-generated - crowdsourced - machine-generated language_creators: - crowdsourced - expert-generated language: - afr - amh - ara - asm - ast - azj - bel - ben - bos - cat - ceb - cmn - ces - cym - dan - deu - ell - eng - spa - est - fas - ful - fin - tgl - fra - gle - glg - guj - hau - heb - hin - hrv - hun - hye - ind - ibo - isl - ita - jpn - jav - kat - kam - kea - kaz - khm - kan - kor - ckb - kir - ltz - lug - lin - lao - lit - luo - lav - mri - mkd - mal - mon - mar - msa - mlt - mya - nob - npi - nld - nso - nya - oci - orm - ory - pan - pol - pus - por - ron - rus - bul - snd - slk - slv - sna - som - srp - swe - swh - tam - tel - tgk - tha - tur - ukr - umb - urd - uzb - vie - wol - xho - yor - yue - zul license: - cc-by-4.0 multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - extended|multilingual_librispeech - extended|covost2 task_categories: - automatic-speech-recognition task_ids: [] paperswithcode_id: librispeech-1 pretty_name: 'The Cross-lingual TRansfer Evaluation of Multilingual Encoders for Speech (XTREME-S) benchmark is a benchmark designed to evaluate speech representations across languages, tasks, domains and data regimes. It covers 102 languages from 10+ language families, 3 different domains and 4 task families: speech recognition, translation, classification and retrieval.' tags: - speech-recognition --- # XTREME-S ## Dataset Description - **Fine-Tuning script:** [research-projects/xtreme-s](https://github.com/huggingface/transformers/tree/master/examples/research_projects/xtreme-s) - **Paper:** [XTREME-S: Evaluating Cross-lingual Speech Representations](https://arxiv.org/abs/2203.10752) - **Leaderboard:** [TODO(PVP)]() - **FLEURS amount of disk used:** 350 GB - **Multilingual Librispeech amount of disk used:** 2700 GB - **Voxpopuli amount of disk used:** 400 GB - **Covost2 amount of disk used:** 70 GB - **Minds14 amount of disk used:** 5 GB - **Total amount of disk used:** ca. 3500 GB The Cross-lingual TRansfer Evaluation of Multilingual Encoders for Speech (XTREME-S) benchmark is a benchmark designed to evaluate speech representations across languages, tasks, domains and data regimes. It covers 102 languages from 10+ language families, 3 different domains and 4 task families: speech recognition, translation, classification and retrieval. ***TLDR; XTREME-S is the first speech benchmark that is both diverse, fully accessible, and reproducible. All datasets can be downloaded with a single line of code. An easy-to-use and flexible fine-tuning script is provided and actively maintained.*** XTREME-S covers speech recognition with Fleurs, Multilingual LibriSpeech (MLS) and VoxPopuli, speech translation with CoVoST-2, speech classification with LangID (Fleurs) and intent classification (MInds-14) and finally speech(-text) retrieval with Fleurs. Each of the tasks covers a subset of the 102 languages included in XTREME-S, from various regions: - **Western Europe**: *Asturian, Bosnian, Catalan, Croatian, Danish, Dutch, English, Finnish, French, Galician, German, Greek, Hungarian, Icelandic, Irish, Italian, Kabuverdianu, Luxembourgish, Maltese, Norwegian, Occitan, Portuguese, Spanish, Swedish, Welsh* - **Eastern Europe**: *Armenian, Belarusian, Bulgarian, Czech, Estonian, Georgian, Latvian, Lithuanian, Macedonian, Polish, Romanian, Russian, Serbian, Slovak, Slovenian, Ukrainian* - **Central-Asia/Middle-East/North-Africa**: *Arabic, Azerbaijani, Hebrew, Kazakh, Kyrgyz, Mongolian, Pashto, Persian, Sorani-Kurdish, Tajik, Turkish, Uzbek* - **Sub-Saharan Africa**: *Afrikaans, Amharic, Fula, Ganda, Hausa, Igbo, Kamba, Lingala, Luo, Northern-Sotho, Nyanja, Oromo, Shona, Somali, Swahili, Umbundu, Wolof, Xhosa, Yoruba, Zulu* - **South-Asia**: *Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali, Oriya, Punjabi, Sindhi, Tamil, Telugu, Urdu* - **South-East Asia**: *Burmese, Cebuano, Filipino, Indonesian, Javanese, Khmer, Lao, Malay, Maori, Thai, Vietnamese* - **CJK languages**: *Cantonese and Mandarin Chinese, Japanese, Korean* ## Design principles ### Diversity XTREME-S aims for task, domain and language diversity. Tasks should be diverse and cover several domains to provide a reliable evaluation of model generalization and robustness to noisy naturally-occurring speech in different environments. Languages should be diverse to ensure that models can adapt to a wide range of linguistic and phonological phenomena. ### Accessibility The sub-dataset for each task can be downloaded with a **single line of code** as shown in [Supported Tasks](#supported-tasks). Each task is available under a permissive license that allows the use and redistribution of the data for research purposes. Tasks have been selected based on their usage by pre-existing multilingual pre-trained models, for simplicity. ### Reproducibility We produce fully **open-sourced, maintained and easy-to-use** fine-tuning scripts for each task as shown under [Fine-tuning Example](#fine-tuning-and-evaluation-example). XTREME-S encourages submissions that leverage publicly available speech and text datasets. Users should detail which data they use. In general, we encourage settings that can be reproduced by the community, but also encourage the exploration of new frontiers for speech representation learning. ## Fine-tuning and Evaluation Example We provide a fine-tuning script under [**research-projects/xtreme-s**](https://github.com/huggingface/transformers/tree/master/examples/research_projects/xtreme-s). The fine-tuning script is written in PyTorch and allows one to fine-tune and evaluate any [Hugging Face model](https://huggingface.co/models) on XTREME-S. The example script is actively maintained by [@anton-l](https://github.com/anton-l) and [@patrickvonplaten](https://github.com/patrickvonplaten). Feel free to reach out via issues or pull requests on GitHub if you have any questions. ## Leaderboards The leaderboard for the XTREME-S benchmark can be found at [this address (TODO(PVP))](). ## Supported Tasks Note that the suppoprted tasks are focused particularly on linguistic aspect of speech, while nonlinguistic/paralinguistic aspects of speech relevant to e.g. speech synthesis or voice conversion are **not** evaluated. <p align="center"> <img src="https://github.com/patrickvonplaten/scientific_images/raw/master/xtreme_s.png" alt="Datasets used in XTREME"/> </p> ### 1. Speech Recognition (ASR) We include three speech recognition datasets: FLEURS-ASR, MLS and VoxPopuli (optionally BABEL). Multilingual fine-tuning is used for these three datasets. #### FLEURS-ASR *FLEURS-ASR* is the speech version of the FLORES machine translation benchmark, covering 2000 n-way parallel sentences in n=102 languages. ```py from datasets import load_dataset fleurs_asr = load_dataset("google/xtreme_s", "fleurs.af_za") # for Afrikaans # to download all data for multi-lingual fine-tuning uncomment following line # fleurs_asr = load_dataset("google/xtreme_s", "fleurs.all") # see structure print(fleurs_asr) # load audio sample on the fly audio_input = fleurs_asr["train"][0]["audio"] # first decoded audio sample transcription = fleurs_asr["train"][0]["transcription"] # first transcription # use `audio_input` and `transcription` to fine-tune your model for ASR # for analyses see language groups all_language_groups = fleurs_asr["train"].features["lang_group_id"].names lang_group_id = fleurs_asr["train"][0]["lang_group_id"] all_language_groups[lang_group_id] ``` #### Multilingual LibriSpeech (MLS) *MLS* is a large multilingual corpus derived from read audiobooks from LibriVox and consists of 8 languages. For this challenge the training data is limited to 10-hours splits. ```py from datasets import load_dataset mls = load_dataset("google/xtreme_s", "mls.pl") # for Polish # to download all data for multi-lingual fine-tuning uncomment following line # mls = load_dataset("google/xtreme_s", "mls.all") # see structure print(mls) # load audio sample on the fly audio_input = mls["train"][0]["audio"] # first decoded audio sample transcription = mls["train"][0]["transcription"] # first transcription # use `audio_input` and `transcription` to fine-tune your model for ASR ``` #### VoxPopuli *VoxPopuli* is a large-scale multilingual speech corpus for representation learning and semi-supervised learning, from which we use the speech recognition dataset. The raw data is collected from 2009-2020 European Parliament event recordings. We acknowledge the European Parliament for creating and sharing these materials. **VoxPopuli has to download the whole dataset 100GB since languages are entangled into each other - maybe not worth testing here due to the size** ```py from datasets import load_dataset voxpopuli = load_dataset("google/xtreme_s", "voxpopuli.ro") # for Romanian # to download all data for multi-lingual fine-tuning uncomment following line # voxpopuli = load_dataset("google/xtreme_s", "voxpopuli.all") # see structure print(voxpopuli) # load audio sample on the fly audio_input = voxpopuli["train"][0]["audio"] # first decoded audio sample transcription = voxpopuli["train"][0]["transcription"] # first transcription # use `audio_input` and `transcription` to fine-tune your model for ASR ``` #### (Optionally) BABEL *BABEL* from IARPA is a conversational speech recognition dataset in low-resource languages. First, download LDC2016S06, LDC2016S12, LDC2017S08, LDC2017S05 and LDC2016S13. BABEL is the only dataset in our benchmark who is less easily accessible, so you will need to sign in to get access to it on LDC. Although not officially part of the XTREME-S ASR datasets, BABEL is often used for evaluating speech representations on a difficult domain (phone conversations). ```py from datasets import load_dataset babel = load_dataset("google/xtreme_s", "babel.as") ``` **The above command is expected to fail with a nice error message, explaining how to download BABEL** The following should work: ```py from datasets import load_dataset babel = load_dataset("google/xtreme_s", "babel.as", data_dir="/path/to/IARPA_BABEL_OP1_102_LDC2016S06.zip") # see structure print(babel) # load audio sample on the fly audio_input = babel["train"][0]["audio"] # first decoded audio sample transcription = babel["train"][0]["transcription"] # first transcription # use `audio_input` and `transcription` to fine-tune your model for ASR ``` ### 2. Speech Translation (ST) We include the CoVoST-2 dataset for automatic speech translation. #### CoVoST-2 The *CoVoST-2* benchmark has become a commonly used dataset for evaluating automatic speech translation. It covers language pairs from English into 15 languages, as well as 21 languages into English. We use only the "X->En" direction to evaluate cross-lingual representations. The amount of supervision varies greatly in this setting, from one hour for Japanese->English to 180 hours for French->English. This makes pretraining particularly useful to enable such few-shot learning. We enforce multiligual fine-tuning for simplicity. Results are splitted in high/med/low-resource language pairs as explained in the [paper (TODO(PVP))]. ```py from datasets import load_dataset covost_2 = load_dataset("google/xtreme_s", "covost2.id.en") # for Indonesian to English # to download all data for multi-lingual fine-tuning uncomment following line # covost_2 = load_dataset("google/xtreme_s", "covost2.all") # see structure print(covost_2) # load audio sample on the fly audio_input = covost_2["train"][0]["audio"] # first decoded audio sample transcription = covost_2["train"][0]["transcription"] # first transcription translation = covost_2["train"][0]["translation"] # first translation # use audio_input and translation to fine-tune your model for AST ``` ### 3. Speech Classification We include two multilingual speech classification datasets: FLEURS-LangID and Minds-14. #### Language Identification - FLEURS-LangID LangID can often be a domain classification, but in the case of FLEURS-LangID, recordings are done in a similar setting across languages and the utterances correspond to n-way parallel sentences, in the exact same domain, making this task particularly relevant for evaluating LangID. The setting is simple, FLEURS-LangID is splitted in train/valid/test for each language. We simply create a single train/valid/test for LangID by merging all. ```py from datasets import load_dataset fleurs_langID = load_dataset("google/xtreme_s", "fleurs.all") # to download all data # see structure print(fleurs_langID) # load audio sample on the fly audio_input = fleurs_langID["train"][0]["audio"] # first decoded audio sample language_class = fleurs_langID["train"][0]["lang_id"] # first id class language = fleurs_langID["train"].features["lang_id"].names[language_class] # use audio_input and language_class to fine-tune your model for audio classification ``` #### Intent classification - Minds-14 Minds-14 is an intent classification made from e-banking speech datasets in 14 languages, with 14 intent labels. We impose a single multilingual fine-tuning to increase the size of the train and test sets and reduce the variance associated with the small size of the dataset per language. ```py from datasets import load_dataset minds_14 = load_dataset("google/xtreme_s", "minds14.fr-FR") # for French # to download all data for multi-lingual fine-tuning uncomment following line # minds_14 = load_dataset("google/xtreme_s", "minds14.all") # see structure print(minds_14) # load audio sample on the fly audio_input = minds_14["train"][0]["audio"] # first decoded audio sample intent_class = minds_14["train"][0]["intent_class"] # first transcription intent = minds_14["train"].features["intent_class"].names[intent_class] # use audio_input and language_class to fine-tune your model for audio classification ``` ### 4. (Optionally) Speech Retrieval We optionally include one speech retrieval dataset: FLEURS-Retrieval as explained in the [FLEURS paper](https://arxiv.org/abs/2205.12446). #### FLEURS-Retrieval FLEURS-Retrieval provides n-way parallel speech and text data. Similar to how XTREME for text leverages Tatoeba to evaluate bitext mining a.k.a sentence translation retrieval, we use FLEURS-Retrieval to evaluate the quality of fixed-size representations of speech utterances. Our goal is to incentivize the creation of fixed-size speech encoder for speech retrieval. The system has to retrieve the English "key" utterance corresponding to the speech translation of "queries" in 15 languages. Results have to be reported on the test sets of FLEURS-Retrieval whose utterances are used as queries (and keys for English). We augment the English keys with a large number of utterances to make the task more difficult. ```py from datasets import load_dataset fleurs_retrieval = load_dataset("google/xtreme_s", "fleurs.af_za") # for Afrikaans # to download all data for multi-lingual fine-tuning uncomment following line # fleurs_retrieval = load_dataset("google/xtreme_s", "fleurs.all") # see structure print(fleurs_retrieval) # load audio sample on the fly audio_input = fleurs_retrieval["train"][0]["audio"] # decoded audio sample text_sample_pos = fleurs_retrieval["train"][0]["transcription"] # positive text sample text_sample_neg = fleurs_retrieval["train"][1:20]["transcription"] # negative text samples # use `audio_input`, `text_sample_pos`, and `text_sample_neg` to fine-tune your model for retrieval ``` Users can leverage the training (and dev) sets of FLEURS-Retrieval with a ranking loss to build better cross-lingual fixed-size representations of speech. ## Dataset Structure The XTREME-S benchmark is composed of the following datasets: - [FLEURS](https://huggingface.co/datasets/google/fleurs#dataset-structure) - [Multilingual Librispeech (MLS)](https://huggingface.co/datasets/facebook/multilingual_librispeech#dataset-structure) Note that for MLS, XTREME-S uses `path` instead of `file` and `transcription` instead of `text`. - [Voxpopuli](https://huggingface.co/datasets/facebook/voxpopuli#dataset-structure) - [Minds14](https://huggingface.co/datasets/polyai/minds14#dataset-structure) - [Covost2](https://huggingface.co/datasets/covost2#dataset-structure) Note that for Covost2, XTREME-S uses `path` instead of `file` and `transcription` instead of `sentence`. - [BABEL](https://huggingface.co/datasets/ldc/iarpa_babel#dataset-structure) Please click on the link of the dataset cards to get more information about its dataset structure. ## Dataset Creation The XTREME-S benchmark is composed of the following datasets: - [FLEURS](https://huggingface.co/datasets/google/fleurs#dataset-creation) - [Multilingual Librispeech (MLS)](https://huggingface.co/datasets/facebook/multilingual_librispeech#dataset-creation) - [Voxpopuli](https://huggingface.co/datasets/facebook/voxpopuli#dataset-creation) - [Minds14](https://huggingface.co/datasets/polyai/minds14#dataset-creation) - [Covost2](https://huggingface.co/datasets/covost2#dataset-creation) - [BABEL](https://huggingface.co/datasets/ldc/iarpa_babel#dataset-creation) Please visit the corresponding dataset cards to get more information about the source data. ## Considerations for Using the Data ### Social Impact of Dataset This dataset is meant to encourage the development of speech technology in a lot more languages of the world. One of the goal is to give equal access to technologies like speech recognition or speech translation to everyone, meaning better dubbing or better access to content from the internet (like podcasts, streaming or videos). ### Discussion of Biases Most datasets have a fair distribution of gender utterances (e.g. the newly introduced FLEURS dataset). While many languages are covered from various regions of the world, the benchmark misses many languages that are all equally important. We believe technology built through XTREME-S should generalize to all languages. ### Other Known Limitations The benchmark has a particular focus on read-speech because common evaluation benchmarks like CoVoST-2 or LibriSpeech evaluate on this type of speech. There is sometimes a known mismatch between performance obtained in a read-speech setting and a more noisy setting (in production for instance). Given the big progress that remains to be made on many languages, we believe better performance on XTREME-S should still correlate well with actual progress made for speech understanding. ## Additional Information All datasets are licensed under the [Creative Commons license (CC-BY)](https://creativecommons.org/licenses/). ### Citation Information #### XTREME-S ``` @article{conneau2022xtreme, title={XTREME-S: Evaluating Cross-lingual Speech Representations}, author={Conneau, Alexis and Bapna, Ankur and Zhang, Yu and Ma, Min and von Platen, Patrick and Lozhkov, Anton and Cherry, Colin and Jia, Ye and Rivera, Clara and Kale, Mihir and others}, journal={arXiv preprint arXiv:2203.10752}, year={2022} } ``` #### MLS ``` @article{Pratap2020MLSAL, title={MLS: A Large-Scale Multilingual Dataset for Speech Research}, author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert}, journal={ArXiv}, year={2020}, volume={abs/2012.03411} } ``` #### VoxPopuli ``` @article{wang2021voxpopuli, title={Voxpopuli: A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation}, author={Wang, Changhan and Riviere, Morgane and Lee, Ann and Wu, Anne and Talnikar, Chaitanya and Haziza, Daniel and Williamson, Mary and Pino, Juan and Dupoux, Emmanuel}, journal={arXiv preprint arXiv:2101.00390}, year={2021} } ``` #### CoVoST 2 ``` @article{DBLP:journals/corr/abs-2007-10310, author = {Changhan Wang and Anne Wu and Juan Miguel Pino}, title = {CoVoST 2: {A} Massively Multilingual Speech-to-Text Translation Corpus}, journal = {CoRR}, volume = {abs/2007.10310}, year = {2020}, url = {https://arxiv.org/abs/2007.10310}, eprinttype = {arXiv}, eprint = {2007.10310}, timestamp = {Thu, 12 Aug 2021 15:37:06 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2007-10310.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` #### Minds14 ``` @article{gerz2021multilingual, title={Multilingual and cross-lingual intent detection from spoken data}, author={Gerz, Daniela and Su, Pei-Hao and Kusztos, Razvan and Mondal, Avishek and Lis, Micha{\l} and Singhal, Eshan and Mrk{\v{s}}i{\'c}, Nikola and Wen, Tsung-Hsien and Vuli{\'c}, Ivan}, journal={arXiv preprint arXiv:2104.08524}, year={2021} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@anton-l](https://github.com/anton-l), [@aconneau](https://github.com/aconneau) for adding this dataset

allenai/reward-bench

allenai

2024-09-09T16:11:45Z

7,408

94

[ "task_categories:question-answering", "language:en", "license:odc-by", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2403.13787", "arxiv:2310.07641", "arxiv:2308.01263", "arxiv:2308.07124", "doi:10.57967/hf/2457", "region:us" ]

[ "question-answering" ]

2023-12-14T18:38:07Z

null

--- language: - en license: odc-by size_categories: - 1K<n<10K task_categories: - question-answering pretty_name: RM Bench dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: chosen_model dtype: string - name: rejected dtype: string - name: rejected_model dtype: string - name: subset dtype: string - name: id dtype: int64 splits: - name: raw num_bytes: 10837043 num_examples: 5123 - name: filtered num_bytes: 4849207 num_examples: 2985 download_size: 7943951 dataset_size: 15686250 configs: - config_name: default data_files: - split: raw path: data/raw-* - split: filtered path: data/filtered-* --- <img src="https://huggingface.co/spaces/allenai/reward-bench/resolve/main/src/logo.png" alt="RewardBench Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> [Code](https://github.com/allenai/reward-bench) | [Leaderboard](https://huggingface.co/spaces/allenai/reward-bench) | [Prior Preference Sets](https://huggingface.co/datasets/allenai/pref-test-sets) | [Results](https://huggingface.co/datasets/allenai/reward-bench-results) | [Paper](https://arxiv.org/abs/2403.13787) # Reward Bench Evaluation Dataset Card The RewardBench evaluation dataset evaluates capabilities of reward models over the following categories: 1. **Chat**: Includes the easy chat subsets (alpacaeval-easy, alpacaeval-length, alpacaeval-hard, mt-bench-easy, mt-bench-medium) 2. **Chat Hard**: Includes the hard chat subsets (mt-bench-hard, llmbar-natural, llmbar-adver-neighbor, llmbar-adver-GPTInst, llmbar-adver-GPTOut, llmbar-adver-manual) 3. **Safety**: Includes the safety subsets (refusals-dangerous, refusals-offensive, xstest-should-refuse, xstest-should-respond, do not answer) 4. **Reasoning**: Includes the code and math subsets (math-prm, hep-cpp, hep-go, hep-java, hep-js, hep-python, hep-rust) The RewardBench leaderboard averages over these subsets and a final category from [prior preference data test sets](https://huggingface.co/datasets/allenai/preference-test-sets) including Anthropic Helpful, Anthropic HHH in BIG-Bench, Stanford Human Preferences (SHP), and OpenAI's Learning to Summarize data. The scoring for RewardBench compares the score of a prompt-chosen pair to a prompt-rejected pair. Success is when the chosen score is higher than rejected. <img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/reward-bench/scoring.png" alt="RewardBench Scoring" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> In order to create a representative, single evaluation score, we perform a limited mixture of averaging across results. For all the subsets detailed below except for Reasoning, we perform per-prompt weighted averaging across all the prompts in the subset to get the section score. For example, in Chat we take a weighted average of the AlpacaEval and MT Bench sets based on the number of prompts. For Reasoning, we increase the weight of the PRM-Math subset so code and math abilities are weighed equally in the final number, rather than increasing the relevance of code. Once all subsets weighted averages are achieved, the final RewardBench score is the average across the subset scores (including Prior Sets). ## Dataset Details In order to maintain all the relevant data, the samples in the dataset will have the following items. Note, the dataset is single-turn: * `prompt` (`str`): the instruction given in the various test sets. * `chosen` (`str`): the response from the better model or the better rated prompt. * `chosen_model` (`str`): where applicable * `rejected` (`str`): the response with the lower score or from word model. * `rejected_model` (`str`): where applicable * `subset` (`str`): the subset (e.g. alpacaeval-easy) of the associated prompt as the dataset is all in one split. * `id` (`int`): an incremented id for every prompt in the benchmark. To select a specific subset use HuggingFace Datasets `.filter` functionality. ``` dataset = dataset.filter(lambda ex: ex["subset"] == "alpacaeval-easy") ``` This can easily be converted to the standard chosen/rejected list of messages format (see [UltraFeedback for an example](https://huggingface.co/datasets/allenai/ultrafeedback_binarized_cleaned)), for example with our data loading utilities on [GitHub](https://github.com/allenai/reward-bench/blob/8eadb09397d58f1930d4f77938e618b9f9b8aeb3/rewardbench/utils.py#L330). ### Subset Summary Total number of the prompts is: 2985. | Subset | Num. Samples (Pre-filtering, post-filtering) | Description | | :---------- | :-----: | :---------: | | alpacaeval-easy | 805, 100 | Great model vs poor model; GPT4-Turbo 97.7% v. Alpaca 7b 26.46% (data [here](https://github.com/tatsu-lab/alpaca_eval/tree/main/results)) | | alpacaeval-length | 805, 95 | Good model vs low model, similar length; Llama2chat 70B 92.66% vs Guanaco 13B 52.61% (data [here](https://github.com/tatsu-lab/alpaca_eval/tree/main/results)) | | alpacaeval-hard | 805, 95 | Great model vs baseline model; Tulu 2 95.0% v. Davinici003 50.0% (data [here](https://github.com/tatsu-lab/alpaca_eval/tree/main/results))| | mt-bench-easy | 28, 28 | MT Bench 10s vs 1s (source [data](https://huggingface.co/spaces/lmsys/mt-bench/tree/main/data/mt_bench)) | | mt-bench-medium | 45, 40 | MT Bench 9s vs 2-5s (source [data](https://huggingface.co/spaces/lmsys/mt-bench/tree/main/data/mt_bench)) | | mt-bench-hard | 45, 37 | MT Bench 7-8 vs 5-6 (source [data](https://huggingface.co/spaces/lmsys/mt-bench/tree/main/data/mt_bench)) | | refusals-dangerous | 505, 100 | Dangerous rejected response vs polite chosen refusal | | refusals-offensive | 704, 100 | Offensive rejected response vs polite chosen refusal | | llmbar-natural | 100 | Manually curated instruction pairs (See [paper](https://arxiv.org/abs/2310.07641)) | | llmbar-adver-neighbor | 134 | Adversarial instruction response vs. off-topic prompt response (See [paper](https://arxiv.org/abs/2310.07641))| | llmbar-adver-GPTInst | 92 | Adversarial instruction response vs. GPT4 generated off-topic prompt response (See [paper](https://arxiv.org/abs/2310.07641))| | llmbar-adver-GPTOut | 47 | Adversarial instruction response vs. unhelpful-prompted GPT4 responses (See [paper](https://arxiv.org/abs/2310.07641))| | llmbar-adver-manual | 46 | Challenge set manually designed chosen vs. rejected | | xstest-should-refuse | 450, 154 | False response dataset (see [paper](https://arxiv.org/abs/2308.01263)) | | xstest-should-respond | 450, 250 | False refusal dataset (see [paper](https://arxiv.org/abs/2308.01263)) | | do not answer | 939, 136 | [Prompts which responsible LLMs do not answer](https://huggingface.co/datasets/LibrAI/do-not-answer): Refusals are chosen and responses are rejected | | hep-cpp | 164 | C++ working code vs. buggy code (See [dataset](https://huggingface.co/datasets/bigcode/humanevalpack) or [paper](https://arxiv.org/abs/2308.07124)) | | hep-go | 164 | Go working code vs. buggy code | | hep-java | 164 | Java working code vs. buggy code | | hep-js | 164 | Javascript working code vs. buggy code | | hep-python | 164 | Python working code vs. buggy code | | hep-rust | 164 | Rust working code vs. buggy code | | math-prm | 447 | Human references vs. model error (see [paper](https://github.com/openai/prm800k)) | The length distribution of the subsets with a Llama tokenizer is shown below. | subset | Chosen Mean Tokens | Rejected Mean Tokens | Chosen Max Tokens | Rejected Max Tokens | Chosen Min Tokens | Rejected Min Tokens | Chosen Mean Unique Tokens | Rejected Mean Unique Tokens | Chosen Max Unique Tokens | Rejected Max Unique Tokens | Chosen Min Unique Tokens | Rejected Min Unique Tokens | |-----------------------|----------------------|------------------------|---------------------|-----------------------|---------------------|-----------------------|-----------------------------|-------------------------------|----------------------------|------------------------------|----------------------------|------------------------------| | alpacaeval-easy | 591.26 | 167.33 | 1332 | 1043 | 40 | 15 | 252.91 | 83.44 | 630 | 290 | 33 | 12 | | alpacaeval-hard | 411.684 | 136.926 | 1112 | 711 | 57 | 12 | 172.537 | 70.9684 | 359 | 297 | 45 | 8 | | alpacaeval-length | 510.589 | 596.895 | 1604 | 2242 | 55 | 52 | 192.442 | 188.547 | 434 | 664 | 30 | 38 | | donotanswer | 169.61 | 320.5 | 745 | 735 | 20 | 20 | 103.743 | 156.941 | 358 | 337 | 18 | 13 | | hep-cpp | 261.262 | 259.488 | 833 | 835 | 53 | 57 | 99.8537 | 99.372 | 201 | 201 | 37 | 40 | | hep-go | 266.22 | 264.598 | 732 | 720 | 55 | 57 | 99.622 | 99.189 | 201 | 201 | 36 | 37 | | hep-java | 263.14 | 260.939 | 748 | 733 | 55 | 54 | 102.311 | 101.927 | 207 | 206 | 39 | 41 | | hep-js | 251.165 | 249.695 | 771 | 774 | 53 | 52 | 93.2744 | 92.9268 | 192 | 192 | 37 | 40 | | hep-python | 211.988 | 211.146 | 624 | 612 | 53 | 49 | 85.6463 | 85.3049 | 190 | 190 | 36 | 35 | | hep-rust | 221.256 | 219.049 | 988 | 993 | 46 | 49 | 95.1402 | 94.8354 | 192 | 192 | 36 | 36 | | llmbar-adver-GPTInst | 170.109 | 377.359 | 636 | 959 | 15 | 15 | 92.9457 | 179.37 | 287 | 471 | 12 | 13 | | llmbar-adver-GPTOut | 96.4255 | 101 | 393 | 476 | 18 | 20 | 60.0426 | 55.0426 | 241 | 228 | 13 | 14 | | llmbar-adver-manual | 159.804 | 264.37 | 607 | 737 | 23 | 33 | 91.9565 | 140.13 | 273 | 385 | 18 | 24 | | llmbar-adver-neighbor | 70.2239 | 172.507 | 603 | 865 | 9 | 13 | 43.3134 | 90.9328 | 250 | 324 | 8 | 9 | | llmbar-natural | 139.42 | 129.82 | 907 | 900 | 17 | 18 | 74.99 | 70.07 | 354 | 352 | 14 | 14 | | math-prm | 279.313 | 488.841 | 1608 | 1165 | 35 | 77 | 83.6264 | 124.582 | 237 | 257 | 23 | 46 | | mt-bench-easy | 391.821 | 481.929 | 778 | 1126 | 155 | 31 | 169.071 | 121.321 | 288 | 434 | 74 | 19 | | mt-bench-hard | 287.784 | 301.649 | 573 | 1176 | 68 | 62 | 133.622 | 121.676 | 261 | 309 | 50 | 48 | | mt-bench-med | 351.375 | 466.025 | 655 | 1297 | 145 | 52 | 159.9 | 140.325 | 285 | 495 | 82 | 41 | | refusals-dangerous | 208.4 | 458.61 | 380 | 804 | 87 | 103 | 128.53 | 211 | 200 | 365 | 71 | 55 | | refusals-offensive | 139.82 | 298.63 | 278 | 1117 | 75 | 26 | 95.98 | 134.02 | 170 | 477 | 60 | 19 | | xstest-should-refuse | 129.227 | 217.019 | 402 | 549 | 18 | 15 | 80.5519 | 116.149 | 194 | 245 | 16 | 13 | | xstest-should-respond | 188.708 | 107.356 | 515 | 465 | 20 | 16 | 103.788 | 67.328 | 231 | 202 | 15 | 16 | ### Filtering Summary The RewardBench dataset is manually filtered from 5123 source prompts to manually verify the chosen-rejected ranking of prompts. * The categories of AlpacaEval and MT Bench are manually filtered for every prompt. * LLMBar, DoNotAnswer, HEP, and Math PRM all contained structured metadata for automatic filtering. * XSTest is a hybrid of manual confirmation with metadata from the project. * Refusals are automatically generated as a refusal or response (where refusal is preffered) with manual confirmation. Substantial filtering details are available in the appendix of the papr. If there are any bugs in the data, please reach out! ### License information Licensing an aggregated dataset is a complex task. We release the RewardBench dataset under [ODC-BY](https://opendatacommons.org/licenses/by/) requiring the user to follow the licenses of the subsequent parts. Licensing LLM datasets is an evolving topic. The licenses primarily apply to the prompts and the completions generated by models are often unlicensed. The details for the datasets used in this work vary in the level of the detail on licenses and method of applying them. | Dataset | Variants | Data License | |---------------|----------------------------------------------------------|------------------------------------------------------| | AlpacaEval | {Easy, Length, Hard} | [CC By NC 4.0](https://github.com/tatsu-lab/alpaca_farm/blob/main/DATA_LICENSE) | | MT Bench | {Easy, Medium, Hard} | [Apache 2.0](https://github.com/lm-sys/FastChat/blob/main/LICENSE) | | LLMBar | {Natural, Neighbor, GPTInst, GPTOut, Manual} | [MIT License](https://github.com/princeton-nlp/LLMBar?tab=MIT-1-ov-file) | | Do Not Answer | | [CC BY NC SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) | | XSTest | {Should Respond, Should Refuse} | [CC By 4.0](https://github.com/paul-rottger/exaggerated-safety?tab=CC-BY-4.0-1-ov-file) | | HumanEvalPack | {HEP CPP, Go, Javascript, Rust, Python, Rust} | [MIT License](https://github.com/bigcode-project/octopack?tab=MIT-1-ov-file) | | PRM Math | | [MIT License](https://github.com/openai/prm800k?tab=MIT-1-ov-file) | Within this dataset are prompts created by AI2 (the refusals data, released as MIT for now, see official release soon) with completions from API and open models. More details will come on this soon. ## Development ### Requirements Building the dataset requires `datasets`. Maintaining the script and notebooks requites `notebook`. ``` pip install datasets notebook nbconvert ``` Convert with: ``` jupyter nbconvert --to script [YOUR_NOTEBOOK].ipynb ``` With no changes to the ipynb, the dataset can be re-built and pushed with the following (PLEASE BE CAREFUL): ``` python build_dataset.py ``` ### Git LFS notes If your uploads fail with: ``` Git LFS upload failed: 14% (1/7), 4.2 MB | 0 B/s (missing) data/train-00000-of-00001.parquet (425c88744455a9b0e7248cdd81fe4716085aae22849798f653f59fc878117a4d) hint: Your push was rejected due to missing or corrupt local objects. hint: You can disable this check with: `git config lfs.allowincompletepush true` ``` First fetch all lfs objects: ``` git lfs fetch --all origin main ``` ### Filtering script (basic) To filter data, run the following script: ``` python scripts/filter.py subset-name 0 ``` with a subset from the dataset and a start index. --- ## Citation ``` @misc{RewardBench, title={RewardBench: Evaluating Reward Models for Language Modeling}, author={Lambert, Nathan and Pyatkin, Valentina and Morrison, Jacob and Miranda, LJ and Lin, Bill Yuchen and Chandu, Khyathi and Dziri, Nouha and Kumar, Sachin and Zick, Tom and Choi, Yejin and Smith, Noah A. and Hajishirzi, Hannaneh}, year={2024}, howpublished={\url{https://huggingface.co/spaces/allenai/reward-bench} } ```

nvidia/ChatQA2-Long-SFT-data

nvidia

2024-09-09T05:39:09Z

287

28

[ "language:en", "license:cc-by-nc-2.0", "size_categories:100K<n<1M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2407.14482", "region:us", "RAG", "conversational QA", "multi-turn QA", "QA with context", "train" ]

[]

2024-09-08T07:25:03Z

2

--- license: cc-by-nc-2.0 size_categories: - 10K<n<100K language: - en tags: - RAG - conversational QA - multi-turn QA - QA with context - train configs: - config_name: long_sft data_files: - split: train path: long_sft/long_sft_QA_train.json - split: test path: long_sft/long_sft_QA_dev.json - config_name: NarrativeQA_131072 data_files: - split: train path: NarrativeQA_131072/NarrativeQA_131072_QA_train.json - split: test path: NarrativeQA_131072/NarrativeQA_131072_QA_dev.json --- ## Data Description Here, we release the full long SFT training dataset of [ChatQA2](https://arxiv.org/abs/2407.14482). It consists of two parts: **long_sft** and **NarrativeQA_131072**. The long_sft dataset is built and derived from existing datasets: [LongAlpaca12k](https://github.com/dvlab-research/LongLoRA), GPT-4 samples from [Open Orca](https://huggingface.co/datasets/Open-Orca/OpenOrca), and [Long Data Collections](https://huggingface.co/datasets/togethercomputer/Long-Data-Collections). The NarrativeQA_131072 dataset is synthetically generated from NarrativeQA by adding related paragraphs to the given ground truth summary. For the first two steps training of ChatQA-2, we follow [ChatQA1.5](https://huggingface.co/datasets/nvidia/ChatQA-Training-Data). For the continue pretraining dataset, we simply follow [Long-Context Data Engineering](https://github.com/FranxYao/Long-Context-Data-Engineering) to generate 10B tokens. **For more information about ChatQA-2, check the [website](https://chatqa2-project.github.io/)!** ## Other Resources [Llama3-ChatQA-2-8B](https://huggingface.co/nvidia/Llama3-ChatQA-2-8B) &ensp; [Llama3-ChatQA-1.5-70B](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-70B) &ensp; [Evaluation Data](https://huggingface.co/nvidia/Llama3-ChatQA-2-70B/tree/main/data) &ensp; [Website](https://chatqa2-project.github.io/) &ensp; [Paper](https://arxiv.org/abs/2407.14482) ## Training Details The training follows a three-stage instruction tuning process. For the first two stages, we follow ChatQA-1.5, i.e., the stage-1 uses the SFT data, and the stage-2 uses a blend of SFT data alongside other datasets. The dataset blending ratio for stage-2 is as follows: - drop: 0.069 - narrativeqa: 0.095 - quoref: 0.026 - ropes: 0.026 - squad1.1: 0.095 - squad2.0: 0.095 - newsqa: 0.095 - tatqa-arithmetic: 0.15 - tatqa-others: 0.08 - synthetic_convqa: 0.3 - sft: 0.2 The stage-3 add the full long SFT dataset to the blend. And the new dataset blending ratio for stage-3 is as follows: - drop: 0.069 - narrativeqa: 0.095 - quoref: 0.026 - ropes: 0.026 - squad1.1: 0.095 - squad2.0: 0.095 - newsqa: 0.095 - tatqa-arithmetic: 0.15 - tatqa-others: 0.08 - synthetic_convqa: 0.3 - sft: 0.2 - long_sft: 2.5 - NarrativeQA_131072: 5.0 ## License The dataset is released for non-commercial use only, subject to [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI. ## Correspondence to Peng Xu ([email protected]), Wei Ping ([email protected]) ## Citation <pre> @article{xu2024chatqa, title={ChatQA 2: Bridging the Gap to Proprietary LLMs in Long Context and RAG Capabilities}, author={Xu, Peng and Ping, Wei and Wu, Xianchao and Liu, Zihan and Shoeybi, Mohammad and Catanzaro, Bryan}, journal={arXiv preprint arXiv:2407.14482}, year={2024} } </pre>

joujiboi/japanese-anime-speech

joujiboi

2024-09-06T18:19:39Z

1,287

124

[ "task_categories:automatic-speech-recognition", "language:ja", "license:cc0-1.0", "size_categories:10K<n<100K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "anime", "japanese", "日本語", "nihongo", "speech", "audio-text", "asr", "whisper", "voice", "large-v3", "ja", "jp" ]

[ "automatic-speech-recognition" ]

2023-11-07T13:53:40Z

null

--- language: - ja license: cc0-1.0 size_categories: - 10K<n<100K task_categories: - automatic-speech-recognition pretty_name: Japanese-Anime-Speech dataset_info: features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 10116168716.932 num_examples: 73004 download_size: 8832932312 dataset_size: 10116168716.932 configs: - config_name: default data_files: - split: train path: data/train-* tags: - anime - japanese - 日本語 - nihongo - speech - audio-text - asr - whisper - voice - large-v3 - ja - jp --- # Japanese Anime Speech Dataset [**日本語はこちら**](https://huggingface.co/datasets/joujiboi/japanese-anime-speech/blob/main/README_JA.md) **japanese-anime-speech** is an audio-text dataset designed for the training of automatic speech recognition models. The dataset is comprised of thousands of audio clips and their corresponding transcriptions from different visual novels. The goal of this dataset is to increase the accuracy of automatic speech recognition models, such as OpenAI's [Whisper](https://huggingface.co/openai/whisper-large-v2), in accurately transcribing dialogue from anime and other similar Japanese media. This genre is characterized by unique linguistic features and speech patterns that diverge from conventional Japanese speech. A list of all audio files and transcriptions can be found [**here**](https://huggingface.co/datasets/joujiboi/japanese-anime-speech/raw/main/audio_transcription_list.txt). <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Content Warning:</b> Please be advised that the majority of the audio in this dataset is sourced from visual novels and may include content that is not suitable for all audiences, such as suggestive sounds or mature topics. Efforts have been undertaken to minimise this content as much as possible. </p> </div> # Dataset information * **73,004** audio-text pairs * **110 hours** of audio (OpenAI suggests a minimum of [5 hours](https://huggingface.co/blog/fine-tune-whisper) for productive [Whisper](https://huggingface.co/openai/whisper-large-v2) fine-tuning). * **5.4s** average audio length * Audio source: **visual novels** * Lastest version: **V5 - March 22nd 2024** # Changelog * V1 - This version contains **16,143** audio-text pairs from the visual novel `IxSHE Tell`. Some cleaning of the transcriptions has been done to get rid of unwanted characters at the start and end of lines. * V2 - The version contains **23,422** audio-text pairs from three different visual novels. Cleaning has been done to remove most nsfw lines, especially noises that aren't words. The audio is now in mp3 format, rather than wav. This version contains **32.6** hours of audio. * V3 - The version contains **38,325** audio-text pairs from five different visual novels. Thorough cleaning has been done to remove most nsfw or low-quality audio files. Transcriptions have been formatted to contain much fewer dramatised duplicated characters (for example 「ああああーーー」), and transcriptions have been made much more consistent. This version contains **52.5 hours** of audio. * V4 - The dataset contains **47,844** audio-text pairs from six different visual novels. Thorough cleaning has been done to remove most nsfw or low-quality audio files. This version contains **63.4 hours** of audio. * **V5** - The dataset contains **73,004** audio-text pairs from eight different visual novels. Thorough cleaning has been done to remove most nsfw or low-quality audio files. This version contains **110 hours** of audio. # Bias and Limitations This dataset, while valuable for training anime-style Japanese speech recognition, has some inherent biases and limitations. The audio is primarily sourced from visual novels, leading to a gender bias towards female voices and a domain-specific vocabulary revolving around topics such as love, relationships, and fantasy. Additionally, the professionally produced nature of the audio results in clear and slow speech, which may not fully reflect real-world speaking patterns. # Use & Credit This dataset is openly available for commercial or non-commercial use. Anyone is welcome to use this dataset as they deem appropriate. However, the creator assumes no responsibility for the consequences of its use. While not mandatory, crediting this dataset with a hyperlink in any derivative work would be greatly appreciated. I hope that by sharing this dataset, we (the open-source community) improve automatic speech recognition for anime content.

MoreTorque/rlogs

MoreTorque

2024-09-06T18:18:47Z

10,867

0

[ "license:apache-2.0", "region:us" ]

[]

2024-06-22T21:22:44Z

null

--- license: apache-2.0 --- ## Dataset Overview This dataset consists of bz2 compressed rlogs captured from OpenPilot. These logs do not contain any user-sensitive information. The primary purpose of this dataset is to facilitate data-driven solutions for improving lateral and longitudinal control in community-supported vehicles. # Contents The rlogs are composed of various events as defined in the OpenPilot log schema. To ensure user privacy, many event types have been excluded. The following is the list of events that remain in the log files: ``` * CarState * LiveParameters * CarControl * LateralPlanDEPRECATED * CarOutput * CarParams * ModelV2 * LiveTorqueParameters * LiveCalibration * Sendcan * Can * LongitudinalPlan * LiveLocationKalman ( Only OrientaionNED, CalibratedOrientaionNED, and AngularVelocityCalibrated have been kept )

4DR1455/finance_questions

4DR1455

2024-09-05T10:59:48Z

193

11

[ "language:en", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-06-20T11:17:35Z

2

--- language: - en --- Financial-instruction-aq22 dataset from DeividasM but with "input" to make it easier to use on a fine tunning template. Still working on it.

InfiniFlow/medical_QA

InfiniFlow

2024-09-05T03:23:52Z

187

27

[ "license:apache-2.0", "region:us" ]

[]

2024-09-05T02:20:22Z

2

--- license: apache-2.0 ---

alexandrainst/ftspeech

alexandrainst

2024-09-04T15:01:21Z

30,392

3

[ "task_categories:automatic-speech-recognition", "language:da", "license:other", "size_categories:1M<n<10M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2005.12368", "region:us" ]

[ "automatic-speech-recognition" ]

2023-05-11T13:08:57Z

null

--- dataset_info: features: - name: utterance_id dtype: string - name: speaker_gender dtype: string - name: sentence dtype: string - name: speaker_id dtype: string - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 209434570129.268 num_examples: 995677 - name: dev_balanced num_bytes: 579692770.829 num_examples: 2601 - name: dev_other num_bytes: 1725502342.095 num_examples: 7595 - name: test_balanced num_bytes: 1158740779.222 num_examples: 5534 - name: test_other num_bytes: 1254987645.527 num_examples: 5837 download_size: 101776974871 dataset_size: 214153493666.941 task_categories: - automatic-speech-recognition language: - da pretty_name: FT Speech size_categories: - 100K<n<1M license: other --- # Dataset Card for FT Speech ## Dataset Description - **Repository:** <https://ftspeech.github.io/> - **Point of Contact:** [Dan Saattrup Nielsen](mailto:[email protected]) - **Size of downloaded dataset files:** 101.78 GB - **Size of the generated dataset:** 214.15 GB - **Total amount of disk used:** 315.93 GB ### Dataset Summary This dataset is an upload of the [FT Speech dataset](https://ftspeech.github.io/). The training, validation and test splits are the original ones. ### Supported Tasks and Leaderboards Training automatic speech recognition is the intended task for this dataset. No leaderboard is active at this point. ### Languages The dataset is available in Danish (`da`). ## Dataset Structure ### Data Instances - **Size of downloaded dataset files:** 101.78 GB - **Size of the generated dataset:** 214.15 GB - **Total amount of disk used:** 315.93 GB An example from the dataset looks as follows. ``` { 'utterance_id': 'S001_20151_M012_P00034-2', 'speaker_gender': 'F', 'sentence': 'alle de fem tekniske justeringer der er en del af lovforslaget', 'speaker_id': 'S001', 'audio': { 'path': 'S001_20151_M012_P00034-2.wav', 'array': array([-3.75366211e-03, -5.27954102e-03, -3.87573242e-03, ..., 9.15527344e-05, -1.52587891e-04, 5.79833984e-04]), 'sampling_rate': 16000 } } ``` ### Data Fields The data fields are the same among all splits. - `utterance_id`: a `string` feature. - `speaker_gender`: a `string` feature. - `sentence`: a `string` feature. - `speaker_id`: a `string` feature. - `audio`: an `Audio` feature. ### Dataset Statistics There are 995,677 samples in the training split, 2,601 in the dev_balanced split, 7,595 in the dev_other split, 5,534 in the test_balanced and 5,837 in the test_other split. #### Speakers There are 374 unique speakers in the training dataset, 20 unique speakers in the validation dataset and 40 unique speakers in the test dataset. None of the dataset splits share any speakers. #### Gender Distribution ![ftspeech-gender-distribution.png](https://cdn-uploads.huggingface.co/production/uploads/60d368a613f774189902f555/0h_L7-riNfQbKFdYWgy01.png) #### Transcription Length Distribution ![ftspeech-length-distribution.png](https://cdn-uploads.huggingface.co/production/uploads/60d368a613f774189902f555/z1MqsvACrY_8XNXAx0UcD.png) ## Dataset Creation ### Curation Rationale There are not many large-scale ASR datasets in Danish. ### Source Data The data constitutes public recordings of sessions from the Danish Parliament, along with manual transcriptions. ## Additional Information ### Dataset Curators Andreas Kirkedal, Marija Stepanović and Barbara Plank curated the dataset as part of their FT Speech paper (see citation below). [Dan Saattrup Nielsen](https://saattrupdan.github.io/) from the [The Alexandra Institute](https://alexandra.dk/) reorganised the dataset and uploaded it to the Hugging Face Hub. ### Licensing Information The dataset is licensed under [this custom license](https://www.ft.dk/da/aktuelt/tv-fra-folketinget/deling-og-rettigheder). ### Citation ``` @inproceedings{ftspeech, author = {Kirkedal, Andreas and Stepanović, Marija and Plank, Barbara}, title = {{FT Speech: Danish Parliament Speech Corpus}}, booktitle = {Proc. Interspeech 2020}, year = {2020}, url = {arxiv.org/abs/2005.12368} } ```

BleachNick/UltraEdit

BleachNick

2024-08-31T13:49:21Z

52,807

8

[ "task_categories:text-to-image", "language:en", "license:cc-by-4.0", "arxiv:2407.05282", "doi:10.57967/hf/2481", "region:us", "art" ]

[ "text-to-image" ]

2024-06-09T11:02:13Z

null

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name: FreeForm_1563 num_bytes: 774817952 num_examples: 2000 - name: FreeForm_1675 num_bytes: 773030944 num_examples: 2000 - name: FreeForm_1962 num_bytes: 786053209 num_examples: 2000 - name: FreeForm_1792 num_bytes: 774700008 num_examples: 2000 - name: FreeForm_1615 num_bytes: 774380131 num_examples: 2000 - name: FreeForm_1846 num_bytes: 774658032 num_examples: 2000 - name: FreeForm_1616 num_bytes: 782429195 num_examples: 2000 - name: FreeForm_1850 num_bytes: 775140091 num_examples: 2000 - name: FreeForm_1964 num_bytes: 780393901 num_examples: 2000 - name: FreeForm_1801 num_bytes: 768773753 num_examples: 2000 - name: FreeForm_1851 num_bytes: 775091817 num_examples: 2000 - name: FreeForm_1965 num_bytes: 774710107 num_examples: 2000 - name: FreeForm_1626 num_bytes: 776500055 num_examples: 2000 - name: FreeForm_1853 num_bytes: 774376334 num_examples: 2000 - name: FreeForm_1967 num_bytes: 767462102 num_examples: 2000 - name: FreeForm_1692 num_bytes: 766343506 num_examples: 2000 - name: FreeForm_1854 num_bytes: 768674186 num_examples: 2000 - name: FreeForm_1975 num_bytes: 765777279 num_examples: 2000 - name: FreeForm_1699 num_bytes: 778883501 num_examples: 2000 - name: FreeForm_1755 num_bytes: 783000185 num_examples: 2000 - name: FreeForm_1757 num_bytes: 769193034 num_examples: 2000 - name: FreeForm_1763 num_bytes: 772044823 num_examples: 2000 - name: FreeForm_1814 num_bytes: 777568635 num_examples: 2000 - name: FreeForm_1816 num_bytes: 776191715 num_examples: 2000 - name: FreeForm_1821 num_bytes: 777857890 num_examples: 2000 - name: FreeForm_1856 num_bytes: 769967566 num_examples: 2000 - name: FreeForm_1862 num_bytes: 767341817 num_examples: 2000 - name: FreeForm_1873 num_bytes: 772574070 num_examples: 2000 - name: FreeForm_1875 num_bytes: 770945433 num_examples: 2000 - name: FreeForm_1877 num_bytes: 772618224 num_examples: 2000 - name: FreeForm_1935 num_bytes: 780171644 num_examples: 2000 - name: FreeForm_1936 num_bytes: 780368989 num_examples: 2000 - name: FreeForm_1938 num_bytes: 775192638 num_examples: 2000 - name: FreeForm_1939 num_bytes: 768517191 num_examples: 2000 - name: FreeForm_1941 num_bytes: 767928606 num_examples: 2000 - name: FreeForm_1977 num_bytes: 780736929 num_examples: 2000 - name: FreeForm_1981 num_bytes: 775615890 num_examples: 2000 - name: FreeForm_1984 num_bytes: 769609649 num_examples: 2000 - name: FreeForm_1985 num_bytes: 770730441 num_examples: 2000 - name: FreeForm_1987 num_bytes: 768263066 num_examples: 2000 - name: FreeForm_1989 num_bytes: 780388977 num_examples: 2000 - name: FreeForm_1990 num_bytes: 772863509 num_examples: 2000 - name: FreeForm_1993 num_bytes: 773757340 num_examples: 2000 - name: FreeForm_1996 num_bytes: 770872885 num_examples: 2000 - name: FreeForm_2000 num_bytes: 32585530 num_examples: 83 - name: FreeForm_1205 num_bytes: 776134960.0 num_examples: 2000 download_size: 1182151585538 dataset_size: 1177371972678.0 configs: - config_name: default data_files: - split: FreeForm_0 path: data/FreeForm_0-* - split: FreeForm_1 path: data/FreeForm_1-* - split: FreeForm_2 path: data/FreeForm_2-* - split: FreeForm_3 path: data/FreeForm_3-* - split: FreeForm_4 path: data/FreeForm_4-* - split: FreeForm_5 path: data/FreeForm_5-* - split: FreeForm_6 path: data/FreeForm_6-* - split: FreeForm_7 path: data/FreeForm_7-* - split: FreeForm_8 path: data/FreeForm_8-* - split: FreeForm_9 path: data/FreeForm_9-* - split: FreeForm_10 path: data/FreeForm_10-* - split: FreeForm_11 path: data/FreeForm_11-* - split: FreeForm_12 path: data/FreeForm_12-* - split: FreeForm_13 path: data/FreeForm_13-* - split: FreeForm_14 path: data/FreeForm_14-* - split: FreeForm_15 path: data/FreeForm_15-* - split: FreeForm_16 path: data/FreeForm_16-* - split: FreeForm_17 path: data/FreeForm_17-* - split: FreeForm_18 path: data/FreeForm_18-* - split: FreeForm_19 path: data/FreeForm_19-* - split: FreeForm_20 path: data/FreeForm_20-* - split: FreeForm_21 path: data/FreeForm_21-* - split: FreeForm_22 path: data/FreeForm_22-* - split: FreeForm_23 path: data/FreeForm_23-* - split: FreeForm_24 path: data/FreeForm_24-* - split: FreeForm_25 path: data/FreeForm_25-* - split: FreeForm_26 path: data/FreeForm_26-* - split: FreeForm_27 path: data/FreeForm_27-* - split: FreeForm_28 path: data/FreeForm_28-* - split: FreeForm_29 path: data/FreeForm_29-* - split: FreeForm_30 path: data/FreeForm_30-* - split: FreeForm_31 path: data/FreeForm_31-* - split: FreeForm_32 path: data/FreeForm_32-* - split: FreeForm_33 path: data/FreeForm_33-* - split: FreeForm_34 path: data/FreeForm_34-* - split: FreeForm_35 path: data/FreeForm_35-* - split: FreeForm_36 path: data/FreeForm_36-* - split: FreeForm_37 path: data/FreeForm_37-* - split: FreeForm_38 path: data/FreeForm_38-* - split: FreeForm_39 path: data/FreeForm_39-* - split: FreeForm_40 path: data/FreeForm_40-* - split: FreeForm_41 path: data/FreeForm_41-* - split: FreeForm_42 path: data/FreeForm_42-* - split: FreeForm_43 path: data/FreeForm_43-* - split: FreeForm_44 path: data/FreeForm_44-* - split: FreeForm_45 path: data/FreeForm_45-* - split: FreeForm_46 path: data/FreeForm_46-* - split: FreeForm_47 path: data/FreeForm_47-* - split: FreeForm_48 path: data/FreeForm_48-* - split: FreeForm_49 path: data/FreeForm_49-* - split: FreeForm_50 path: data/FreeForm_50-* - split: FreeForm_51 path: data/FreeForm_51-* - split: FreeForm_52 path: data/FreeForm_52-* - split: FreeForm_53 path: data/FreeForm_53-* - split: FreeForm_54 path: data/FreeForm_54-* - split: FreeForm_55 path: data/FreeForm_55-* - split: FreeForm_56 path: data/FreeForm_56-* - split: FreeForm_57 path: data/FreeForm_57-* - split: FreeForm_58 path: data/FreeForm_58-* - split: FreeForm_59 path: data/FreeForm_59-* - split: FreeForm_60 path: data/FreeForm_60-* - split: FreeForm_61 path: data/FreeForm_61-* - split: FreeForm_62 path: data/FreeForm_62-* - split: FreeForm_63 path: data/FreeForm_63-* - split: FreeForm_64 path: data/FreeForm_64-* - split: FreeForm_65 path: data/FreeForm_65-* - split: FreeForm_66 path: data/FreeForm_66-* - split: FreeForm_67 path: data/FreeForm_67-* - split: FreeForm_68 path: data/FreeForm_68-* - split: FreeForm_69 path: data/FreeForm_69-* - split: FreeForm_70 path: data/FreeForm_70-* - split: FreeForm_71 path: data/FreeForm_71-* - split: FreeForm_72 path: data/FreeForm_72-* - split: FreeForm_73 path: data/FreeForm_73-* - split: FreeForm_74 path: data/FreeForm_74-* - split: FreeForm_75 path: data/FreeForm_75-* - split: FreeForm_76 path: data/FreeForm_76-* - split: FreeForm_77 path: data/FreeForm_77-* - split: FreeForm_78 path: data/FreeForm_78-* - split: FreeForm_79 path: data/FreeForm_79-* - split: FreeForm_80 path: data/FreeForm_80-* - split: FreeForm_81 path: data/FreeForm_81-* - split: FreeForm_82 path: data/FreeForm_82-* - split: FreeForm_83 path: data/FreeForm_83-* - split: FreeForm_84 path: data/FreeForm_84-* - split: FreeForm_85 path: data/FreeForm_85-* - split: FreeForm_86 path: data/FreeForm_86-* - split: FreeForm_87 path: data/FreeForm_87-* - split: FreeForm_88 path: data/FreeForm_88-* - split: FreeForm_89 path: data/FreeForm_89-* - split: FreeForm_90 path: data/FreeForm_90-* - split: FreeForm_91 path: data/FreeForm_91-* - split: FreeForm_92 path: data/FreeForm_92-* - split: FreeForm_93 path: data/FreeForm_93-* - split: FreeForm_94 path: data/FreeForm_94-* - split: FreeForm_95 path: data/FreeForm_95-* - split: FreeForm_96 path: data/FreeForm_96-* - split: FreeForm_97 path: data/FreeForm_97-* - split: FreeForm_98 path: data/FreeForm_98-* - split: FreeForm_99 path: data/FreeForm_99-* - split: FreeForm_100 path: data/FreeForm_100-* - split: FreeForm_101 path: data/FreeForm_101-* - split: FreeForm_102 path: data/FreeForm_102-* - split: FreeForm_103 path: data/FreeForm_103-* - split: FreeForm_104 path: data/FreeForm_104-* - split: FreeForm_105 path: data/FreeForm_105-* - split: FreeForm_106 path: data/FreeForm_106-* - split: FreeForm_107 path: data/FreeForm_107-* - split: FreeForm_108 path: data/FreeForm_108-* - split: FreeForm_109 path: data/FreeForm_109-* - split: FreeForm_110 path: data/FreeForm_110-* - split: FreeForm_111 path: data/FreeForm_111-* - split: FreeForm_112 path: data/FreeForm_112-* - split: FreeForm_113 path: data/FreeForm_113-* - split: FreeForm_114 path: data/FreeForm_114-* - split: FreeForm_115 path: data/FreeForm_115-* - split: FreeForm_116 path: data/FreeForm_116-* - split: FreeForm_117 path: data/FreeForm_117-* - split: FreeForm_118 path: data/FreeForm_118-* - split: FreeForm_119 path: data/FreeForm_119-* - split: FreeForm_120 path: data/FreeForm_120-* - split: FreeForm_121 path: data/FreeForm_121-* - split: FreeForm_122 path: data/FreeForm_122-* - split: FreeForm_123 path: data/FreeForm_123-* - split: FreeForm_124 path: data/FreeForm_124-* - split: FreeForm_125 path: data/FreeForm_125-* - split: FreeForm_126 path: data/FreeForm_126-* - split: FreeForm_127 path: data/FreeForm_127-* - split: FreeForm_128 path: data/FreeForm_128-* - split: FreeForm_129 path: data/FreeForm_129-* - split: FreeForm_130 path: data/FreeForm_130-* - split: FreeForm_131 path: data/FreeForm_131-* - split: FreeForm_132 path: data/FreeForm_132-* - split: FreeForm_133 path: data/FreeForm_133-* - split: FreeForm_134 path: data/FreeForm_134-* - split: FreeForm_135 path: data/FreeForm_135-* - split: FreeForm_136 path: data/FreeForm_136-* - split: FreeForm_137 path: data/FreeForm_137-* - split: FreeForm_138 path: data/FreeForm_138-* - split: FreeForm_139 path: data/FreeForm_139-* - split: FreeForm_140 path: data/FreeForm_140-* - split: FreeForm_141 path: data/FreeForm_141-* - split: FreeForm_142 path: data/FreeForm_142-* - split: FreeForm_143 path: data/FreeForm_143-* - split: FreeForm_144 path: data/FreeForm_144-* - split: FreeForm_145 path: data/FreeForm_145-* - split: FreeForm_146 path: data/FreeForm_146-* - split: FreeForm_147 path: data/FreeForm_147-* - split: FreeForm_148 path: data/FreeForm_148-* - split: FreeForm_149 path: data/FreeForm_149-* - split: FreeForm_150 path: data/FreeForm_150-* - split: FreeForm_151 path: data/FreeForm_151-* - split: FreeForm_152 path: data/FreeForm_152-* - split: FreeForm_153 path: data/FreeForm_153-* - split: FreeForm_154 path: data/FreeForm_154-* - split: FreeForm_155 path: data/FreeForm_155-* - split: FreeForm_156 path: data/FreeForm_156-* - split: FreeForm_157 path: data/FreeForm_157-* - split: FreeForm_158 path: data/FreeForm_158-* - split: FreeForm_159 path: data/FreeForm_159-* - split: FreeForm_160 path: data/FreeForm_160-* - split: FreeForm_161 path: data/FreeForm_161-* - split: FreeForm_162 path: data/FreeForm_162-* - split: FreeForm_163 path: data/FreeForm_163-* - split: FreeForm_164 path: data/FreeForm_164-* - split: FreeForm_165 path: data/FreeForm_165-* - split: FreeForm_166 path: data/FreeForm_166-* - split: FreeForm_167 path: data/FreeForm_167-* - split: FreeForm_168 path: data/FreeForm_168-* - split: FreeForm_169 path: data/FreeForm_169-* - split: FreeForm_170 path: data/FreeForm_170-* - split: FreeForm_171 path: data/FreeForm_171-* - split: FreeForm_172 path: data/FreeForm_172-* - split: FreeForm_173 path: data/FreeForm_173-* - split: FreeForm_174 path: data/FreeForm_174-* - split: FreeForm_175 path: data/FreeForm_175-* - split: FreeForm_176 path: data/FreeForm_176-* - split: FreeForm_177 path: data/FreeForm_177-* - split: FreeForm_178 path: data/FreeForm_178-* - split: FreeForm_179 path: data/FreeForm_179-* - split: FreeForm_180 path: data/FreeForm_180-* - split: FreeForm_181 path: data/FreeForm_181-* - split: FreeForm_182 path: data/FreeForm_182-* - split: FreeForm_183 path: data/FreeForm_183-* - split: FreeForm_184 path: data/FreeForm_184-* - split: FreeForm_185 path: data/FreeForm_185-* - split: FreeForm_186 path: data/FreeForm_186-* - split: FreeForm_187 path: data/FreeForm_187-* - split: FreeForm_188 path: data/FreeForm_188-* - split: FreeForm_189 path: data/FreeForm_189-* - split: FreeForm_190 path: data/FreeForm_190-* - split: FreeForm_191 path: data/FreeForm_191-* - split: FreeForm_192 path: data/FreeForm_192-* - split: FreeForm_193 path: data/FreeForm_193-* - split: FreeForm_194 path: data/FreeForm_194-* - split: FreeForm_195 path: data/FreeForm_195-* - split: FreeForm_196 path: data/FreeForm_196-* - split: FreeForm_197 path: data/FreeForm_197-* - split: FreeForm_198 path: data/FreeForm_198-* - split: FreeForm_199 path: data/FreeForm_199-* - split: FreeForm_200 path: data/FreeForm_200-* - split: FreeForm_201 path: data/FreeForm_201-* - split: FreeForm_202 path: data/FreeForm_202-* - split: FreeForm_203 path: data/FreeForm_203-* - split: FreeForm_204 path: data/FreeForm_204-* - split: FreeForm_205 path: data/FreeForm_205-* - split: FreeForm_206 path: data/FreeForm_206-* - split: FreeForm_207 path: data/FreeForm_207-* - split: FreeForm_208 path: data/FreeForm_208-* - split: FreeForm_209 path: data/FreeForm_209-* - split: FreeForm_210 path: data/FreeForm_210-* - split: FreeForm_211 path: data/FreeForm_211-* - split: FreeForm_212 path: data/FreeForm_212-* - split: FreeForm_213 path: data/FreeForm_213-* - split: FreeForm_214 path: data/FreeForm_214-* - split: FreeForm_215 path: data/FreeForm_215-* - split: FreeForm_216 path: data/FreeForm_216-* - split: FreeForm_217 path: data/FreeForm_217-* - split: FreeForm_218 path: data/FreeForm_218-* - split: FreeForm_219 path: data/FreeForm_219-* - split: FreeForm_220 path: data/FreeForm_220-* - split: FreeForm_221 path: data/FreeForm_221-* - split: FreeForm_222 path: data/FreeForm_222-* - split: FreeForm_223 path: data/FreeForm_223-* - split: FreeForm_224 path: data/FreeForm_224-* - split: FreeForm_225 path: data/FreeForm_225-* - split: FreeForm_226 path: data/FreeForm_226-* - split: FreeForm_227 path: data/FreeForm_227-* - split: FreeForm_228 path: data/FreeForm_228-* - split: FreeForm_229 path: data/FreeForm_229-* - split: FreeForm_230 path: data/FreeForm_230-* - split: FreeForm_231 path: data/FreeForm_231-* - split: FreeForm_232 path: data/FreeForm_232-* - split: FreeForm_233 path: data/FreeForm_233-* - split: FreeForm_234 path: data/FreeForm_234-* - split: FreeForm_235 path: data/FreeForm_235-* - split: FreeForm_236 path: data/FreeForm_236-* - split: FreeForm_237 path: data/FreeForm_237-* - split: FreeForm_238 path: data/FreeForm_238-* - split: FreeForm_239 path: data/FreeForm_239-* - split: FreeForm_240 path: data/FreeForm_240-* - split: FreeForm_241 path: data/FreeForm_241-* - split: FreeForm_242 path: data/FreeForm_242-* - split: FreeForm_243 path: data/FreeForm_243-* - split: FreeForm_244 path: data/FreeForm_244-* - split: FreeForm_245 path: data/FreeForm_245-* - split: FreeForm_246 path: data/FreeForm_246-* - split: FreeForm_247 path: data/FreeForm_247-* - split: FreeForm_248 path: data/FreeForm_248-* - split: FreeForm_249 path: data/FreeForm_249-* - split: FreeForm_250 path: data/FreeForm_250-* - split: FreeForm_251 path: data/FreeForm_251-* - split: FreeForm_252 path: data/FreeForm_252-* - split: FreeForm_253 path: data/FreeForm_253-* - split: FreeForm_254 path: data/FreeForm_254-* - split: FreeForm_255 path: data/FreeForm_255-* - split: FreeForm_256 path: data/FreeForm_256-* - split: FreeForm_257 path: data/FreeForm_257-* - split: FreeForm_258 path: data/FreeForm_258-* - split: FreeForm_259 path: data/FreeForm_259-* - split: FreeForm_260 path: data/FreeForm_260-* - split: FreeForm_261 path: data/FreeForm_261-* - split: FreeForm_262 path: data/FreeForm_262-* - split: FreeForm_263 path: data/FreeForm_263-* - split: FreeForm_264 path: data/FreeForm_264-* - split: FreeForm_265 path: data/FreeForm_265-* - split: FreeForm_266 path: data/FreeForm_266-* - split: FreeForm_267 path: data/FreeForm_267-* - split: FreeForm_268 path: data/FreeForm_268-* - split: FreeForm_269 path: data/FreeForm_269-* - split: FreeForm_270 path: data/FreeForm_270-* - split: FreeForm_271 path: data/FreeForm_271-* - split: FreeForm_272 path: data/FreeForm_272-* - split: FreeForm_273 path: data/FreeForm_273-* - split: FreeForm_274 path: data/FreeForm_274-* - split: FreeForm_275 path: data/FreeForm_275-* - split: FreeForm_276 path: data/FreeForm_276-* - split: FreeForm_277 path: data/FreeForm_277-* - split: FreeForm_278 path: data/FreeForm_278-* - split: FreeForm_279 path: data/FreeForm_279-* - split: FreeForm_280 path: data/FreeForm_280-* - split: FreeForm_281 path: data/FreeForm_281-* - 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split: FreeForm_1763 path: data/FreeForm_1763-* - split: FreeForm_1814 path: data/FreeForm_1814-* - split: FreeForm_1816 path: data/FreeForm_1816-* - split: FreeForm_1821 path: data/FreeForm_1821-* - split: FreeForm_1856 path: data/FreeForm_1856-* - split: FreeForm_1862 path: data/FreeForm_1862-* - split: FreeForm_1873 path: data/FreeForm_1873-* - split: FreeForm_1875 path: data/FreeForm_1875-* - split: FreeForm_1877 path: data/FreeForm_1877-* - split: FreeForm_1935 path: data/FreeForm_1935-* - split: FreeForm_1936 path: data/FreeForm_1936-* - split: FreeForm_1938 path: data/FreeForm_1938-* - split: FreeForm_1939 path: data/FreeForm_1939-* - split: FreeForm_1941 path: data/FreeForm_1941-* - split: FreeForm_1977 path: data/FreeForm_1977-* - split: FreeForm_1981 path: data/FreeForm_1981-* - split: FreeForm_1984 path: data/FreeForm_1984-* - split: FreeForm_1985 path: data/FreeForm_1985-* - split: FreeForm_1987 path: data/FreeForm_1987-* - split: FreeForm_1989 path: data/FreeForm_1989-* - split: FreeForm_1990 path: data/FreeForm_1990-* - split: FreeForm_1993 path: data/FreeForm_1993-* - split: FreeForm_1996 path: data/FreeForm_1996-* - split: FreeForm_2000 path: data/FreeForm_2000-* tags: - art --- # Dataset Card for Dataset Name  This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed] ## Bibtex citation ```bibtex @misc{zhao2024ultraeditinstructionbasedfinegrainedimage, title={UltraEdit: Instruction-based Fine-Grained Image Editing at Scale}, author={Haozhe Zhao and Xiaojian Ma and Liang Chen and Shuzheng Si and Rujie Wu and Kaikai An and Peiyu Yu and Minjia Zhang and Qing Li and Baobao Chang}, year={2024}, eprint={2407.05282}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2407.05282}, } ```

amithm3/shrutilipi

amithm3

2024-08-31T05:27:21Z

15,355

3

[ "task_categories:automatic-speech-recognition", "language:kn", "language:sa", "language:bn", "language:pa", "language:ml", "language:gu", "language:ta", "language:te", "language:hi", "language:mr", "license:apache-2.0", "size_categories:1M<n<10M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "audio", "transcription", "AI4Bharat", "shrutilipi" ]

[ "automatic-speech-recognition" ]

2024-04-08T17:53:27Z

null

--- language: - kn - sa - bn - pa - ml - gu - ta - te - hi - mr license: apache-2.0 size_categories: - 1M<n<10M task_categories: - automatic-speech-recognition pretty_name: AI4Bharat Shrutilipi ASR Dataset dataset_info: - config_name: bn features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 59658532357.726 num_examples: 302349 - name: validation num_bytes: 6723169844.11 num_examples: 37602 - name: test num_bytes: 7660623563.6 num_examples: 38740 download_size: 74278694994 dataset_size: 74042325765.436 - config_name: gu features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 55793674372.628 num_examples: 329931 - name: validation num_bytes: 6293796356.189 num_examples: 40773 - name: test num_bytes: 7165218289.408 num_examples: 40853 download_size: 78346523702 dataset_size: 69252689018.225 - config_name: hi features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 213699256456.296 num_examples: 877604 - name: validation num_bytes: 27583551082.248 num_examples: 110692 - name: test num_bytes: 25110580660.236 num_examples: 108492 download_size: 269912939092 dataset_size: 266393388198.78 - config_name: kn features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 54770494386.876 num_examples: 278766 - name: validation num_bytes: 7864058142.98 num_examples: 34726 - name: test num_bytes: 7572538417.28 num_examples: 35166 download_size: 74257809304 dataset_size: 70207090947.136 - config_name: ml features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 71262913087.942 num_examples: 467414 - name: validation num_bytes: 7751159979.48 num_examples: 58985 - name: test num_bytes: 8930337765.4 num_examples: 59230 download_size: 99439381074 dataset_size: 87944410832.82199 - config_name: mr features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 125894833883.753 num_examples: 505639 - name: validation num_bytes: 14280421505.308 num_examples: 63407 - name: test num_bytes: 15230198579.815 num_examples: 63397 download_size: 147608513634 dataset_size: 155405453968.876 - config_name: pa features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 11549437955.164 num_examples: 41874 - name: validation num_bytes: 1317876276.359 num_examples: 5311 - name: test num_bytes: 1453641173.132 num_examples: 5139 download_size: 13966090670 dataset_size: 14320955404.654999 - config_name: sa features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 6219394271.104 num_examples: 11532 - name: validation num_bytes: 718650848.144 num_examples: 1408 - name: test num_bytes: 752806235.026 num_examples: 1474 download_size: 7321556791 dataset_size: 7690851354.274 - config_name: ta features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 101739123587.681 num_examples: 429417 - name: validation num_bytes: 12903430948.456 num_examples: 54012 - name: test num_bytes: 12724306851.984 num_examples: 53827 download_size: 126128595816 dataset_size: 127366861388.12099 - config_name: te features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 33158344172.292 num_examples: 155322 - name: validation num_bytes: 4085414503.579 num_examples: 19501 - name: test num_bytes: 4173443926.076 num_examples: 19189 download_size: 43278403108 dataset_size: 41417202601.94701 configs: - config_name: bn data_files: - split: train path: data/bn/train-* - split: validation path: data/bn/validation-* - split: test path: data/bn/test-* - config_name: gu data_files: - split: train path: data/gu/train-* - split: validation path: data/gu/validation-* - split: test path: data/gu/test-* - config_name: hi data_files: - split: train path: data/hi/train-* - split: validation path: data/hi/validation-* - split: test path: data/hi/test-* - config_name: kn data_files: - split: train path: data/kn/train-* - split: validation path: data/kn/validation-* - split: test path: data/kn/test-* - config_name: ml data_files: - split: train path: data/ml/train-* - split: validation path: data/ml/validation-* - split: test path: data/ml/test-* - config_name: mr data_files: - split: train path: data/mr/train-* - split: validation path: data/mr/validation-* - split: test path: data/mr/test-* - config_name: pa data_files: - split: train path: data/pa/train-* - split: validation path: data/pa/validation-* - split: test path: data/pa/test-* - config_name: sa data_files: - split: train path: data/sa/train-* - split: validation path: data/sa/validation-* - split: test path: data/sa/test-* - config_name: ta data_files: - split: train path: data/ta/train-* - split: validation path: data/ta/validation-* - split: test path: data/ta/test-* - config_name: te data_files: - split: train path: data/te/train-* - split: validation path: data/te/validation-* - split: test path: data/te/test-* tags: - audio - transcription - AI4Bharat - shrutilipi ---

NousResearch/hermes-function-calling-v1

NousResearch

2024-08-30T06:07:08Z

2,284

290

[ "task_categories:text-generation", "task_categories:question-answering", "task_categories:feature-extraction", "language:en", "license:apache-2.0", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[ "text-generation", "question-answering", "feature-extraction" ]

2024-08-14T01:22:36Z

null

--- license: apache-2.0 task_categories: - text-generation - question-answering - feature-extraction language: - en configs: - config_name: func_calling_singleturn data_files: "func-calling-singleturn.json" default: true - config_name: func_calling data_files: "func-calling.json" - config_name: glaive_func_calling data_files: "glaive-function-calling-5k.json" - config_name: json_mode_agentic data_files: "json-mode-agentic.json" - config_name: json_mode_singleturn data_files: "json-mode-singleturn.json" --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/nNQdNcHw9tvQw0AIPGW_i.png) # Hermes Function-Calling V1 This dataset is the compilation of structured output and function calling data used in the Hermes 2 Pro series of models. This repository contains a structured output dataset with function-calling conversations, json-mode, agentic json-mode and structured extraction samples, designed to train LLM models in performing function calls and returning structured output based on natural language instructions. The dataset features various conversational scenarios where AI agents are required to interpret queries and execute appropriate single or multiple function calls. The synthetic data generation was led by @interstellarninja in collaboration with @NousResearch, @teknium, @THEODOROS and many others who provided guidance. ## Hermes Function Calling Standard Hermes Function-calling Standard enables creation of LLM agents that are capable of executing API calls directly from user instructions. For instance, when asked to "find a flight from New York to Los Angeles for next Friday," a function-calling agent can interpret the request, generate the necessary function call (e.g., `search_flights`), and return the results. These agents significantly enhance the utility of AI by enabling direct interactions with APIs, making them invaluable in digital assistants across various domains. For a complete useage guide of models trained on this data, see our github repo: https://github.com/NousResearch/Hermes-Function-Calling ## Repository Structure There are 5 datasets that comprise the datamix, **func-calling-singleturn.json** - Single turn function calls **func-calling.json** - Multi-turn conversation function calls **glaive-function-calling-5k.json** - Updated and cleaned Glaive Function Calling 5k dataset by Glaive AI **json-mode-agentic.json** - Advanced JSON structured output samples **json-mode-singleturn.json** - Single turn JSON structured output samples The dataset has the following configs, used to load each file: - ```func_calling_singleturn``` - ```func_calling``` - ```glaive_func_calling``` - ```json_mode_agentic``` - ```json_mode_singleturn``` ### Inference Example Output Here's an example of the inference output: ``` <|im_start|>user Fetch the stock fundamentals data for Tesla (TSLA)<|im_end|> <|im_start|>assistant <tool_call> {'arguments': {'symbol': 'TSLA'}, 'name': 'get_stock_fundamentals'} </tool_call><|im_end|> <|im_start|>tool <tool_response> {"name": "get_stock_fundamentals", "content": {'symbol': 'TSLA', 'company_name': 'Tesla, Inc.', 'sector': 'Consumer Cyclical', 'industry': 'Auto Manufacturers', 'market_cap': 611384164352, 'pe_ratio': 49.604652, 'pb_ratio': 9.762013, 'dividend_yield': None, 'eps': 4.3, 'beta': 2.427, '52_week_high': 299.29, '52_week_low': 152.37}} </tool_response> <|im_end|> <|im_start|>assistant The stock fundamentals data for Tesla (TSLA) are as follows: - **Symbol**: TSLA - **Company Name**: Tesla, Inc. - **Sector**: Consumer Cyclical - **Industry**: Auto Manufacturers - **Market Capitalization**: $566,160,130,480 - **Forward Price-to-Earnings Ratio (PE Ratio)**: 42.73 - **Price-to-Book Ratio (PB Ratio)**: 9.04 - **Dividend Yield**: N/A - **Trailing Earnings Per Share (EPS)**: $4.3 - **Beta Value of the Stock**: 2.42 - **52-Week High Price of the Stock**: $299.29 - **52-Week Low Price of the Stock**: $152.37 This information provides a snapshot of Tesla's financial position and performance based on the fundamental data obtained from the yfinance API. It shows that Tesla has a substantial market capitalization and a relatively high P/E and P/B ratio compared to other stocks in its industry. The company does not pay a dividend at the moment, which is reflected by a 'Dividend Yield' of 'None'. The Beta value indicates that Tesla's stock has a moderate level of volatility relative to the market. The 52-week high and low prices give an idea of the stock's range over the past year. This data can be useful when assessing investment opportunities and making investment decisions. ``` ### Hermes-2 Pro Prompt Format for Function Calling Our model was trained on specific system prompts and structures for Function Calling. You should use the system role with this message, followed by a function signature json as this example shows here. ``` <|im_start|>system You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: <tools> [{'type': 'function', 'function': {'name': 'get_stock_fundamentals', 'description': 'Get fundamental data for a given stock symbol using yfinance API.', 'parameters': {'type': 'object', 'properties': {'symbol': {'type': 'string'}}, 'required': ['symbol']}}}] </tools> Use the following pydantic model json schema for each tool call you will make: {'title': 'FunctionCall', 'type': 'object', 'properties': {'arguments': {'title': 'Arguments', 'type': 'object'}, 'name': {'title': 'Name', 'type': 'string'}}, 'required': ['arguments', 'name']} For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call> {'arguments': <args-dict>, 'name': <function-name>} </tool_call><|im_end|> ``` To complete the function call, create a user prompt that follows the above system prompt, like so: ``` <|im_start|>user Fetch the stock fundamentals data for Tesla (TSLA)<|im_end|> ``` The model will then generate a tool call, which your inference code must parse, and plug into a function ``` <|im_start|>assistant <tool_call> {'arguments': {'symbol': 'TSLA'}, 'name': 'get_stock_fundamentals'} </tool_call><|im_end|> ``` Once you parse the tool call, call the api and get the returned values for the call, and pass it back in as a new role, tool like so: ``` <|im_start|>tool <tool_response> {"name": "get_stock_fundamentals", "content": {'symbol': 'TSLA', 'company_name': 'Tesla, Inc.', 'sector': 'Consumer Cyclical', 'industry': 'Auto Manufacturers', 'market_cap': 611384164352, 'pe_ratio': 49.604652, 'pb_ratio': 9.762013, 'dividend_yield': None, 'eps': 4.3, 'beta': 2.427, '52_week_high': 299.29, '52_week_low': 152.37}} </tool_response> <|im_end|> ``` The assistant will then read in that data from the function's response, and generate a natural language response: ``` <|im_start|>assistant The stock fundamentals data for Tesla (TSLA) are as follows: - **Symbol**: TSLA - **Company Name**: Tesla, Inc. - **Sector**: Consumer Cyclical - **Industry**: Auto Manufacturers - **Market Capitalization**: $566,160,130,480 - **Forward Price-to-Earnings Ratio (PE Ratio)**: 42.73 - **Price-to-Book Ratio (PB Ratio)**: 9.04 - **Dividend Yield**: N/A - **Trailing Earnings Per Share (EPS)**: $4.3 - **Beta Value of the Stock**: 2.42 - **52-Week High Price of the Stock**: $299.29 - **52-Week Low Price of the Stock**: $152.37 This information provides a snapshot of Tesla's financial position and performance based on the fundamental data obtained from the yfinance API. It shows that Tesla has a substantial market capitalization and a relatively high P/E and P/B ratio compared to other stocks in its industry. The company does not pay a dividend at the moment, which is reflected by a 'Dividend Yield' of 'None'. The Beta value indicates that Tesla's stock has a moderate level of volatility relative to the market. The 52-week high and low prices give an idea of the stock's range over the past year. This data can be useful when assessing investment opportunities and making investment decisions.<|im_end|> ``` ### Prompt Format for JSON Mode / Structured Outputs Our model was also trained on a specific system prompt for Structured Outputs, which should respond with only a json object response, in a specific json schema. Your schema can be made from a pydantic object using our codebase, with the standalone script jsonmode.py available here: https://github.com/NousResearch/Hermes-Function-Calling/tree/main ``` <|im_start|>system You are a helpful assistant that answers in JSON. Here's the json schema you must adhere to:\n<schema>\n{schema}\n</schema><|im_end|> ``` Given the {schema} that you provide, it should follow the format of that json to create it's response, all you have to do is give a typical user prompt, and it will respond in JSON. ## Dataset Structure The dataset follows a sharegpt structure. This means it is a list of dictionaries, with each dictionary containing a new list of dicts called conversations. Each turn in a conversation has two dictionaries, a "from" field, which denotes the role of that turn, and a "value" field which contains the actual text. ### Tool-use with API response: ```json { "id": "753d8365-0e54-43b1-9514-3f9b819fd31c", "conversations": [ { "from": "system", "value": "You are a function calling AI model. You are provided with function signatures within <tools> </tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions.\n<tools>\n[{'type': 'function', 'function': {'name': 'search_flights', 'description': 'Searches for flights based on departure and destination cities, dates, class, and other preferences.', 'parameters': {'type': 'object', 'properties': {'departure_city': {'type': 'string', 'description': 'The city from which the flight will depart.'}, 'destination_city': {'type': 'string', 'description': 'The destination city for the flight.'}, 'departure_date': {'type': 'string', 'description': 'The departure date for the flight.', 'format': 'date'}, 'return_date': {'type': 'string', 'description': 'The return date for the flight.', 'format': 'date'}, 'class': {'type': 'string', 'description': 'The class of the flight ticket.', 'enum': ['economy', 'business', 'first']}, 'flexible_cancellation': {'type': 'boolean', 'description': 'Indicates if the search should filter for flights with flexible cancellation policies.'}}, 'required': ['departure_city', 'destination_city', 'departure_date', 'return_date', 'class']}}}]\n</tools>\nFor each function call return a json object with function name and arguments within <tool_call> </tool_call> tags with the following schema:\n<tool_call>\n{'arguments': <args-dict>, 'name': <function-name>}\n</tool_call>\n" }, { "from": "human", "value": "I'm planning a kayaking trip and looking to book flights from Los Angeles to Auckland. My departure is scheduled for July 10th, 2023, and I intend to return on July 24th, 2023. I would prefer to travel in economy class and would also like the option to have flexible cancellation policies for the tickets due to the uncertain nature of outdoor activities. Could you please search for flights that meet these criteria and provide me with the available options?" }, { "from": "gpt", "value": "<tool_call>\n{'arguments': {'departure_city': 'Los Angeles', 'destination_city': 'Auckland', 'departure_date': '2023-07-10', 'return_date': '2023-07-24', 'class': 'economy', 'flexible_cancellation': True}, 'name': 'search_flights'}\n</tool_call>\n" }, { "from": "tool", "value": "<tool_response>\n{\"name\": \"search_flights\", \"content\": {\"flights\": [{\"flight_number\": \"AA118\", \"airline\": \"American Airlines\", \"departure_airport\": \"LAX\", \"departure_city\": \"Los Angeles\", \"departure_date\": \"2023-07-10T08:00:00Z\", \"departure_terminal\": \"4\", \"arrival_airport\": \"AKL\", \"arrival_city\": \"Auckland\", \"arrival_date\": \"2023-07-11T14:25:00Z\", \"arrival_terminal\": \"I\", \"duration\": \"17h 25m\", \"class\": \"Economy\", \"price\": \"$950\", \"flexible_cancellation\": true}, {\"flight_number\": \"UA796\", \"airline\": \"United Airlines\", \"departure_airport\": \"LAX\", \"departure_city\": \"Los Angeles\", \"departure_date\": \"2023-07-10T11:30:00Z\", \"departure_terminal\": \"7\", \"arrival_airport\": \"AKL\", \"arrival_city\": \"Auckland\", \"arrival_date\": \"2023-07-11T18:30:00Z\", \"arrival_terminal\": \"I\", \"duration\": \"16h 0m\", \"class\": \"Economy\", \"price\": \"$975\", \"flexible_cancellation\": true}, {\"flight_number\": \"NZ6\", \"airline\": \"Air New Zealand\", \"departure_airport\": \"LAX\", \"departure_city\": \"Los Angeles\", \"departure_date\": \"2023-07-10T21:00:00Z\", \"departure_terminal\": \"B\", \"arrival_airport\": \"AKL\", \"arrival_city\": \"Auckland\", \"arrival_date\": \"2023-07-12T05:20:00Z\", \"arrival_terminal\": \"I\", \"duration\": \"13h 20m\", \"class\": \"Economy\", \"price\": \"$1020\", \"flexible_cancellation\": true}], \"return_flights\": [{\"flight_number\": \"AA117\", \"airline\": \"American Airlines\", \"departure_airport\": \"AKL\", \"departure_city\": \"Auckland\", \"departure_date\": \"2023-07-24T14:10:00Z\", \"departure_terminal\": \"I\", \"arrival_airport\": \"LAX\", \"arrival_city\": \"Los Angeles\", \"arrival_date\": \"2023-07-24T06:00:00Z\", \"arrival_terminal\": \"4\", \"duration\": \"12h 50m\", \"class\": \"Economy\", \"price\": \"$950\", \"flexible_cancellation\": true}, {\"flight_number\": \"UA797\", \"airline\": \"United Airlines\", \"departure_airport\": \"AKL\", \"departure_city\": \"Auckland\", \"departure_date\": \"2023-07-24T19:15:00Z\", \"departure_terminal\": \"I\", \"arrival_airport\": \"LAX\", \"arrival_city\": \"Los Angeles\", \"arrival_date\": \"2023-07-24T10:15:00Z\", \"arrival_terminal\": \"7\", \"duration\": \"11h 0m\", \"class\": \"Economy\", \"price\": \"$975\", \"flexible_cancellation\": true}, {\"flight_number\": \"NZ5\", \"airline\": \"Air New Zealand\", \"departure_airport\": \"AKL\", \"departure_city\": \"Auckland\", \"departure_date\": \"2023-07-24T19:30:00Z\", \"departure_terminal\": \"I\", \"arrival_airport\": \"LAX\", \"arrival_city\": \"Los Angeles\", \"arrival_date\": \"2023-07-24T10:30:00Z\", \"arrival_terminal\": \"B\", \"duration\": \"12h 0m\", \"class\": \"Economy\", \"price\": \"$1020\", \"flexible_cancellation\": true}]}}\n</tool_response>\n" }, { "from": "gpt", "value": "Here are the available flight options with flexible cancellation policies for your kayaking trip from Los Angeles to Auckland:\n\n**Outbound Flights (Los Angeles to Auckland):**\n1. **American Airlines AA118**\n - Departure: July 10, 2023, 8:00 AM from LAX Terminal 4\n - Arrival: July 11, 2023, 2:25 PM at AKL Terminal I\n - Duration: 17h 25m\n - Price: $950\n\n2. **United Airlines UA796**\n - Departure: July 10, 2023, 11:30 AM from LAX Terminal 7\n - Arrival: July 11, 2023, 6:30 PM at AKL Terminal I\n - Duration: 16h 0m\n - Price: $975\n\n3. **Air New Zealand NZ6**\n - Departure: July 10, 2023, 9:00 PM from LAX Terminal B\n - Arrival: July 12, 2023, 5:20 AM at AKL Terminal I\n - Duration: 13h 20m\n - Price: $1020\n\n**Return Flights (Auckland to Los Angeles):**\n1. **American Airlines AA117**\n - Departure: July 24, 2023, 2:10 PM from AKL Terminal I\n - Arrival: July 24, 2023, 6:00 AM at LAX Terminal 4\n - Duration: 12h 50m\n - Price: $950\n\n2. **United Airlines UA797**\n - Departure: July 24, 2023, 7:15 PM from AKL Terminal I\n - Arrival: July 24, 2023, 10:15 AM at LAX Terminal 7\n - Duration: 11h 0m\n - Price: $975\n\n3. **Air New Zealand NZ5**\n - Departure: July 24, 2023, 7:30 PM from AKL Terminal I\n - Arrival: July 24, 2023, 10:30 AM at LAX Terminal B\n - Duration: 12h 0m\n - Price: $1020\n\nAll the listed prices are in economy class with flexible cancellation policies. Please review the options and let me know if you would like to proceed with booking any of these flights or if you need further assistance." } ], "category": "E-commerce Platforms", "subcategory": "Kayak", "task": "Flight Search" } ``` ### Structured extraction with function-calling: ```json { "id": "afba1f0c-3212-467d-8594-d72a06da4d05", "conversations": [ { "from": "system", "value": "You are an expert structured information extraction AI model. You will be provided with documents to extract information from. You are also provided with the json schema to output extracted information in the function signatures within XML tags <tools></tools>. Don't make assumptions about what values to plug into json schema. \n<tools>\n[{\"type\": \"function\", \"function\": {\"name\": \"ExpertQAExtractor\", \"description\": \"Extracts a list of questions that ask how a concept or information from the document can be applied to a real-world situation. These assess ability to apply knowledge.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"application_questions\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"required\": [\"application_questions\"]}}}]\n</tools>\nFor each extraction function call return a json object with function name and arguments followed by a <tool_call> tag with the following schema:\n<tool_call>\n{'arguments': <args-dict>, 'name': <function-name>}\n</tool_call>" }, { "from": "human", "value": "Can you help me extract queries from the following passage <passage> : A directed graph. \n weighted, by the way. If a pair of vertices in such a graph is attached \"both ways,\" then each of the two edges will have its own weight. \n Washington, DC \n Fredericksburg \n Richmond \n Virginia Beach \n 50 \n 60 100 \n 150 \n Figure 5.3: A weighted (and undirected) graph. \n**adjacent.** If two vertices have an edge between them, they are said to be adjacent. \n**connected.** The word **connected** has two meanings: it applies both to pairs of vertices and to entire graphs. We say that two vertices are connected if there is at least one path between them. Each vertex is therefore \"reachable\" from the other. In Figure 5.1, President and actor are connected, but Ford's Theatre and Civil War are not. \"Connected\" is also used to describe entire graphs, if _every_ node can be reached from all others. It's easy to see that Fig\n90 CHAPTER 5. STRUCTURES \n ure 5.3 is a connected graph, whereas Figure 5.1 is not (because Civil War and Gettysburg are isolated from the other nodes). It's not always trivial to determine whether a graph is connected, however: imagine a tangled morass of a million vertices, with ten million edges, and having to figure out whether or not every vertex is reachable from every other. (And if that seems unrealistically large, consider Facebook, which has over a billion nodes.) \n**degree.** A vertex's degree is simply the number of edges that connect to it. Virginia Beach has degree 2, and Fredericksburg \n3. In the case of a directed graph, we sometimes distinguish between the number of incoming arrows a vertex has (called its **in-degree** ) and the number of outgoing arrows (the **out- degree** ). Muhammad Ali had a higher out-degree (3) than in-degree (1) since he won most of the time. \n**cycle.** A cycle is a path that begins and ends at the same vertex.^2 In Figure 5.3, Richmond-to-Virginia Beach-to-Fredericksburgto-Richmond is a cycle. Any loop is a cycle all by itself. For directed graphs, the entire loop must comprise edges in the \"forward\" direction: no fair going backwards. In Figure 5.2, Frazier-to-Ali-to-Foreman-to-Frazier is a cycle, as is the simpler Ali-to-Frazier-to-Ali. \n**DAG (directed, acyclic graph).** One common use of graphs is to represent flows of dependencies, for instance the prerequisites that different college courses have for one another. Another example is project management workflows: the tasks needed to complete a project become vertices, and then the dependencies they have on one another become edges. The graph in Figure 5.4 shows the steps in making a batch of brownies, and how these steps depend on each other. The eggs have to be cracked before the ingredients can be mixed, \n(^2) We'll also say that a cycle can't repeat any edges or vertices along the way, so that it can't go back and forth repeatedly and pointlessly between two adjacent nodes. Some mathematicians call this a **simple cycle** to distinguish it from the more general **cycle** , but we'll just say that no cycles can repeat like this. \n5.1. GRAPHS 91 \n and the oven has to be preheated before baking, but the pan can be greased any old time, provided that it's done before pouring the brown goop into it. \n mix ingredients \n pour brown stuff in bowl \n crack two eggs measure 2 tbsp oil \n preheat oven \n bake for 30 mins \n grease pan \n pour into pan \n cool \n enjoy! \n Figure 5.4: A DAG. \n A graph of dependencies like this must be both directed and acyclic , or it wouldn't make sense. Directed, of course, means that task X can require task Y to be completed before it, without the reverse also being true. If they both depended on each other, we'd have an infinite loop, and no brownies could ever get baked! Acyclic means that no kind of cycle can exist in the graph, even one that goes through multiple vertices. Such a cycle would again result in an infinite loop, making the project hopeless. Imagine if there were an arrow from bake for 30 mins back to grease pan in Figure 5.4. Then, we'd have to grease the pan before pouring the goop into it, and we'd have to pour the goop before baking, but we'd also have to bake before greasing the pan! We'd be stuck right off the bat: there'd be no way to complete any of those tasks since they'd all indirectly depend on each other. A graph that is both directed and acyclic (and therefore free of these problems) is sometimes called a DAG for short. \n92 CHAPTER 5. STRUCTURES \n**Spatial positioning** \nOne important thing to understand about graphs is which aspects of a diagram are relevant. Specifically, _the spatial positioning of the vertices doesn't matter._ In Figure 5.2 we drew Muhammad Ali in the mid-upper left, and Sonny Liston in the extreme upper right. But this was an arbitrary choice, and irrelevant. More specifically, this isn't part of the information the diagram claims to represent. We could have positioned the vertices differently, as in Figure 5.5, and had _the same graph_. In both diagrams, there are the same vertices, and the same edges between them (check me). Therefore, these are mathematically the same graph. \nGeorge Foreman Sonny Liston (^) Muhammad Ali Joe Frazier Figure 5.5: A different look to **the same graph as Figure 5.2**. This might not seem surprising for the prize fighter graph, but for graphs like the MapQuest graph, which actually represent physical locations, it can seem jarring. In Figure 5.3 we could have drawn Richmond north of Fredericksburg, and Virginia Beach on the far west side of the diagram, and still had the same graph, provided that all the nodes and links were the same. Just remember that the spatial positioning is designed for human convenience, and isn't part of the mathematical information. It's similar to how there's no order to the elements of a set, even though when we specify a set extensionally, we have to list them in _some_ order to avoid writing all the element names on top of each other. On a graph diagram, we have to draw each vertex _somewhere_ , but where we put it is simply aesthetic. \n5.1. GRAPHS 93 \n**Relationship to sets** \nWe seem to have strayed far afield from sets with all this graph stuff. But actually, there are some important connections to be made to those original concepts. Recall the wizards set A from chapter 3 that we extended to contain { Harry, Ron, Hermione, Neville }. Now consider the following endorelation on A: \n (Harry, Ron) (Ron, Harry) (Ron, Hermione) (Ron, Neville) (Hermione, Hermione) (Neville, Harry) \nThis relation, and all it contains, is represented faithfully by the graph in Figure 5.6. The elements of A are the vertices of course, and each ordered pair of the relation is reflected in an edge of the graph. Can you see how _exactly_ the same information is represented by both forms? \n Hermione \n Ron Neville \n Harry \n Figure 5.6: A graph depicting a endorelation. \nFigure 5.6 is a directed graph, of course. What if it were an undirected graph? The answer is that the corresponding relation would be _symmetric_. An undirected graph implies that if there's an edge between two vertices, it goes \"both ways.\" This is really identical to saying a relation is symmetric: if an (x, y) is in the relation, then the corresponding (y, x) must also be. An example is Figure 5.7, which depicts the following symmetric relation: \n94 CHAPTER 5. STRUCTURES \n (Harry, Ron) (Ron, Harry) (Ron, Hermione) (Hermione, Ron) (Harry, Harry) (Neville, Neville) \n Harry Ron \n Hermione Neville \n Figure 5.7: A graph depicting a symmetric endorelation. \nNotice how the loops (edges from a node back to itself) in these diagrams represent ordered pairs in which both elements are the same. \nAnother connection between graphs and sets has to do with partitions. Figure 5.7 was not a connected graph: Neville couldn't be reached from any of the other nodes. Now consider: isn't a graph like this similar in some ways to a _partition_ of A -- namely, this one? \n { Harry, Ron, Hermione } and { Neville }. \nWe've simply partitioned the elements of A into the groups that are connected. If you remove the edge between Harry and Ron in that graph, you have: \n { Harry }, { Ron, Hermione }, and { Neville }. \nThen add one between Hermione and Neville, and now you have: \n5.1. GRAPHS 95 \n { Harry } and { Ron, Hermione, Neville }. \nIn other words, the \"connectedness\" of a graph can be represented precisely as a partition of the set of vertices. Each connected subset is in its own group, and every vertex is in one and only one group: therefore, these isolated groups are mutually exclusive and collectively exhaustive. Cool. \n**Graph traversal** \nIf you had a long list -- perhaps of phone numbers, names, or purchase orders -- and you needed to go through and do something to each element of the list -- dial all the numbers, scan the list for a certain name, add up all the orders -- it'd be pretty obvious how to do it. You just start at the top and work your way down. It might be tedious, but it's not confusing. \nIterating through the elements like this is called **traversing** the data structure. You want to make sure you encounter each element once (and only once) so you can do whatever needs to be done with it. It's clear how to traverse a list. But how to traverse a graph? There is no obvious \"first\" or \"last\" node, and each one is linked to potentially many others. And as we've seen, the vertices might not even _be_ fully connected, so a traversal path through all the nodes might not even exist. \nThere are two different ways of traversing a graph: breadth-first, and depth-first. They provide different ways of exploring the nodes, and as a side effect, each is able to discover whether the graph is connected or not. Let's look at each in turn. \n**Breadth-first traversal** \nWith **breadth-first traversal** , we begin at a starting vertex (it doesn't matter which one) and explore the graph cautiously and delicately. We probe equally deep in all directions, making sure we've looked a little ways down each possible path before exploring each of those paths a little further. \n96 CHAPTER 5. STRUCTURES \nTo do this, we use a very simple data structure called a **queue**. A queue is simply a list of nodes that are waiting in line. (In Britain, I'm told, instead of saying \"line up\" at the sandwich shop, they say \"queue up.\") When we enter a node into the queue at the tail end, we call it **enqueueing** the node, and when we remove one from the front, we call it **dequeueing** the node. The nodes in the middle patiently wait their turn to be dealt with, getting closer to the front every time the front node is dequeued. \nAn example of this data structure in action is shown in Figure 5.8. Note carefully that we always insert nodes at one end (on the right) and remove them from the _other_ end (the left). This means that the first item to be enqueued (in this case, the triangle) will be the first to be dequeued. \"Calls will be answered in the order they were received.\" This fact has given rise to another name for a queue: a \" **FIFO** ,\" which stands for \"first-in-first-out.\" \n Start with an empty queue: | Enqueue a triangle, and we have: |^ Enqueue a star, and we have: |^ Enqueue a heart, and we have: |^ Dequeue the triangle, and we have: | Enqueue a club, and we have: | Dequeue the star, and we have: | Dequeue the heart, and we have: | Dequeue the club. We're empty again: | \nFigure 5.8: A queue in action. The vertical bar marks the \"front of the line,\" and the elements are waiting to be dequeued in order from left to right. \nNow here's how we use a queue to traverse a graph breadth-first. We're going to start at a particular node, and put all of its adjacent nodes into a queue. This makes them all safely \"wait in line\" until we get around to exploring them. Then, we repeatedly take the first node in line, do whatever we need to do with it, and then put all of _its_ adjacent nodes in line. We keep doing this until the queue is empty. \n5.1. GRAPHS 97 \nNow it might have occurred to you that we can run into trouble if we encounter the same node multiple times while we're traversing. This can happen if the graph has a cycle: there will be more than one path to reach some nodes, and we could get stuck in an infinite loop if we're not careful. For this reason, we introduce the concept of **marking** nodes. This is kind of like leaving a trail of breadcrumbs: if we're ever about to explore a node, but find out it's marked, then we know we've already been there, and it's pointless to search it again. \nSo there </passage>" }, { "from": "gpt", "value": "<tool_call>\\n{\"arguments\": {\"queries\": ['How can the concept of graph traversal be applied to real-world situations, such as project management workflows or social networks?', 'Can you think of any other real-world scenarios where understanding and effectively using spatial positioning in a graph would be important?', 'In what ways can the concept of marking nodes be useful in problem-solving or decision-making processes?'], \"name\": \"ExpertQAExtractor\"}}\\n</tool_call>" } ], "category": "Information Extraction", "subcategory": "Json Schema", "task": "Structured json schema extaction with function calling" } ``` ### JSON-mode with pydantic JSON schema ```json { "id": "57aa5f1b-6af3-42b8-9598-d48540277354", "conversations": [ { "from": "system", "value": "You are a helpful assistant that answers in JSON. Here's the json schema you must adhere to:\n<schema>\n{'title': 'MovieDatabaseEntry', 'type': 'object', 'properties': {'movieId': {'title': 'Movie ID', 'type': 'string'}, 'title': {'title': 'Title', 'type': 'string'}, 'genre': {'title': 'Genre', 'type': 'string'}, 'director': {'title': 'Director', 'type': 'string'}, 'cast': {'title': 'Cast', 'type': 'array', 'items': {'type': 'string'}}}, 'required': ['movieId', 'title', 'genre', 'director', 'cast']}\n</schema>\n" }, { "from": "human", "value": "I'm organizing a movie database and need to create a structured JSON object for each film entry. The next movie I want to add is 'Interstellar', which has a unique movie ID 'INTSTL_2014'. The genre is 'Science Fiction', directed by Christopher Nolan. The main cast includes Matthew McConaughey as 'Cooper', Anne Hathaway as 'Brand', Jessica Chastain as 'Murph', and Michael Caine as 'Professor Brand'." }, { "from": "gpt", "value": "{\"movieId\": \"INTSTL_2014\", \"title\": \"Interstellar\", \"genre\": \"Science Fiction\", \"director\": \"Christopher Nolan\", \"cast\": [\"Matthew McConaughey as Cooper\", \"Anne Hathaway as Brand\", \"Jessica Chastain as Murph\", \"Michael Caine as Professor Brand\"]}" } ], "category": "Media & Entertainment", "subcategory": "Media", "schema": "{\"title\": \"MovieDatabaseEntry\", \"type\": \"object\", \"properties\": {\"movieId\": {\"title\": \"Movie ID\", \"type\": \"string\"}, \"title\": {\"title\": \"Title\", \"type\": \"string\"}, \"genre\": {\"title\": \"Genre\", \"type\": \"string\"}, \"director\": {\"title\": \"Director\", \"type\": \"string\"}, \"cast\": {\"title\": \"Cast\", \"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"required\": [\"movieId\", \"title\", \"genre\", \"director\", \"cast\"]}", "__index_level_0__": 671 } ``` ### JSON-mode with Agentic Schemas: ```json { "id": "c0618e9a-d832-4e2f-b9fe-28c8d8ca8007", "conversations": [ { "from": "system", "value": "You are a helpful assistant that answers in JSON. Here's the json schema you must adhere to:\n<schema>\n{'type': 'object', 'properties': {'agent_execution': {'type': 'object', 'properties': {'task_decomposition': {'type': 'string'}, 'action_retrieval': {'type': 'string'}, 'code_execution': {'type': 'string'}, 'error_analysis': {'type': 'string'}, 'code_amendment': {'type': 'string'}, 'execution_critique': {'type': 'string'}, 'task_replanning': {'type': 'string'}}, 'required': ['task_decomposition', 'action_retrieval', 'code_execution', 'error_analysis', 'code_amendment', 'execution_critique', 'task_replanning']}}}\n</schema>\n" }, { "from": "human", "value": "As a software engineer, I'm currently working on integrating an agent execution framework into our system to automate complex tasks. I need to generate a JSON object that represents the process of executing a task where the agent decomposes the task into subtasks, retrieves the necessary actions from an action library, and executes code to fulfill the task requirements. The task at hand is to automate the process of data migration from one database to another, ensuring data integrity and handling any errors that may arise. The agent should also provide a critique of the task execution and replan the task if necessary. Specifically, the agent decomposes the data migration task into subtasks such as data extraction, data transformation, and data loading. It then retrieves actions like 'extract_data', 'transform_data', and 'load_data' from the action library. Following this, the agent executes the retrieved code snippets for each subtask, ensuring data integrity throughout the migration process. During this process, the agent analyzes any errors encountered during execution, identifying issues such as data format mismatches or connectivity problems. Based on the error analysis, the agent amends the code to handle exceptions and retry the subtask. After the execution, the agent provides a critique of the execution, highlighting successful completions and areas that required amendments. If a subtask cannot be completed successfully after amendments, the agent replans the task by decomposing it into alternative subtasks, thus ensuring a robust and adaptive approach to task automation." }, { "from": "gpt", "value": "{\"agent_execution\": {\"task_decomposition\": \"The agent decomposes the data migration task into subtasks such as data extraction, data transformation, and data loading.\", \"action_retrieval\": \"The agent retrieves actions like 'extract_data', 'transform_data', and 'load_data' from the action library.\", \"code_execution\": \"The agent executes the retrieved code snippets for each subtask, ensuring data integrity throughout the migration process.\", \"error_analysis\": \"The agent analyzes any errors encountered during execution, identifying issues such as data format mismatches or connectivity problems.\", \"code_amendment\": \"The agent amends the code based on the error analysis to handle exceptions and retry the subtask.\", \"execution_critique\": \"The agent provides a critique of the execution, highlighting successful completions and areas that required amendments.\", \"task_replanning\": \"If a subtask cannot be completed successfully after amendments, the agent replans the task by decomposing it into alternative subtasks.\"}}" } ], "category": "Copilot Frameworks", "subcategory": "Agent Execution", "schema": "{\"type\": \"object\", \"properties\": {\"agent_execution\": {\"type\": \"object\", \"properties\": {\"task_decomposition\": {\"type\": \"string\"}, \"action_retrieval\": {\"type\": \"string\"}, \"code_execution\": {\"type\": \"string\"}, \"error_analysis\": {\"type\": \"string\"}, \"code_amendment\": {\"type\": \"string\"}, \"execution_critique\": {\"type\": \"string\"}, \"task_replanning\": {\"type\": \"string\"}}, \"required\": [\"task_decomposition\", \"action_retrieval\", \"code_execution\", \"error_analysis\", \"code_amendment\", \"execution_critique\", \"task_replanning\"]}}}" } ``` # How to cite: ```bibtext @misc{Hermes-Function-Calling-Dataset-V1, url={https://huggingface.co/NousResearch/hermes-function-calling-v1}, c title={Hermes-Function-Calling-Dataset-V1}, author={"interstellarninja", "Teknium"} } ```

Major-TOM/Core-S1RTC

Major-TOM

2024-08-29T16:17:53Z

27,095

12

[ "license:cc-by-sa-4.0", "size_categories:1M<n<10M", "format:parquet", "modality:image", "modality:tabular", "modality:text", "modality:geospatial", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2402.12095", "region:us", "earth-observation", "remote-sensing", "sentinel-1", "sar", "synthethic-aperture-radar", "satellite", "geospatial" ]

[]

2024-03-15T14:13:22Z

null

--- license: cc-by-sa-4.0 tags: - earth-observation - remote-sensing - sentinel-1 - sar - synthethic-aperture-radar - satellite - geospatial size_categories: - 1M<n<10M dataset_info: - config_name: default features: - name: product_id dtype: string - name: grid_cell dtype: string - name: product_datetime dtype: string - name: thumbnail dtype: image - name: vv dtype: binary - name: vh dtype: binary configs: - config_name: default data_files: images/*.parquet - config_name: metadata data_files: metadata.parquet --- # Core-S1RTC Contains a global coverage of Sentinel-1 (RTC) patches, each of size 1,068 x 1,068 pixels. | Source | Sensing Type | Number of Patches | Patch Size | Total Pixels | |--------|--------------|-------------------|------------|--------------| |Sentinel-1 RTC | Synthetic Aperture Radar |1,469,955|1,068 x 1,068 (10 m) | > 1.676 Trillion | ## Content | Column | Details | Resolution | |--------|---------|------------| | VV | Received Linear Power in the VV Polarization | 10m | | VH | Received Linear Power in the VV Polarization | 10m | | thumbnail | Rescaled false colour<sup>1</sup> saved as png | 10m | <sup>1</sup> False colour composites are made with decibel-scale values with red green and blue defined as ```R:VV G:VV+VH B:VH```. For each channel, a contrast-stretch is applied, transforming minimum-maximum to 0-255. This means bluer areas have relatively higher VH values, whilst brightness is a function of overall intensity. This is relative within each thumbnail because of the normalisation, and so cannot be compared across different samples. ## Spatial Coverage This is a global monotemporal dataset. Nearly every piece of Earth captured by Sentinel-1 is contained at least once in this dataset (and only once, excluding some marginal overlaps). The coverage is about 35% lower than for Core Sentinel-2 dataset due to the sensor coverage limitations. The following figure demonstrates the spatial coverage (only black pixels are absent): ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6304c06eeb6d777a838eab63/hFf98OyO25mL9Q962d5NL.png) ## Example Use Interface scripts are available at https://github.com/ESA-PhiLab/Major-TOM Here's a sneak peek with a thumbnail image: ```python from fsspec.parquet import open_parquet_file import pyarrow.parquet as pq from io import BytesIO from PIL import Image PARQUET_FILE = 'part_03900' # parquet number ROW_INDEX = 42 # row number (about 500 per parquet) url = "https://huggingface.co/datasets/Major-TOM/Core-S1RTC/resolve/main/images/{}.parquet".format(PARQUET_FILE) with open_parquet_file(url,columns = ["thumbnail"]) as f: with pq.ParquetFile(f) as pf: first_row_group = pf.read_row_group(ROW_INDEX, columns=['thumbnail']) stream = BytesIO(first_row_group['thumbnail'][0].as_py()) image = Image.open(stream) ``` ## Cite [![arxiv](https://img.shields.io/badge/Open_Access-arxiv:2402.12095-b31b1b)](https://arxiv.org/abs/2402.12095/) ```latex @inproceedings{Major_TOM, title={Major TOM: Expandable Datasets for Earth Observation}, author={Alistair Francis and Mikolaj Czerkawski}, year={2024}, booktitle={IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium}, eprint={2402.12095}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` Powered by [Φ-lab, European Space Agency (ESA) 🛰️](https://huggingface.co/ESA-philab)

Omartificial-Intelligence-Space/Arab3M-Triplets

Omartificial-Intelligence-Space

2024-08-29T12:10:58Z

45

4

[ "task_categories:sentence-similarity", "language:ar", "license:apache-2.0", "size_categories:1M<n<10M", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "STS", "Embeddings", "Arabic" ]

[ "sentence-similarity" ]

2024-07-11T10:24:46Z

3

--- license: apache-2.0 extra_gated_fields: Name: text Affilation: text Company: text Country: country Specific date: date_picker I want to use this dataset for: type: select options: - Research - Education - label: Other value: other I agree to use this dataset for non-commercial use ONLY: checkbox task_categories: - sentence-similarity language: - ar tags: - STS - Embeddings - Arabic pretty_name: Arab3M-Triplets size_categories: - 1M<n<10M --- # Arab3M-Triplets This dataset is designed for training and evaluating models using contrastive learning techniques, particularly in the context of natural language understanding. The dataset consists of triplets: an anchor sentence, a positive sentence, and a negative sentence. The goal is to encourage models to learn meaningful representations by distinguishing between semantically similar and dissimilar sentences. ## Dataset Overview - **Format**: Parquet - **Number of rows**: 3.03 million - **Columns**: - `anchor`: A sentence serving as the reference point. - `positive`: A sentence that is semantically similar to the `anchor`. - `negative`: A sentence that is semantically dissimilar to the `anchor`. ## Usage This dataset can be used to train models for various NLP tasks, including: - **Sentence Similarity**: Training models to identify sentences with similar meanings. - **Contrastive Learning**: Teaching models to differentiate between semantically related and unrelated sentences. - **Representation Learning**: Developing models that learn robust sentence embeddings. ### Loading the Dataset You can load the dataset using the Hugging Face `datasets` library: ```python from datasets import load_dataset dataset = load_dataset('Omartificial-Intelligence-Space/Arab3M-Triplets') ```

fixie-ai/covost2

fixie-ai

2024-08-27T20:58:08Z

17,492

2

[ "size_categories:1M<n<10M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-07-16T23:40:52Z

null

--- dataset_info: - config_name: ar_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 61607709.192 num_examples: 2283 - name: validation num_bytes: 56223234.024 num_examples: 1758 - name: test num_bytes: 54650910.41 num_examples: 1695 download_size: 160468333 dataset_size: 172481853.626 - config_name: ca_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 4397026262.322 num_examples: 95854 - name: validation num_bytes: 544108371.96 num_examples: 12730 - name: test num_bytes: 604755238.63 num_examples: 12730 download_size: 4957773433 dataset_size: 5545889872.912 - config_name: cy_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 51478765.087 num_examples: 1241 - name: validation num_bytes: 26992697.0 num_examples: 690 - name: test num_bytes: 28772216.0 num_examples: 690 download_size: 102604972 dataset_size: 107243678.087 - config_name: de_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 5680326209.222 num_examples: 127834 - name: validation num_bytes: 631442490.202 num_examples: 13511 - name: test num_bytes: 637042944.685 num_examples: 13511 download_size: 6490850158 dataset_size: 6948811644.108999 - config_name: en_ar features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14105902817.18 num_examples: 289430 - name: validation num_bytes: 718527564.808 num_examples: 15531 - name: test num_bytes: 729114452.301 num_examples: 15531 download_size: 13815709729 dataset_size: 15553544834.289001 - config_name: en_ca features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14099092976.18 num_examples: 289430 - name: validation num_bytes: 718171719.808 num_examples: 15531 - name: test num_bytes: 728790610.301 num_examples: 15531 download_size: 13814365593 dataset_size: 15546055306.289001 - config_name: en_cy features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14098487703.18 num_examples: 289430 - name: validation num_bytes: 718141953.808 num_examples: 15531 - name: test num_bytes: 728793811.301 num_examples: 15531 download_size: 13813953593 dataset_size: 15545423468.289001 - config_name: en_de features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14099886814.18 num_examples: 289430 - name: validation num_bytes: 718219105.808 num_examples: 15531 - name: test num_bytes: 728857067.301 num_examples: 15531 download_size: 13815103686 dataset_size: 15546962987.289001 - config_name: en_et features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14096877545.18 num_examples: 289430 - name: validation num_bytes: 718057559.808 num_examples: 15531 - name: test num_bytes: 728710692.301 num_examples: 15531 download_size: 13813410823 dataset_size: 15543645797.289001 - config_name: en_fa features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14108661241.18 num_examples: 289430 - name: validation num_bytes: 718670909.808 num_examples: 15531 - name: test num_bytes: 729271000.301 num_examples: 15531 download_size: 13816798013 dataset_size: 15556603151.289001 - config_name: en_id features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14098627451.18 num_examples: 289430 - name: validation num_bytes: 718144327.808 num_examples: 15531 - name: test num_bytes: 728802322.301 num_examples: 15531 download_size: 13813201260 dataset_size: 15545574101.289001 - config_name: en_ja features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14103911774.18 num_examples: 289430 - name: validation num_bytes: 718409304.808 num_examples: 15531 - name: test num_bytes: 729050991.301 num_examples: 15531 download_size: 13815875328 dataset_size: 15551372070.289001 - config_name: en_lv features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14098703097.18 num_examples: 289430 - name: validation num_bytes: 718152571.808 num_examples: 15531 - name: test num_bytes: 728792572.301 num_examples: 15531 download_size: 13814849886 dataset_size: 15545648241.289001 - 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config_name: it_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 1517510665.568 num_examples: 31698 - name: validation num_bytes: 422409218.1 num_examples: 8940 - name: test num_bytes: 454569171.595 num_examples: 8951 download_size: 2125529183 dataset_size: 2394489055.2630005 - config_name: ja_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 38181610.624 num_examples: 1119 - name: validation num_bytes: 24623052.0 num_examples: 635 - name: test num_bytes: 25558787.0 num_examples: 684 download_size: 88228548 dataset_size: 88363449.624 - config_name: lv_en features: - name: client_id dtype: string - name: file dtype: string - 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name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 216102081.4 num_examples: 7108 - name: validation num_bytes: 55386349.319 num_examples: 1699 - name: test num_bytes: 60219179.711 num_examples: 1699 download_size: 320267264 dataset_size: 331707610.43 - config_name: pt_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 274723273.528 num_examples: 9158 - name: validation num_bytes: 118345891.704 num_examples: 3318 - name: test num_bytes: 166247624.001 num_examples: 4023 download_size: 540891735 dataset_size: 559316789.233 - config_name: ru_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 557219472.672 num_examples: 12112 - name: validation num_bytes: 290218427.6 num_examples: 6110 - name: test num_bytes: 312622838.0 num_examples: 6300 download_size: 1112848246 dataset_size: 1160060738.272 - config_name: sl_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 55992153.0 num_examples: 1843 - name: validation num_bytes: 15074155.0 num_examples: 509 - name: test num_bytes: 10209711.0 num_examples: 360 download_size: 83863293 dataset_size: 81276019.0 - config_name: sv-SE_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 48298330.64 num_examples: 2160 - name: validation num_bytes: 32544646.416 num_examples: 1349 - name: test num_bytes: 46894324.615 num_examples: 1595 download_size: 121860373 dataset_size: 127737301.671 - config_name: ta_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 47757197.616 num_examples: 1358 - name: validation num_bytes: 13670695.0 num_examples: 384 - name: test num_bytes: 29891516.0 num_examples: 786 download_size: 87791516 dataset_size: 91319408.616 - config_name: tr_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 119299427.798 num_examples: 3966 - name: validation num_bytes: 52552534.232 num_examples: 1624 - name: test num_bytes: 59106253.862 num_examples: 1629 download_size: 224018260 dataset_size: 230958215.89200002 - config_name: zh-CN_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: validation num_bytes: 231018998.33 num_examples: 4843 - name: test num_bytes: 243850956.45 num_examples: 4898 - name: train num_bytes: 341425113.6 num_examples: 7085 download_size: 766660661 dataset_size: 816295068.38 configs: - config_name: ar_en data_files: - split: train path: ar_en/train-* - split: validation path: ar_en/validation-* - split: test path: ar_en/test-* - config_name: ca_en data_files: - split: train path: ca_en/train-* - split: validation path: ca_en/validation-* - split: test path: ca_en/test-* - config_name: cy_en data_files: - split: train path: cy_en/train-* - split: validation path: cy_en/validation-* - split: test path: cy_en/test-* - config_name: de_en data_files: - split: train path: de_en/train-* - split: validation path: de_en/validation-* - split: test path: de_en/test-* - config_name: en_ar data_files: - split: train path: en_ar/train-* - split: validation path: en_ar/validation-* - split: test path: en_ar/test-* - config_name: en_ca data_files: - split: train path: en_ca/train-* - split: validation path: en_ca/validation-* - split: test path: en_ca/test-* - config_name: en_cy data_files: - split: train path: en_cy/train-* - split: validation path: en_cy/validation-* - split: test path: en_cy/test-* - config_name: en_de data_files: - split: train path: en_de/train-* - split: validation path: en_de/validation-* - split: test path: en_de/test-* - config_name: en_et data_files: - split: train path: en_et/train-* - split: validation path: en_et/validation-* - split: test path: en_et/test-* - config_name: en_fa data_files: - split: train path: en_fa/train-* - split: validation path: en_fa/validation-* - split: test path: en_fa/test-* - config_name: en_id data_files: - split: train path: en_id/train-* - split: validation path: en_id/validation-* - split: test path: en_id/test-* - config_name: en_ja data_files: - split: train path: en_ja/train-* - split: validation path: en_ja/validation-* - split: test path: en_ja/test-* - config_name: en_lv data_files: - split: train path: en_lv/train-* - split: validation path: en_lv/validation-* - split: test path: en_lv/test-* - config_name: en_mn data_files: - split: train path: en_mn/train-* - split: validation path: en_mn/validation-* - split: test path: en_mn/test-* - config_name: en_sl data_files: - split: train path: en_sl/train-* - split: validation path: en_sl/validation-* - split: test path: en_sl/test-* - config_name: en_sv-SE data_files: - split: train path: en_sv-SE/train-* - split: validation path: en_sv-SE/validation-* - split: test path: en_sv-SE/test-* - config_name: en_ta data_files: - split: train path: en_ta/train-* - split: validation path: en_ta/validation-* - split: test path: en_ta/test-* - config_name: en_tr data_files: - split: train path: en_tr/train-* - split: validation path: en_tr/validation-* - split: test path: en_tr/test-* - config_name: en_zh-CN data_files: - split: train path: en_zh-CN/train-* - split: validation path: en_zh-CN/validation-* - split: test path: en_zh-CN/test-* - config_name: es_en data_files: - split: validation path: es_en/validation-* - split: test path: es_en/test-* - split: train path: es_en/train-* - config_name: et_en data_files: - split: train path: et_en/train-* - split: validation path: et_en/validation-* - split: test path: et_en/test-* - config_name: fa_en data_files: - split: train path: fa_en/train-* - split: validation path: fa_en/validation-* - split: test path: fa_en/test-* - config_name: fr_en data_files: - split: validation path: fr_en/validation-* - split: test path: fr_en/test-* - split: train path: fr_en/train-* - config_name: id_en data_files: - split: train path: id_en/train-* - split: validation path: id_en/validation-* - split: test path: id_en/test-* - config_name: it_en data_files: - split: train path: it_en/train-* - split: validation path: it_en/validation-* - split: test path: it_en/test-* - config_name: ja_en data_files: - split: train path: ja_en/train-* - split: validation path: ja_en/validation-* - split: test path: ja_en/test-* - config_name: lv_en data_files: - split: train path: lv_en/train-* - split: validation path: lv_en/validation-* - split: test path: lv_en/test-* - config_name: mn_en data_files: - split: train path: mn_en/train-* - split: validation path: mn_en/validation-* - split: test path: mn_en/test-* - config_name: nl_en data_files: - split: train path: nl_en/train-* - split: validation path: nl_en/validation-* - split: test path: nl_en/test-* - config_name: pt_en data_files: - split: train path: pt_en/train-* - split: validation path: pt_en/validation-* - split: test path: pt_en/test-* - config_name: ru_en data_files: - split: train path: ru_en/train-* - split: validation path: ru_en/validation-* - split: test path: ru_en/test-* - config_name: sl_en data_files: - split: train path: sl_en/train-* - split: validation path: sl_en/validation-* - split: test path: sl_en/test-* - config_name: sv-SE_en data_files: - split: train path: sv-SE_en/train-* - split: validation path: sv-SE_en/validation-* - split: test path: sv-SE_en/test-* - config_name: ta_en data_files: - split: train path: ta_en/train-* - split: validation path: ta_en/validation-* - split: test path: ta_en/test-* - config_name: tr_en data_files: - split: train path: tr_en/train-* - split: validation path: tr_en/validation-* - split: test path: tr_en/test-* - config_name: zh-CN_en data_files: - split: validation path: zh-CN_en/validation-* - split: test path: zh-CN_en/test-* - split: train path: zh-CN_en/train-* --- This is a partial copy of [CoVoST2](https://huggingface.co/datasets/facebook/covost2) dataset. The main difference is that the audio data is included in the dataset, which makes usage easier and allows browsing the samples using HF Dataset Viewer. The limitation of this method is that all audio samples of the `EN_XX` subsets are duplicated, as such the size of the dataset is larger. As such, not all the data is included: Only the `validation` and `test` subsets are available. From the `XX_EN` subsets, only `fr`, `es`, and `zh-CN` are included.

google/fleurs

google

2024-08-25T05:03:32Z

27,911

287

[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "annotations_creators:crowdsourced", "annotations_creators:machine-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "language:afr", "language:amh", "language:ara", "language:asm", "language:ast", "language:azj", "language:bel", "language:ben", "language:bos", "language:cat", "language:ceb", "language:cmn", "language:ces", "language:cym", "language:dan", "language:deu", "language:ell", "language:eng", "language:spa", "language:est", "language:fas", "language:ful", "language:fin", "language:tgl", "language:fra", "language:gle", "language:glg", "language:guj", "language:hau", "language:heb", "language:hin", "language:hrv", "language:hun", "language:hye", "language:ind", "language:ibo", "language:isl", "language:ita", "language:jpn", "language:jav", "language:kat", "language:kam", "language:kea", "language:kaz", "language:khm", "language:kan", "language:kor", "language:ckb", "language:kir", "language:ltz", "language:lug", "language:lin", "language:lao", "language:lit", "language:luo", "language:lav", "language:mri", "language:mkd", "language:mal", "language:mon", "language:mar", "language:msa", "language:mlt", "language:mya", "language:nob", "language:npi", "language:nld", "language:nso", "language:nya", "language:oci", "language:orm", "language:ory", "language:pan", "language:pol", "language:pus", "language:por", "language:ron", "language:rus", "language:bul", "language:snd", "language:slk", "language:slv", "language:sna", "language:som", "language:srp", "language:swe", "language:swh", "language:tam", "language:tel", "language:tgk", "language:tha", "language:tur", "language:ukr", "language:umb", "language:urd", "language:uzb", "language:vie", "language:wol", "language:xho", "language:yor", "language:yue", "language:zul", "license:cc-by-4.0", "size_categories:10K<n<100K", "arxiv:2205.12446", "arxiv:2106.03193", "region:us", "speech-recognition" ]

[ "automatic-speech-recognition" ]

2022-04-19T10:25:58Z

null

--- annotations_creators: - expert-generated - crowdsourced - machine-generated language_creators: - crowdsourced - expert-generated language: - afr - amh - ara - asm - ast - azj - bel - ben - bos - cat - ceb - cmn - ces - cym - dan - deu - ell - eng - spa - est - fas - ful - fin - tgl - fra - gle - glg - guj - hau - heb - hin - hrv - hun - hye - ind - ibo - isl - ita - jpn - jav - kat - kam - kea - kaz - khm - kan - kor - ckb - kir - ltz - lug - lin - lao - lit - luo - lav - mri - mkd - mal - mon - mar - msa - mlt - mya - nob - npi - nld - nso - nya - oci - orm - ory - pan - pol - pus - por - ron - rus - bul - snd - slk - slv - sna - som - srp - swe - swh - tam - tel - tgk - tha - tur - ukr - umb - urd - uzb - vie - wol - xho - yor - yue - zul license: - cc-by-4.0 multilinguality: - multilingual size_categories: - 10K<n<100K task_categories: - automatic-speech-recognition task_ids: [] pretty_name: 'The Cross-lingual TRansfer Evaluation of Multilingual Encoders for Speech (XTREME-S) benchmark is a benchmark designed to evaluate speech representations across languages, tasks, domains and data regimes. It covers 102 languages from 10+ language families, 3 different domains and 4 task families: speech recognition, translation, classification and retrieval.' tags: - speech-recognition --- # FLEURS ## Dataset Description - **Fine-Tuning script:** [pytorch/speech-recognition](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition) - **Paper:** [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://arxiv.org/abs/2205.12446) - **Total amount of disk used:** ca. 350 GB Fleurs is the speech version of the [FLoRes machine translation benchmark](https://arxiv.org/abs/2106.03193). We use 2009 n-way parallel sentences from the FLoRes dev and devtest publicly available sets, in 102 languages. Training sets have around 10 hours of supervision. Speakers of the train sets are different than speakers from the dev/test sets. Multilingual fine-tuning is used and ”unit error rate” (characters, signs) of all languages is averaged. Languages and results are also grouped into seven geographical areas: - **Western Europe**: *Asturian, Bosnian, Catalan, Croatian, Danish, Dutch, English, Finnish, French, Galician, German, Greek, Hungarian, Icelandic, Irish, Italian, Kabuverdianu, Luxembourgish, Maltese, Norwegian, Occitan, Portuguese, Spanish, Swedish, Welsh* - **Eastern Europe**: *Armenian, Belarusian, Bulgarian, Czech, Estonian, Georgian, Latvian, Lithuanian, Macedonian, Polish, Romanian, Russian, Serbian, Slovak, Slovenian, Ukrainian* - **Central-Asia/Middle-East/North-Africa**: *Arabic, Azerbaijani, Hebrew, Kazakh, Kyrgyz, Mongolian, Pashto, Persian, Sorani-Kurdish, Tajik, Turkish, Uzbek* - **Sub-Saharan Africa**: *Afrikaans, Amharic, Fula, Ganda, Hausa, Igbo, Kamba, Lingala, Luo, Northern-Sotho, Nyanja, Oromo, Shona, Somali, Swahili, Umbundu, Wolof, Xhosa, Yoruba, Zulu* - **South-Asia**: *Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali, Oriya, Punjabi, Sindhi, Tamil, Telugu, Urdu* - **South-East Asia**: *Burmese, Cebuano, Filipino, Indonesian, Javanese, Khmer, Lao, Malay, Maori, Thai, Vietnamese* - **CJK languages**: *Cantonese and Mandarin Chinese, Japanese, Korean* ## How to use & Supported Tasks ### How to use The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. For example, to download the Hindi config, simply specify the corresponding language config name (i.e., "hi_in" for Hindi): ```python from datasets import load_dataset fleurs = load_dataset("google/fleurs", "hi_in", split="train") ``` Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk. ```python from datasets import load_dataset fleurs = load_dataset("google/fleurs", "hi_in", split="train", streaming=True) print(next(iter(fleurs))) ``` *Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed). Local: ```python from datasets import load_dataset from torch.utils.data.sampler import BatchSampler, RandomSampler fleurs = load_dataset("google/fleurs", "hi_in", split="train") batch_sampler = BatchSampler(RandomSampler(fleurs), batch_size=32, drop_last=False) dataloader = DataLoader(fleurs, batch_sampler=batch_sampler) ``` Streaming: ```python from datasets import load_dataset from torch.utils.data import DataLoader fleurs = load_dataset("google/fleurs", "hi_in", split="train") dataloader = DataLoader(fleurs, batch_size=32) ``` To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets). ### Example scripts Train your own CTC or Seq2Seq Automatic Speech Recognition models on FLEURS with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition). Fine-tune your own Language Identification models on FLEURS with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/audio-classification) ### 1. Speech Recognition (ASR) ```py from datasets import load_dataset fleurs_asr = load_dataset("google/fleurs", "af_za") # for Afrikaans # to download all data for multi-lingual fine-tuning uncomment following line # fleurs_asr = load_dataset("google/fleurs", "all") # see structure print(fleurs_asr) # load audio sample on the fly audio_input = fleurs_asr["train"][0]["audio"] # first decoded audio sample transcription = fleurs_asr["train"][0]["transcription"] # first transcription # use `audio_input` and `transcription` to fine-tune your model for ASR # for analyses see language groups all_language_groups = fleurs_asr["train"].features["lang_group_id"].names lang_group_id = fleurs_asr["train"][0]["lang_group_id"] all_language_groups[lang_group_id] ``` ### 2. Language Identification LangID can often be a domain classification, but in the case of FLEURS-LangID, recordings are done in a similar setting across languages and the utterances correspond to n-way parallel sentences, in the exact same domain, making this task particularly relevant for evaluating LangID. The setting is simple, FLEURS-LangID is splitted in train/valid/test for each language. We simply create a single train/valid/test for LangID by merging all. ```py from datasets import load_dataset fleurs_langID = load_dataset("google/fleurs", "all") # to download all data # see structure print(fleurs_langID) # load audio sample on the fly audio_input = fleurs_langID["train"][0]["audio"] # first decoded audio sample language_class = fleurs_langID["train"][0]["lang_id"] # first id class language = fleurs_langID["train"].features["lang_id"].names[language_class] # use audio_input and language_class to fine-tune your model for audio classification ``` ### 3. Retrieval Retrieval provides n-way parallel speech and text data. Similar to how XTREME for text leverages Tatoeba to evaluate bitext mining a.k.a sentence translation retrieval, we use Retrieval to evaluate the quality of fixed-size representations of speech utterances. Our goal is to incentivize the creation of fixed-size speech encoder for speech retrieval. The system has to retrieve the English "key" utterance corresponding to the speech translation of "queries" in 15 languages. Results have to be reported on the test sets of Retrieval whose utterances are used as queries (and keys for English). We augment the English keys with a large number of utterances to make the task more difficult. ```py from datasets import load_dataset fleurs_retrieval = load_dataset("google/fleurs", "af_za") # for Afrikaans # to download all data for multi-lingual fine-tuning uncomment following line # fleurs_retrieval = load_dataset("google/fleurs", "all") # see structure print(fleurs_retrieval) # load audio sample on the fly audio_input = fleurs_retrieval["train"][0]["audio"] # decoded audio sample text_sample_pos = fleurs_retrieval["train"][0]["transcription"] # positive text sample text_sample_neg = fleurs_retrieval["train"][1:20]["transcription"] # negative text samples # use `audio_input`, `text_sample_pos`, and `text_sample_neg` to fine-tune your model for retrieval ``` Users can leverage the training (and dev) sets of FLEURS-Retrieval with a ranking loss to build better cross-lingual fixed-size representations of speech. ## Dataset Structure We show detailed information the example configurations `af_za` of the dataset. All other configurations have the same structure. ### Data Instances **af_za** - Size of downloaded dataset files: 1.47 GB - Size of the generated dataset: 1 MB - Total amount of disk used: 1.47 GB An example of a data instance of the config `af_za` looks as follows: ``` {'id': 91, 'num_samples': 385920, 'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/310a663d52322700b3d3473cbc5af429bd92a23f9bc683594e70bc31232db39e/home/vaxelrod/FLEURS/oss2_obfuscated/af_za/audio/train/17797742076841560615.wav', 'audio': {'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/310a663d52322700b3d3473cbc5af429bd92a23f9bc683594e70bc31232db39e/home/vaxelrod/FLEURS/oss2_obfuscated/af_za/audio/train/17797742076841560615.wav', 'array': array([ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., -1.1205673e-04, -8.4638596e-05, -1.2731552e-04], dtype=float32), 'sampling_rate': 16000}, 'raw_transcription': 'Dit is nog nie huidiglik bekend watter aantygings gemaak sal word of wat owerhede na die seun gelei het nie maar jeugmisdaad-verrigtinge het in die federale hof begin', 'transcription': 'dit is nog nie huidiglik bekend watter aantygings gemaak sal word of wat owerhede na die seun gelei het nie maar jeugmisdaad-verrigtinge het in die federale hof begin', 'gender': 0, 'lang_id': 0, 'language': 'Afrikaans', 'lang_group_id': 3} ``` ### Data Fields The data fields are the same among all splits. - **id** (int): ID of audio sample - **num_samples** (int): Number of float values - **path** (str): Path to the audio file - **audio** (dict): Audio object including loaded audio array, sampling rate and path ot audio - **raw_transcription** (str): The non-normalized transcription of the audio file - **transcription** (str): Transcription of the audio file - **gender** (int): Class id of gender - **lang_id** (int): Class id of language - **lang_group_id** (int): Class id of language group ### Data Splits Every config only has the `"train"` split containing of *ca.* 1000 examples, and a `"validation"` and `"test"` split each containing of *ca.* 400 examples. ## Dataset Creation We collect between one and three recordings for each sentence (2.3 on average), and buildnew train-dev-test splits with 1509, 150 and 350 sentences for train, dev and test respectively. ## Considerations for Using the Data ### Social Impact of Dataset This dataset is meant to encourage the development of speech technology in a lot more languages of the world. One of the goal is to give equal access to technologies like speech recognition or speech translation to everyone, meaning better dubbing or better access to content from the internet (like podcasts, streaming or videos). ### Discussion of Biases Most datasets have a fair distribution of gender utterances (e.g. the newly introduced FLEURS dataset). While many languages are covered from various regions of the world, the benchmark misses many languages that are all equally important. We believe technology built through FLEURS should generalize to all languages. ### Other Known Limitations The dataset has a particular focus on read-speech because common evaluation benchmarks like CoVoST-2 or LibriSpeech evaluate on this type of speech. There is sometimes a known mismatch between performance obtained in a read-speech setting and a more noisy setting (in production for instance). Given the big progress that remains to be made on many languages, we believe better performance on FLEURS should still correlate well with actual progress made for speech understanding. ## Additional Information All datasets are licensed under the [Creative Commons license (CC-BY)](https://creativecommons.org/licenses/). ### Citation Information You can access the FLEURS paper at https://arxiv.org/abs/2205.12446. Please cite the paper when referencing the FLEURS corpus as: ``` @article{fleurs2022arxiv, title = {FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech}, author = {Conneau, Alexis and Ma, Min and Khanuja, Simran and Zhang, Yu and Axelrod, Vera and Dalmia, Siddharth and Riesa, Jason and Rivera, Clara and Bapna, Ankur}, journal={arXiv preprint arXiv:2205.12446}, url = {https://arxiv.org/abs/2205.12446}, year = {2022}, ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) and [@aconneau](https://github.com/aconneau) for adding this dataset.

gksriharsha/chitralekha

gksriharsha

2024-08-23T23:00:03Z

160,664

4

[ "task_categories:image-to-text", "language:te", "license:mit", "size_categories:10M<n<100M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "doi:10.57967/hf/3403", "region:us" ]

[ "image-to-text" ]

2023-11-29T14:31:24Z

null

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config_name: TLOTRajanBI_Ship features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 3353184954.5082364 num_examples: 474312 - name: validation num_bytes: 1117735387.7458818 num_examples: 158105 - name: test num_bytes: 1117735387.7458818 num_examples: 158105 download_size: 5601810958 dataset_size: 5588655730 - config_name: TLOTRajanB_Ship features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 3333244214.4 num_examples: 473649 - name: validation num_bytes: 1111081404.8 num_examples: 157883 - name: test num_bytes: 1111081404.8 num_examples: 157883 download_size: 11147115559 dataset_size: 5555407024.000001 - config_name: TLOTRajaniBI_Ship features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 2052738894.6 num_examples: 475389 - name: validation num_bytes: 684246298.2 num_examples: 158463 - name: test num_bytes: 684246298.2 num_examples: 158463 download_size: 3411081728 dataset_size: 3421231491 - 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config_name: TLOTSitaraBI_Ship features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 2519685455.5459795 num_examples: 476097 - name: validation num_bytes: 839900444.2270104 num_examples: 158700 - name: test num_bytes: 839900444.2270104 num_examples: 158700 download_size: 4197747699 dataset_size: 4199486344 - config_name: TLOTSitaraB_Ship features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 2503669021.2 num_examples: 476304 - name: validation num_bytes: 834556340.4 num_examples: 158768 - name: test num_bytes: 834556340.4 num_examples: 158768 download_size: 4170641698 dataset_size: 4172781702 - config_name: TLOTSwamiB features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 2425012348.9576674 num_examples: 477330 - name: validation num_bytes: 808342530.0211664 num_examples: 159111 - name: test num_bytes: 808342530.0211664 num_examples: 159111 download_size: 4038041582 dataset_size: 4041697409 - 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config_name: TenaliRamakrishna-Regular features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1412098107.6 num_examples: 479922 - name: validation num_bytes: 470699369.2 num_examples: 159974 - name: test num_bytes: 470699369.2 num_examples: 159974 download_size: 2373061510 dataset_size: 2353496846 - config_name: Tikkana features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 237760800.6 num_examples: 476520 - name: validation num_bytes: 79253600.2 num_examples: 158840 - name: test num_bytes: 79253600.2 num_examples: 158840 download_size: 266272383 dataset_size: 396268001 - config_name: TimmanaRegular features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1476790008.6 num_examples: 478059 - name: validation num_bytes: 492263336.2 num_examples: 159353 - name: test num_bytes: 492263336.2 num_examples: 159353 download_size: 2461309068 dataset_size: 2461316681 - config_name: Vajram features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1522698226.9404452 num_examples: 480837 - name: validation num_bytes: 507566075.64681506 num_examples: 160279 - name: test num_bytes: 507569242.41273975 num_examples: 160280 download_size: 2548130724 dataset_size: 2537833545 - config_name: Vani features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1457020940.7032518 num_examples: 476385 - name: validation num_bytes: 485673646.9010839 num_examples: 158795 - name: test num_bytes: 485676705.39566433 num_examples: 158796 download_size: 2434817917 dataset_size: 2428371293 - config_name: Vanib features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1522290417.6 num_examples: 474951 - name: validation num_bytes: 507430139.2 num_examples: 158317 - name: test num_bytes: 507430139.2 num_examples: 158317 download_size: 2529233521 dataset_size: 2537150696 - config_name: Vemana features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1699154826.4604304 num_examples: 476205 - name: validation num_bytes: 566388510.2697848 num_examples: 158736 - name: test num_bytes: 566388510.2697848 num_examples: 158736 download_size: 2814457802 dataset_size: 2831931847 - config_name: akshar features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1339177104.1214905 num_examples: 476169 - name: validation num_bytes: 446395180.4392547 num_examples: 158724 - name: test num_bytes: 446395180.4392547 num_examples: 158724 download_size: 2284376294 dataset_size: 2231967465 - config_name: gautami features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1459193859.1610594 num_examples: 476425 - name: validation num_bytes: 486399994.91947037 num_examples: 158809 - name: test num_bytes: 486399994.91947037 num_examples: 158809 download_size: 2447315957 dataset_size: 2431993849 - config_name: gautamib features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1464740409.2608879 num_examples: 477459 - name: validation num_bytes: 488249870.869556 num_examples: 159154 - name: test num_bytes: 488249870.869556 num_examples: 159154 download_size: 2454242590 dataset_size: 2441240151 - config_name: lohit_te features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1566900366.462158 num_examples: 477809 - name: validation num_bytes: 522301215.268921 num_examples: 159270 - name: test num_bytes: 522301215.268921 num_examples: 159270 download_size: 2611413315 dataset_size: 2611502797 configs: - config_name: Dhurjati data_files: - split: train path: Dhurjati/train-* - split: validation path: Dhurjati/validation-* - split: test path: Dhurjati/test-* - config_name: Gidugu data_files: - split: train path: Gidugu/train-* - split: validation path: Gidugu/validation-* - split: test path: Gidugu/test-* - config_name: Gurajada data_files: - split: train path: Gurajada/train-* - split: validation path: Gurajada/validation-* - split: test path: Gurajada/test-* - config_name: Mallanna data_files: - split: train path: Mallanna/train-* - split: validation path: Mallanna/validation-* - split: test path: Mallanna/test-* - config_name: Mandali-Regular data_files: - split: train path: Mandali-Regular/train-* - split: validation path: Mandali-Regular/validation-* - split: test path: Mandali-Regular/test-* - config_name: NATS data_files: - split: train path: NATS/train-* - split: validation path: NATS/validation-* - split: test path: NATS/test-* - config_name: NTR data_files: - split: train path: NTR/train-* - split: validation path: NTR/validation-* - split: test path: NTR/test-* - config_name: NotoSansTelugu-Bold data_files: - split: train path: NotoSansTelugu-Bold/train-* - split: validation path: NotoSansTelugu-Bold/validation-* - split: test path: NotoSansTelugu-Bold/test-* - config_name: NotoSansTelugu-Regular data_files: - split: train path: NotoSansTelugu-Regular/train-* - split: validation path: NotoSansTelugu-Regular/validation-* - split: test path: NotoSansTelugu-Regular/test-* - config_name: NotoSansTeluguUI-Bold data_files: - split: train path: NotoSansTeluguUI-Bold/train-* - split: validation path: NotoSansTeluguUI-Bold/validation-* - split: test path: NotoSansTeluguUI-Bold/test-* - config_name: NotoSansTeluguUI-Regular data_files: - split: train path: NotoSansTeluguUI-Regular/train-* - split: validation path: NotoSansTeluguUI-Regular/validation-* - split: test path: NotoSansTeluguUI-Regular/test-* - config_name: NotoSerifTelugu-VariableFont_wght data_files: - split: train path: NotoSerifTelugu-VariableFont_wght/train-* - split: validation path: NotoSerifTelugu-VariableFont_wght/validation-* - split: test path: NotoSerifTelugu-VariableFont_wght/test-* - config_name: Pothana2000 data_files: - split: train path: Pothana2000/train-* - split: validation path: Pothana2000/validation-* - split: test path: Pothana2000/test-* - config_name: Ramabhadra data_files: - split: train path: Ramabhadra/train-* - split: validation path: Ramabhadra/validation-* - split: test path: Ramabhadra/test-* - config_name: Ramabhadra1 data_files: - split: train path: Ramabhadra1/train-* - split: validation path: Ramabhadra1/validation-* - split: test path: Ramabhadra1/test-* - config_name: RamaneeyaWin data_files: - split: train path: RamaneeyaWin/train-* - split: validation path: RamaneeyaWin/validation-* - split: test path: RamaneeyaWin/test-* - config_name: Ramaraja-Regular data_files: - split: train path: Ramaraja-Regular/train-* - split: validation path: Ramaraja-Regular/validation-* - split: test path: Ramaraja-Regular/test-* - config_name: Suguna data_files: - split: train path: Suguna/train-* - split: validation path: Suguna/validation-* - split: test path: Suguna/test-* - config_name: Suranna data_files: - split: train path: Suranna/train-* - split: validation path: Suranna/validation-* - split: test path: Suranna/test-* - config_name: Suravara_Samhita data_files: - split: train path: Suravara_Samhita/train-* - split: validation path: Suravara_Samhita/validation-* - split: test path: Suravara_Samhita/test-* - config_name: Suravara_Swarna data_files: - split: train path: Suravara_Swarna/train-* - split: validation path: Suravara_Swarna/validation-* - split: test path: Suravara_Swarna/test-* - config_name: Suravara_Swarna_bold data_files: - split: train path: Suravara_Swarna_bold/train-* - split: validation path: Suravara_Swarna_bold/validation-* - split: test path: Suravara_Swarna_bold/test-* - config_name: Suravara_Swarna_italic data_files: - split: train path: Suravara_Swarna_italic/train-* - split: validation path: Suravara_Swarna_italic/validation-* - split: test path: Suravara_Swarna_italic/test-* - config_name: Suravaram data_files: - split: train path: Suravaram/train-* - split: validation path: Suravaram/validation-* - split: test path: Suravaram/test-* - config_name: TLOTAmmaBI_ship data_files: - split: train path: TLOTAmmaBI_ship/train-* - split: validation path: TLOTAmmaBI_ship/validation-* - split: test path: TLOTAmmaBI_ship/test-* - config_name: TLOTAmmaB_ship data_files: - split: train path: TLOTAmmaB_ship/train-* - split: validation path: TLOTAmmaB_ship/validation-* - split: test path: TLOTAmmaB_ship/test-* - config_name: TLOTAmmaI_ship data_files: - split: train path: TLOTAmmaI_ship/train-* - split: validation path: TLOTAmmaI_ship/validation-* - split: test path: TLOTAmmaI_ship/test-* - config_name: TLOTAmmaN_ship data_files: - split: train path: TLOTAmmaN_ship/train-* - split: validation path: TLOTAmmaN_ship/validation-* - split: test path: TLOTAmmaN_ship/test-* - config_name: TLOTAmrutaBI_Ship data_files: - split: train path: TLOTAmrutaBI_Ship/train-* - split: validation path: TLOTAmrutaBI_Ship/validation-* - split: test path: TLOTAmrutaBI_Ship/test-* - config_name: TLOTAmrutaB_Ship data_files: - split: train path: TLOTAmrutaB_Ship/train-* - split: validation path: TLOTAmrutaB_Ship/validation-* - split: test path: TLOTAmrutaB_Ship/test-* - config_name: TLOTAtreyaBI_Ship data_files: - split: train path: TLOTAtreyaBI_Ship/train-* - split: validation path: TLOTAtreyaBI_Ship/validation-* - split: test path: TLOTAtreyaBI_Ship/test-* - config_name: TLOTAtreyaB_Ship data_files: - split: train path: TLOTAtreyaB_Ship/train-* - split: validation path: TLOTAtreyaB_Ship/validation-* - split: test path: TLOTAtreyaB_Ship/test-* - config_name: TLOTAtreyaI_Ship data_files: - split: train path: TLOTAtreyaI_Ship/train-* - split: validation path: TLOTAtreyaI_Ship/validation-* - split: test path: TLOTAtreyaI_Ship/test-* - config_name: TLOTAtreyaN_Ship data_files: - split: train path: TLOTAtreyaN_Ship/train-* - split: validation path: TLOTAtreyaN_Ship/validation-* - split: test path: TLOTAtreyaN_Ship/test-* - config_name: TLOTChandanaBI_Ship data_files: - split: train path: TLOTChandanaBI_Ship/train-* - split: validation path: TLOTChandanaBI_Ship/validation-* - split: test path: TLOTChandanaBI_Ship/test-* - config_name: TLOTChandanaB_Ship data_files: - split: train path: TLOTChandanaB_Ship/train-* - split: validation path: TLOTChandanaB_Ship/validation-* - split: test path: TLOTChandanaB_Ship/test-* - config_name: TLOTDevaI_Ship data_files: - split: train path: TLOTDevaI_Ship/train-* - split: validation path: TLOTDevaI_Ship/validation-* - split: test path: TLOTDevaI_Ship/test-* - config_name: TLOTDevaN_Ship data_files: - split: train path: TLOTDevaN_Ship/train-* - split: validation path: TLOTDevaN_Ship/validation-* - split: test path: TLOTDevaN_Ship/test-* - config_name: TLOTDraupadiBI_Ship data_files: - split: train path: TLOTDraupadiBI_Ship/train-* - split: validation path: TLOTDraupadiBI_Ship/validation-* - split: test path: TLOTDraupadiBI_Ship/test-* - config_name: TLOTDraupadiB_ship data_files: - split: train path: TLOTDraupadiB_ship/train-* - split: validation path: TLOTDraupadiB_ship/validation-* - split: test path: TLOTDraupadiB_ship/test-* - config_name: TLOTDraupadiI_Ship data_files: - split: train path: TLOTDraupadiI_Ship/train-* - split: validation path: TLOTDraupadiI_Ship/validation-* - split: test path: TLOTDraupadiI_Ship/test-* - config_name: TLOTDraupadiN_Ship data_files: - split: train path: TLOTDraupadiN_Ship/train-* - split: validation path: TLOTDraupadiN_Ship/validation-* - split: test path: TLOTDraupadiN_Ship/test-* - config_name: TLOTGolkondaBI_Ship data_files: - split: train path: TLOTGolkondaBI_Ship/train-* - split: validation path: TLOTGolkondaBI_Ship/validation-* - split: test path: TLOTGolkondaBI_Ship/test-* - config_name: TLOTGolkondaB_Ship data_files: - split: train path: TLOTGolkondaB_Ship/train-* - split: validation path: TLOTGolkondaB_Ship/validation-* - split: test path: TLOTGolkondaB_Ship/test-* - config_name: TLOTKrishnaB_Ship data_files: - split: train path: TLOTKrishnaB_Ship/train-* - split: validation path: TLOTKrishnaB_Ship/validation-* - split: test path: TLOTKrishnaB_Ship/test-* - config_name: TLOTKrishnaI_Ship data_files: - split: train path: TLOTKrishnaI_Ship/train-* - split: validation path: TLOTKrishnaI_Ship/validation-* - split: test path: TLOTKrishnaI_Ship/test-* - config_name: TLOTKrishnaN_Ship data_files: - split: train path: TLOTKrishnaN_Ship/train-* - split: validation path: TLOTKrishnaN_Ship/validation-* - split: test path: TLOTKrishnaN_Ship/test-* - config_name: TLOTManuBI_Ship data_files: - split: train path: TLOTManuBI_Ship/train-* - split: validation path: TLOTManuBI_Ship/validation-* - split: test path: TLOTManuBI_Ship/test-* - config_name: TLOTManuB_Ship data_files: - split: train path: TLOTManuB_Ship/train-* - split: validation path: TLOTManuB_Ship/validation-* - split: test path: TLOTManuB_Ship/test-* - config_name: TLOTManuI_Ship data_files: - split: train path: TLOTManuI_Ship/train-* - split: validation path: TLOTManuI_Ship/validation-* - split: test path: TLOTManuI_Ship/test-* - config_name: TLOTManuN_Ship data_files: - split: train path: TLOTManuN_Ship/train-* - split: validation path: TLOTManuN_Ship/validation-* - split: test path: TLOTManuN_Ship/test-* - config_name: TLOTMenakaBI_Ship data_files: - split: train path: TLOTMenakaBI_Ship/train-* - split: validation path: TLOTMenakaBI_Ship/validation-* - split: test path: TLOTMenakaBI_Ship/test-* - config_name: TLOTMenakaB_Ship data_files: - split: train path: TLOTMenakaB_Ship/train-* - split: validation path: TLOTMenakaB_Ship/validation-* - split: test path: TLOTMenakaB_Ship/test-* - config_name: TLOTMenakaI_Ship data_files: - split: train path: TLOTMenakaI_Ship/train-* - split: validation path: TLOTMenakaI_Ship/validation-* - split: test path: TLOTMenakaI_Ship/test-* - config_name: TLOTMenakaN_Ship data_files: - split: train path: TLOTMenakaN_Ship/train-* - split: validation path: TLOTMenakaN_Ship/validation-* - split: test path: TLOTMenakaN_Ship/test-* - config_name: TLOTPavaniBI_Ship data_files: - split: train path: TLOTPavaniBI_Ship/train-* - split: validation path: TLOTPavaniBI_Ship/validation-* - split: test path: TLOTPavaniBI_Ship/test-* - config_name: TLOTPavaniB_Ship data_files: - split: train path: TLOTPavaniB_Ship/train-* - split: validation path: TLOTPavaniB_Ship/validation-* - split: test path: TLOTPavaniB_Ship/test-* - config_name: TLOTPriyaB_Ship data_files: - split: train path: TLOTPriyaB_Ship/train-* - split: validation path: TLOTPriyaB_Ship/validation-* - split: test path: TLOTPriyaB_Ship/test-* - config_name: TLOTRajanBI_Ship data_files: - split: train path: TLOTRajanBI_Ship/train-* - split: validation path: TLOTRajanBI_Ship/validation-* - split: test path: TLOTRajanBI_Ship/test-* - config_name: TLOTRajanB_Ship data_files: - split: train path: TLOTRajanB_Ship/train-* - split: validation path: TLOTRajanB_Ship/validation-* - split: test path: TLOTRajanB_Ship/test-* - config_name: TLOTRajaniBI_Ship data_files: - split: train path: TLOTRajaniBI_Ship/train-* - split: validation path: TLOTRajaniBI_Ship/validation-* - split: test path: TLOTRajaniBI_Ship/test-* - config_name: TLOTRajaniB_Ship data_files: - split: train path: TLOTRajaniB_Ship/train-* - split: validation path: TLOTRajaniB_Ship/validation-* - split: test path: TLOTRajaniB_Ship/test-* - config_name: TLOTSanjanaBI_Ship data_files: - split: train path: TLOTSanjanaBI_Ship/train-* - split: validation path: TLOTSanjanaBI_Ship/validation-* - split: test path: TLOTSanjanaBI_Ship/test-* - config_name: TLOTSanjanaB_Ship data_files: - split: train path: TLOTSanjanaB_Ship/train-* - split: validation path: TLOTSanjanaB_Ship/validation-* - split: test path: TLOTSanjanaB_Ship/test-* - config_name: TLOTSitaraBI_Ship data_files: - split: train path: TLOTSitaraBI_Ship/train-* - split: validation path: TLOTSitaraBI_Ship/validation-* - split: test path: TLOTSitaraBI_Ship/test-* - config_name: TLOTSitaraB_Ship data_files: - split: train path: TLOTSitaraB_Ship/train-* - split: validation path: TLOTSitaraB_Ship/validation-* - split: test path: TLOTSitaraB_Ship/test-* - config_name: TLOTSwamiBI_Ship data_files: - split: train path: TLOTSwamiBI_Ship/train-* - split: validation path: TLOTSwamiBI_Ship/validation-* - split: test path: TLOTSwamiBI_Ship/test-* - config_name: TLOTSwamiB_Ship data_files: - split: train path: TLOTSwamiB_Ship/train-* - split: validation path: TLOTSwamiB_Ship/validation-* - split: test path: TLOTSwamiB_Ship/test-* - config_name: TLOTVennela1B_Ship data_files: - split: train path: TLOTVennela1B_Ship/train-* - split: validation path: TLOTVennela1B_Ship/validation-* - split: test path: TLOTVennela1B_Ship/test-* - config_name: TLOTVennelaBI_Ship data_files: - split: train path: TLOTVennelaBI_Ship/train-* - split: validation path: TLOTVennelaBI_Ship/validation-* - split: test path: TLOTVennelaBI_Ship/test-* - config_name: TLOTVennelaI_Ship data_files: - split: train path: TLOTVennelaI_Ship/train-* - split: validation path: TLOTVennelaI_Ship/validation-* - split: test path: TLOTVennelaI_Ship/test-* - config_name: TenaliRamakrishna-Regular data_files: - split: train path: TenaliRamakrishna-Regular/train-* - split: validation path: TenaliRamakrishna-Regular/validation-* - split: test path: TenaliRamakrishna-Regular/test-* - config_name: TimmanaRegular data_files: - split: train path: TimmanaRegular/train-* - split: validation path: TimmanaRegular/validation-* - split: test path: TimmanaRegular/test-* - config_name: Vanib data_files: - split: train path: Vanib/train-* - split: validation path: Vanib/validation-* - split: test path: Vanib/test-* - config_name: Vemana data_files: - split: train path: Vemana/train-* - split: validation path: Vemana/validation-* - split: test path: Vemana/test-* - config_name: akshar data_files: - split: train path: akshar/train-* - split: validation path: akshar/validation-* - split: test path: akshar/test-* - config_name: gautami data_files: - split: train path: gautami/train-* - split: validation path: gautami/validation-* - split: test path: gautami/test-* - config_name: gautamib data_files: - split: train path: gautamib/train-* - split: validation path: gautamib/validation-* - split: test path: gautamib/test-* license: mit task_categories: - image-to-text language: - te size_categories: - 1M<n<10M --- # Chitralekha ## Dataset Details ### Dataset Version Some of the fonts do not have proper letters/rendering of different telugu letter combinations. Those have been removed as much as I can find them. If there are any other mistakes that you notice, please raise an issue and I will try my best to look into it ### Dataset Description This extensive dataset, hosted on Huggingface, is a comprehensive resource for Optical Character Recognition (OCR) in the Telugu language, featuring an impressive array of 80+ configurations. Each configuration in this dataset corresponds to a unique font, meticulously curated by Dr. Rakesh Achanta and sourced from his GitHub repository (https://github.com/TeluguOCR/banti_telugu_ocr). The dataset is specifically designed to support and enhance the development of OCR models, ranging from simple Convolutional Recurrent Neural Network (CRNN) architectures to more advanced systems like trOCR. The versatility of this dataset lies in its large volume and diversity, making it an ideal choice for researchers and developers aiming to build robust OCR systems for the Telugu script. Key Features: - Font Diversity: Over 80 unique fonts, each forming a separate configuration, providing a rich variety in text styles and nuances. - Large Volume: Each configuration contains approximately 800,000 examples, summing up to a vast pool of data for comprehensive training and evaluation. - Data Split: The dataset is pre-split into training, validation, and test sets, following a 60/20/20 ratio, to facilitate efficient model training and benchmarking. - Use Cases: Ideal for developing a wide range of OCR models - from basic CRNNs to sophisticated models like trOCR. - Accessibility: Hosted on Huggingface, ensuring easy access and integration with various machine learning frameworks and tools. This dataset stands as a testament to Dr. Rakesh Achanta's dedication to enhancing Telugu language processing technologies. It is not just a tool for model development but also a gateway to preserving and digitizing the rich literary heritage of the Telugu language. Researchers and developers leveraging this dataset are encouraged to adhere to the ethical guidelines of AI research and development, ensuring that the applications developed are for the benefit of language preservation, accessibility, and technological advancement in a responsible manner. - **Fonts Curated by:** Dr. Rakesh Achanta - **Shared by:** Krishna Sriharsha Gundu - **Data Curated by:** Anusha Motamarri - **Language(s) (NLP):** Telugu ### Ethical Considerations: Researchers and developers leveraging this dataset are encouraged to adhere to the ethical guidelines of AI research and development. Applications developed using this dataset should prioritize: - Language preservation and cultural heritage protection - Improving accessibility of Telugu text for diverse user groups - Responsible technological advancement in language processing ### Dataset Sources [optional]  - **Repository:** [Original Books Dataset](https://github.com/AnushaMotamarri/Telugu-Books-Dataset)

legacy-datasets/common_voice

legacy-datasets

2024-08-22T08:27:23Z

4,611

137

[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:multilingual", "source_datasets:extended|common_voice", "language:ab", "language:ar", "language:as", "language:br", "language:ca", "language:cnh", "language:cs", "language:cv", "language:cy", "language:de", "language:dv", "language:el", "language:en", "language:eo", "language:es", "language:et", "language:eu", "language:fa", "language:fi", "language:fr", "language:fy", "language:ga", "language:hi", "language:hsb", "language:hu", "language:ia", "language:id", "language:it", "language:ja", "language:ka", "language:kab", "language:ky", "language:lg", "language:lt", "language:lv", "language:mn", "language:mt", "language:nl", "language:or", "language:pa", "language:pl", "language:pt", "language:rm", "language:ro", "language:ru", "language:rw", "language:sah", "language:sl", "language:sv", "language:ta", "language:th", "language:tr", "language:tt", "language:uk", "language:vi", "language:vot", "language:zh", "license:cc0-1.0", "size_categories:100K<n<1M", "region:us" ]

[ "automatic-speech-recognition" ]

2022-03-02T23:29:22Z

null

--- pretty_name: Common Voice annotations_creators: - crowdsourced language_creators: - crowdsourced language: - ab - ar - as - br - ca - cnh - cs - cv - cy - de - dv - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - hi - hsb - hu - ia - id - it - ja - ka - kab - ky - lg - lt - lv - mn - mt - nl - or - pa - pl - pt - rm - ro - ru - rw - sah - sl - sv - ta - th - tr - tt - uk - vi - vot - zh language_bcp47: - fy-NL - ga-IE - pa-IN - rm-sursilv - rm-vallader - sv-SE - zh-CN - zh-HK - zh-TW license: - cc0-1.0 multilinguality: - multilingual size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K - n<1K source_datasets: - extended|common_voice task_categories: - automatic-speech-recognition task_ids: [] paperswithcode_id: common-voice viewer: false dataset_info: - config_name: ab features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 1295622 num_examples: 22 - name: test num_bytes: 411844 num_examples: 9 - name: validation - name: other num_bytes: 40023390 num_examples: 752 - name: validated num_bytes: 1707426 num_examples: 31 - name: invalidated num_bytes: 361626 num_examples: 8 download_size: 41038412 dataset_size: 43799908 - config_name: ar features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 359335168 num_examples: 14227 - name: test num_bytes: 237546641 num_examples: 7622 - name: validation num_bytes: 209606861 num_examples: 7517 - name: other num_bytes: 515822404 num_examples: 18283 - name: validated num_bytes: 1182522872 num_examples: 43291 - name: invalidated num_bytes: 194805036 num_examples: 6333 download_size: 1756264615 dataset_size: 2699638982 - config_name: as features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 11442279 num_examples: 270 - name: test num_bytes: 5071343 num_examples: 110 - name: validation num_bytes: 5480156 num_examples: 124 - name: other - name: validated num_bytes: 21993698 num_examples: 504 - name: invalidated num_bytes: 886145 num_examples: 31 download_size: 22226465 dataset_size: 44873621 - config_name: br features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 62238289 num_examples: 2780 - name: test num_bytes: 54461339 num_examples: 2087 - name: validation num_bytes: 46995570 num_examples: 1997 - name: other num_bytes: 269858143 num_examples: 10912 - name: validated num_bytes: 203503622 num_examples: 8560 - name: invalidated num_bytes: 20861017 num_examples: 623 download_size: 465276982 dataset_size: 657917980 - config_name: ca features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 12966939466 num_examples: 285584 - name: test num_bytes: 745761890 num_examples: 15724 - name: validation num_bytes: 716442038 num_examples: 15724 - name: other num_bytes: 2693542910 num_examples: 64446 - name: validated num_bytes: 18115833966 num_examples: 416701 - name: invalidated num_bytes: 850402888 num_examples: 18846 download_size: 20743110341 dataset_size: 36088923158 - config_name: cnh features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 18866674 num_examples: 807 - name: test num_bytes: 24675321 num_examples: 752 - name: validation num_bytes: 22162315 num_examples: 756 - name: other num_bytes: 84878963 num_examples: 2934 - name: validated num_bytes: 69330148 num_examples: 2432 - name: invalidated num_bytes: 13642724 num_examples: 433 download_size: 161331331 dataset_size: 233556145 - config_name: cs features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 215205282 num_examples: 5655 - name: test num_bytes: 148499476 num_examples: 4144 - name: validation num_bytes: 148312130 num_examples: 4118 - name: other num_bytes: 282225475 num_examples: 7475 - name: validated num_bytes: 1019817024 num_examples: 30431 - name: invalidated num_bytes: 24717823 num_examples: 685 download_size: 1271909933 dataset_size: 1838777210 - config_name: cv features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 31649510 num_examples: 931 - name: test num_bytes: 32513061 num_examples: 788 - name: validation num_bytes: 28429779 num_examples: 818 - name: other num_bytes: 288294623 num_examples: 6927 - name: validated num_bytes: 126717875 num_examples: 3496 - name: invalidated num_bytes: 57923138 num_examples: 1282 download_size: 439329081 dataset_size: 565527986 - config_name: cy features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 271642649 num_examples: 6839 - name: test num_bytes: 206865596 num_examples: 4820 - name: validation num_bytes: 201813388 num_examples: 4776 - name: other num_bytes: 688469886 num_examples: 17919 - name: validated num_bytes: 2763112391 num_examples: 72984 - name: invalidated num_bytes: 146874576 num_examples: 3648 download_size: 3434474658 dataset_size: 4278778486 - config_name: de features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 11463160619 num_examples: 246525 - name: test num_bytes: 744617681 num_examples: 15588 - name: validation num_bytes: 729559862 num_examples: 15588 - name: other num_bytes: 464513461 num_examples: 10095 - name: validated num_bytes: 22402489041 num_examples: 565186 - name: invalidated num_bytes: 1440604803 num_examples: 32789 download_size: 23283812097 dataset_size: 37244945467 - config_name: dv features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 118576140 num_examples: 2680 - name: test num_bytes: 94281409 num_examples: 2202 - name: validation num_bytes: 94117088 num_examples: 2077 - name: other - name: validated num_bytes: 528571107 num_examples: 11866 - name: invalidated num_bytes: 37694847 num_examples: 840 download_size: 540488041 dataset_size: 873240591 - config_name: el features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 80759076 num_examples: 2316 - name: test num_bytes: 53820491 num_examples: 1522 - name: validation num_bytes: 44818565 num_examples: 1401 - name: other num_bytes: 186861175 num_examples: 5659 - name: validated num_bytes: 204446790 num_examples: 5996 - name: invalidated num_bytes: 6023769 num_examples: 185 download_size: 381570611 dataset_size: 576729866 - config_name: en features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 26088826658 num_examples: 564337 - name: test num_bytes: 758718688 num_examples: 16164 - name: validation num_bytes: 795638801 num_examples: 16164 - name: other num_bytes: 5796244022 num_examples: 169895 - name: validated num_bytes: 48425872575 num_examples: 1224864 - name: invalidated num_bytes: 9122973965 num_examples: 189562 download_size: 60613063630 dataset_size: 90988274709 - config_name: eo features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 993655930 num_examples: 19587 - name: test num_bytes: 420153812 num_examples: 8969 - name: validation num_bytes: 391427586 num_examples: 8987 - name: other num_bytes: 142476819 num_examples: 2946 - name: validated num_bytes: 2603249289 num_examples: 58094 - name: invalidated num_bytes: 238105462 num_examples: 4736 download_size: 2883560869 dataset_size: 4789068898 - config_name: es features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 6918333205 num_examples: 161813 - name: test num_bytes: 754049291 num_examples: 15089 - name: validation num_bytes: 735558084 num_examples: 15089 - name: other num_bytes: 5528972205 num_examples: 144791 - name: validated num_bytes: 9623788388 num_examples: 236314 - name: invalidated num_bytes: 1664876264 num_examples: 40640 download_size: 16188844718 dataset_size: 25225577437 - config_name: et features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 161124199 num_examples: 2966 - name: test num_bytes: 133183135 num_examples: 2509 - name: validation num_bytes: 137604813 num_examples: 2507 - name: other num_bytes: 30339130 num_examples: 569 - name: validated num_bytes: 573417188 num_examples: 10683 - name: invalidated num_bytes: 193019544 num_examples: 3557 download_size: 767174465 dataset_size: 1228688009 - config_name: eu features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 317322801 num_examples: 7505 - name: test num_bytes: 238866501 num_examples: 5172 - name: validation num_bytes: 228150083 num_examples: 5172 - name: other num_bytes: 988079897 num_examples: 23570 - name: validated num_bytes: 2621488299 num_examples: 63009 - name: invalidated num_bytes: 208553909 num_examples: 5387 download_size: 3664586106 dataset_size: 4602461490 - config_name: fa features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 239255087 num_examples: 7593 - name: test num_bytes: 217939210 num_examples: 5213 - name: validation num_bytes: 196558067 num_examples: 5213 - name: other num_bytes: 737017546 num_examples: 22510 - name: validated num_bytes: 8120181903 num_examples: 251659 - name: invalidated num_bytes: 499570226 num_examples: 11698 download_size: 8884585819 dataset_size: 10010522039 - config_name: fi features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 16017393 num_examples: 460 - name: test num_bytes: 16117529 num_examples: 428 - name: validation num_bytes: 15471757 num_examples: 415 - name: other num_bytes: 5836400 num_examples: 149 - name: validated num_bytes: 47669391 num_examples: 1305 - name: invalidated num_bytes: 2228215 num_examples: 59 download_size: 49882909 dataset_size: 103340685 - config_name: fr features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 12439892070 num_examples: 298982 - name: test num_bytes: 733943163 num_examples: 15763 - name: validation num_bytes: 703801114 num_examples: 15763 - name: other num_bytes: 117998889 num_examples: 3222 - name: validated num_bytes: 17921836252 num_examples: 461004 - name: invalidated num_bytes: 1794149368 num_examples: 40351 download_size: 19130141984 dataset_size: 33711620856 - config_name: fy-NL features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 159116360 num_examples: 3927 - name: test num_bytes: 126913262 num_examples: 3020 - name: validation num_bytes: 112288554 num_examples: 2790 - name: other num_bytes: 893887467 num_examples: 21569 - name: validated num_bytes: 429651922 num_examples: 10495 - name: invalidated num_bytes: 38985422 num_examples: 1031 download_size: 1237743070 dataset_size: 1760842987 - config_name: ga-IE features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 15396820 num_examples: 541 - name: test num_bytes: 16611739 num_examples: 506 - name: validation num_bytes: 14897739 num_examples: 497 - name: other num_bytes: 61948768 num_examples: 2130 - name: validated num_bytes: 93371649 num_examples: 3352 - name: invalidated num_bytes: 10993268 num_examples: 409 download_size: 156553447 dataset_size: 213219983 - config_name: hi features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 4860737 num_examples: 157 - name: test num_bytes: 4728043 num_examples: 127 - name: validation num_bytes: 5569352 num_examples: 135 - name: other num_bytes: 4176110 num_examples: 139 - name: validated num_bytes: 15158052 num_examples: 419 - name: invalidated num_bytes: 2801051 num_examples: 60 download_size: 21424045 dataset_size: 37293345 - config_name: hsb features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 43049910 num_examples: 808 - name: test num_bytes: 20929094 num_examples: 387 - name: validation num_bytes: 8769458 num_examples: 172 - name: other num_bytes: 3173841 num_examples: 62 - name: validated num_bytes: 72748422 num_examples: 1367 - name: invalidated num_bytes: 5589972 num_examples: 227 download_size: 79362060 dataset_size: 154260697 - config_name: hu features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 126163153 num_examples: 3348 - name: test num_bytes: 57056435 num_examples: 1649 - name: validation num_bytes: 50306925 num_examples: 1434 - name: other num_bytes: 12051094 num_examples: 295 - name: validated num_bytes: 234307671 num_examples: 6457 - name: invalidated num_bytes: 5881521 num_examples: 169 download_size: 242758708 dataset_size: 485766799 - config_name: ia features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 96577153 num_examples: 3477 - name: test num_bytes: 33204678 num_examples: 899 - name: validation num_bytes: 67436779 num_examples: 1601 - name: other num_bytes: 30937041 num_examples: 1095 - name: validated num_bytes: 197248304 num_examples: 5978 - name: invalidated num_bytes: 6769573 num_examples: 192 download_size: 226499645 dataset_size: 432173528 - config_name: id features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 63515863 num_examples: 2130 - name: test num_bytes: 60711104 num_examples: 1844 - name: validation num_bytes: 56963520 num_examples: 1835 - name: other num_bytes: 206578628 num_examples: 6782 - name: validated num_bytes: 272570942 num_examples: 8696 - name: invalidated num_bytes: 16566129 num_examples: 470 download_size: 475918233 dataset_size: 676906186 - config_name: it features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 2555546829 num_examples: 58015 - name: test num_bytes: 656285877 num_examples: 12928 - name: validation num_bytes: 621955330 num_examples: 12928 - name: other num_bytes: 671213467 num_examples: 14549 - name: validated num_bytes: 4552252754 num_examples: 102579 - name: invalidated num_bytes: 564610354 num_examples: 12189 download_size: 5585781573 dataset_size: 9621864611 - config_name: ja features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 27600264 num_examples: 722 - name: test num_bytes: 26475556 num_examples: 632 - name: validation num_bytes: 22098940 num_examples: 586 - name: other num_bytes: 34588931 num_examples: 885 - name: validated num_bytes: 106916400 num_examples: 3072 - name: invalidated num_bytes: 17819020 num_examples: 504 download_size: 152879796 dataset_size: 235499111 - config_name: ka features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 47790695 num_examples: 1058 - name: test num_bytes: 30301524 num_examples: 656 - name: validation num_bytes: 24951079 num_examples: 527 - name: other num_bytes: 2144603 num_examples: 44 - name: validated num_bytes: 104135978 num_examples: 2275 - name: invalidated num_bytes: 7004160 num_examples: 139 download_size: 104280554 dataset_size: 216328039 - config_name: kab features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 3219289101 num_examples: 120530 - name: test num_bytes: 446453041 num_examples: 14622 - name: validation num_bytes: 414159937 num_examples: 14622 - name: other num_bytes: 2282481767 num_examples: 88021 - name: validated num_bytes: 15310455176 num_examples: 573718 - name: invalidated num_bytes: 581587104 num_examples: 18134 download_size: 17171606918 dataset_size: 22254426126 - config_name: ky features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 75460488 num_examples: 1955 - name: test num_bytes: 57116561 num_examples: 1503 - name: validation num_bytes: 61393867 num_examples: 1511 - name: other num_bytes: 258081579 num_examples: 7223 - name: validated num_bytes: 355742823 num_examples: 9236 - name: invalidated num_bytes: 41007711 num_examples: 926 download_size: 579440853 dataset_size: 848803029 - config_name: lg features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 46910479 num_examples: 1250 - name: test num_bytes: 26951803 num_examples: 584 - name: validation num_bytes: 16709367 num_examples: 384 - name: other num_bytes: 111180838 num_examples: 3110 - name: validated num_bytes: 90606863 num_examples: 2220 - name: invalidated num_bytes: 14069959 num_examples: 290 download_size: 208197149 dataset_size: 306429309 - config_name: lt features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 34605356 num_examples: 931 - name: test num_bytes: 19940391 num_examples: 466 - name: validation num_bytes: 10462851 num_examples: 244 - name: other num_bytes: 71150206 num_examples: 1629 - name: validated num_bytes: 65138550 num_examples: 1644 - name: invalidated num_bytes: 4414780 num_examples: 102 download_size: 135299706 dataset_size: 205712134 - config_name: lv features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 67269173 num_examples: 2552 - name: test num_bytes: 56937435 num_examples: 1882 - name: validation num_bytes: 55289058 num_examples: 2002 - name: other num_bytes: 40259801 num_examples: 1560 - name: validated num_bytes: 179726893 num_examples: 6444 - name: invalidated num_bytes: 4383319 num_examples: 143 download_size: 208307691 dataset_size: 403865679 - config_name: mn features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 89913910 num_examples: 2183 - name: test num_bytes: 86737041 num_examples: 1862 - name: validation num_bytes: 82343275 num_examples: 1837 - name: other num_bytes: 146365394 num_examples: 3272 - name: validated num_bytes: 327264827 num_examples: 7487 - name: invalidated num_bytes: 31764232 num_examples: 667 download_size: 486369317 dataset_size: 764388679 - config_name: mt features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 73850815 num_examples: 2036 - name: test num_bytes: 66520195 num_examples: 1617 - name: validation num_bytes: 56412066 num_examples: 1516 - name: other num_bytes: 220666971 num_examples: 5714 - name: validated num_bytes: 218212969 num_examples: 5747 - name: invalidated num_bytes: 12328068 num_examples: 314 download_size: 425114242 dataset_size: 647991084 - config_name: nl features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 321946148 num_examples: 9460 - name: test num_bytes: 205287443 num_examples: 5708 - name: validation num_bytes: 186095353 num_examples: 4938 - name: other num_bytes: 801418 num_examples: 27 - name: validated num_bytes: 1710636990 num_examples: 52488 - name: invalidated num_bytes: 115133112 num_examples: 3308 download_size: 1741827548 dataset_size: 2539900464 - config_name: or features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 16067910 num_examples: 388 - name: test num_bytes: 4270651 num_examples: 98 - name: validation num_bytes: 5485937 num_examples: 129 - name: other num_bytes: 177775963 num_examples: 4302 - name: validated num_bytes: 25824418 num_examples: 615 - name: invalidated num_bytes: 2701922 num_examples: 62 download_size: 199077358 dataset_size: 232126801 - config_name: pa-IN features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 7572499 num_examples: 211 - name: test num_bytes: 4375532 num_examples: 116 - name: validation num_bytes: 1702492 num_examples: 44 - name: other num_bytes: 56683312 num_examples: 1411 - name: validated num_bytes: 13650443 num_examples: 371 - name: invalidated num_bytes: 1690766 num_examples: 43 download_size: 69748265 dataset_size: 85675044 - config_name: pl features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 273394509 num_examples: 7468 - name: test num_bytes: 205047541 num_examples: 5153 - name: validation num_bytes: 195917307 num_examples: 5153 - name: other num_bytes: 442144781 num_examples: 12848 - name: validated num_bytes: 3150860197 num_examples: 90791 - name: invalidated num_bytes: 180801918 num_examples: 4601 download_size: 3537012341 dataset_size: 4448166253 - config_name: pt features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 231451724 num_examples: 6514 - name: test num_bytes: 180108694 num_examples: 4641 - name: validation num_bytes: 165966139 num_examples: 4592 - name: other num_bytes: 283497435 num_examples: 8390 - name: validated num_bytes: 1480529669 num_examples: 41584 - name: invalidated num_bytes: 67948392 num_examples: 1740 download_size: 1704252567 dataset_size: 2409502053 - config_name: rm-sursilv features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 62396326 num_examples: 1384 - name: test num_bytes: 51707733 num_examples: 1194 - name: validation num_bytes: 52114252 num_examples: 1205 - name: other num_bytes: 93351293 num_examples: 2102 - name: validated num_bytes: 166218231 num_examples: 3783 - name: invalidated num_bytes: 30593270 num_examples: 639 download_size: 275950479 dataset_size: 456381105 - config_name: rm-vallader features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 29528457 num_examples: 574 - name: test num_bytes: 18805466 num_examples: 378 - name: validation num_bytes: 17012341 num_examples: 357 - name: other num_bytes: 36890435 num_examples: 727 - name: validated num_bytes: 65711922 num_examples: 1316 - name: invalidated num_bytes: 9356204 num_examples: 374 download_size: 108113989 dataset_size: 177304825 - config_name: ro features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - 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name: other num_bytes: 450644862 num_examples: 10247 - name: validated num_bytes: 3212213931 num_examples: 74256 - name: invalidated num_bytes: 145739451 num_examples: 3056 download_size: 3655676916 dataset_size: 5241280916 - config_name: rw features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 21645788973 num_examples: 515197 - name: test num_bytes: 707959382 num_examples: 15724 - name: validation num_bytes: 698662384 num_examples: 15032 - name: other num_bytes: 923146896 num_examples: 22923 - name: validated num_bytes: 35011249432 num_examples: 832929 - name: invalidated num_bytes: 7969286423 num_examples: 206790 download_size: 42545189583 dataset_size: 66956093490 - config_name: sah features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 68286985 num_examples: 1442 - name: test num_bytes: 38534020 num_examples: 757 - name: validation num_bytes: 17900397 num_examples: 405 - name: other num_bytes: 62594222 num_examples: 1275 - name: validated num_bytes: 124800352 num_examples: 2606 - name: invalidated num_bytes: 3594160 num_examples: 66 download_size: 181245626 dataset_size: 315710136 - config_name: sl features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 66122967 num_examples: 2038 - name: test num_bytes: 26872195 num_examples: 881 - name: validation num_bytes: 16353097 num_examples: 556 - name: other num_bytes: 79268518 num_examples: 2502 - name: validated num_bytes: 148371273 num_examples: 4669 - name: invalidated num_bytes: 3048301 num_examples: 92 download_size: 222751292 dataset_size: 340036351 - config_name: sv-SE features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 62727263 num_examples: 2331 - name: test num_bytes: 59127381 num_examples: 2027 - name: validation num_bytes: 53846355 num_examples: 2019 - name: other num_bytes: 109970049 num_examples: 3043 - name: validated num_bytes: 327049001 num_examples: 12552 - name: invalidated num_bytes: 13462567 num_examples: 462 download_size: 421434184 dataset_size: 626182616 - config_name: ta features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 69052658 num_examples: 2009 - name: test num_bytes: 67616865 num_examples: 1781 - name: validation num_bytes: 63248009 num_examples: 1779 - name: other num_bytes: 246650792 num_examples: 7428 - name: validated num_bytes: 438961956 num_examples: 12652 - name: invalidated num_bytes: 23587453 num_examples: 594 download_size: 679766097 dataset_size: 909117733 - config_name: th features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 100435725 num_examples: 2917 - name: test num_bytes: 82030679 num_examples: 2188 - name: validation num_bytes: 63237632 num_examples: 1922 - name: other num_bytes: 95235301 num_examples: 2671 - name: validated num_bytes: 245734783 num_examples: 7028 - name: invalidated num_bytes: 18247080 num_examples: 467 download_size: 341305736 dataset_size: 604921200 - config_name: tr features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 57879052 num_examples: 1831 - name: test num_bytes: 60268059 num_examples: 1647 - name: validation num_bytes: 54914798 num_examples: 1647 - name: other num_bytes: 10954154 num_examples: 325 - name: validated num_bytes: 585777527 num_examples: 18685 - name: invalidated num_bytes: 59288266 num_examples: 1726 download_size: 620848700 dataset_size: 829081856 - config_name: tt features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 348132697 num_examples: 11211 - name: test num_bytes: 135120057 num_examples: 4485 - name: validation num_bytes: 61690964 num_examples: 2127 - name: other num_bytes: 62158038 num_examples: 1798 - name: validated num_bytes: 767791517 num_examples: 25781 - name: invalidated num_bytes: 10403128 num_examples: 287 download_size: 777153207 dataset_size: 1385296401 - config_name: uk features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 161925063 num_examples: 4035 - name: test num_bytes: 138422211 num_examples: 3235 - name: validation num_bytes: 135483169 num_examples: 3236 - name: other num_bytes: 327979131 num_examples: 8161 - name: validated num_bytes: 889863965 num_examples: 22337 - name: invalidated num_bytes: 55745301 num_examples: 1255 download_size: 1218559031 dataset_size: 1709418840 - config_name: vi features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 6244454 num_examples: 221 - name: test num_bytes: 6656365 num_examples: 198 - name: validation num_bytes: 6531856 num_examples: 200 - name: other num_bytes: 31315434 num_examples: 870 - name: validated num_bytes: 19432595 num_examples: 619 - name: invalidated num_bytes: 2981661 num_examples: 78 download_size: 51929480 dataset_size: 73162365 - config_name: vot features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 146467 num_examples: 3 - name: test - name: validation - name: other num_bytes: 7963322 num_examples: 411 - name: validated num_bytes: 146467 num_examples: 3 - name: invalidated num_bytes: 107949 num_examples: 6 download_size: 7792602 dataset_size: 8364205 - config_name: zh-CN features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 793667379 num_examples: 18541 - name: test num_bytes: 420202544 num_examples: 8760 - name: validation num_bytes: 396096323 num_examples: 8743 - name: other num_bytes: 381264783 num_examples: 8948 - name: validated num_bytes: 1618113625 num_examples: 36405 - name: invalidated num_bytes: 266234479 num_examples: 5305 download_size: 2184602350 dataset_size: 3875579133 - config_name: zh-HK features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 221459521 num_examples: 7506 - name: test num_bytes: 217627041 num_examples: 5172 - name: validation num_bytes: 196071110 num_examples: 5172 - name: other num_bytes: 1319233252 num_examples: 38830 - name: validated num_bytes: 1482087591 num_examples: 41835 - name: invalidated num_bytes: 124170969 num_examples: 2999 download_size: 2774145806 dataset_size: 3560649484 - config_name: zh-TW features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string splits: - name: train num_bytes: 97323787 num_examples: 3507 - name: test num_bytes: 85512325 num_examples: 2895 - name: validation num_bytes: 80402637 num_examples: 2895 - name: other num_bytes: 623801957 num_examples: 22477 - name: validated num_bytes: 1568842090 num_examples: 61232 - name: invalidated num_bytes: 100241443 num_examples: 3584 download_size: 2182836295 dataset_size: 2556124239 config_names: - ab - ar - as - br - ca - cnh - cs - cv - cy - de - dv - el - en - eo - es - et - eu - fa - fi - fr - fy-NL - ga-IE - hi - hsb - hu - ia - id - it - ja - ka - kab - ky - lg - lt - lv - mn - mt - nl - or - pa-IN - pl - pt - rm-sursilv - rm-vallader - ro - ru - rw - sah - sl - sv-SE - ta - th - tr - tt - uk - vi - vot - zh-CN - zh-HK - zh-TW --- # Dataset Card for common_voice <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Deprecated:</b> Dataset "common_voice" is deprecated and will soon be deleted. Use datasets under <a href="https://huggingface.co/mozilla-foundation">mozilla-foundation</a> organisation instead. For example, you can load <a href="https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0">Common Voice 13</a> dataset via <code>load_dataset("mozilla-foundation/common_voice_13_0", "en")</code></p> </div> ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://commonvoice.mozilla.org/en/datasets - **Repository:** https://github.com/common-voice/common-voice - **Paper:** https://commonvoice.mozilla.org/en/datasets - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 9,283 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help train the accuracy of speech recognition engines. The dataset currently consists of 7,335 validated hours in 60 languages, but were always adding more voices and languages. Take a look at our Languages page to request a language or start contributing. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages English ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file, called path and its sentence. Additional fields include accent, age, client_id, up_votes down_votes, gender, locale and segment. ` {'accent': 'netherlands', 'age': 'fourties', 'client_id': 'bbbcb732e0f422150c30ff3654bbab572e2a617da107bca22ff8b89ab2e4f124d03b6a92c48322862f60bd0179ae07baf0f9b4f9c4e11d581e0cec70f703ba54', 'down_votes': 0, 'gender': 'male', 'locale': 'nl', 'path': 'nl/clips/common_voice_nl_23522441.mp3', 'segment': "''", 'sentence': 'Ik vind dat een dubieuze procedure.', 'up_votes': 2, 'audio': {'path': `nl/clips/common_voice_nl_23522441.mp3', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 48000} ` ### Data Fields client_id: An id for which client (voice) made the recording path: The path to the audio file audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. sentence: The sentence the user was prompted to speak up_votes: How many upvotes the audio file has received from reviewers down_votes: How many downvotes the audio file has received from reviewers age: The age of the speaker. gender: The gender of the speaker accent: Accent of the speaker locale: The locale of the speaker segment: Usually empty field ### Data Splits The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other. The validated data is data that has been validated with reviewers and recieved upvotes that the data is of high quality. The invalidated data is data has been invalidated by reviewers and recieved downvotes that the data is of low quality. The reported data is data that has been reported, for different reasons. The other data is data that has not yet been reviewed. The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train. ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ## Considerations for Using the Data ### Social Impact of Dataset The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/) ### Citation Information ``` @inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 } ``` ### Contributions Thanks to [@BirgerMoell](https://github.com/BirgerMoell) for adding this dataset.

AI4Sec/cti-bench

AI4Sec

2024-08-17T00:58:57Z

2,536

6

[ "task_categories:zero-shot-classification", "task_categories:question-answering", "task_categories:text-classification", "language:en", "license:cc-by-nc-sa-4.0", "size_categories:1K<n<10K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2406.07599", "doi:10.57967/hf/2506", "region:us", "cti", "cyber threat intelligence", "llm" ]

[ "zero-shot-classification", "question-answering", "text-classification" ]

2024-06-12T10:24:23Z

2

--- license: cc-by-nc-sa-4.0 task_categories: - zero-shot-classification - question-answering - text-classification language: - en tags: - cti - cyber threat intelligence - llm pretty_name: CTIBench size_categories: - 1K<n<10K configs: - config_name: cti-mcq data_files: - split: test path: "cti-mcq.tsv" sep: "\t" - config_name: cti-rcm data_files: - split: test path: "cti-rcm.tsv" sep: "\t" - config_name: cti-vsp data_files: - split: test path: "cti-vsp.tsv" sep: "\t" - config_name: cti-taa data_files: - split: test path: "cti-taa.tsv" sep: "\t" - config_name: cti-ate data_files: - split: test path: "cti-ate.tsv" sep: "\t" - config_name: cti-rcm-2021 data_files: - split: test path: "cti-rcm-2021.tsv" sep: "\t" --- # Dataset Card for CTIBench  A set of benchmark tasks designed to evaluate large language models (LLMs) on cyber threat intelligence (CTI) tasks. ## Dataset Details ### Dataset Description  CTIBench is a comprehensive suite of benchmark tasks and datasets designed to evaluate LLMs in the field of CTI. Components: - CTI-MCQ: A knowledge evaluation dataset with multiple-choice questions to assess the LLMs' understanding of CTI standards, threats, detection strategies, mitigation plans, and best practices. This dataset is built using authoritative sources and standards within the CTI domain, including NIST, MITRE, and GDPR. - CTI-RCM: A practical task that involves mapping Common Vulnerabilities and Exposures (CVE) descriptions to Common Weakness Enumeration (CWE) categories. This task evaluates the LLMs' ability to understand and classify cyber threats. - CTI-VSP: Another practical task that requires calculating the Common Vulnerability Scoring System (CVSS) scores. This task assesses the LLMs' ability to evaluate the severity of cyber vulnerabilities. - CTI-TAA: A task that involves analyzing publicly available threat reports and attributing them to specific threat actors or malware families. This task tests the LLMs' capability to understand historical cyber threat behavior and identify meaningful correlations. - **Curated by:** Md Tanvirul Alam & Dipkamal Bhusal (RIT)  ### Dataset Sources  **Repository:** https://github.com/xashru/cti-bench  ## Uses  CTIBench is designed to provide a comprehensive evaluation framework for large language models (LLMs) within the domain of cyber threat intelligence (CTI). Dataset designed in CTIBench assess the understanding of CTI standards, threats, detection strategies, mitigation plans, and best practices by LLMs, and evaluates the LLMs' ability to understand, and analyze about cyber threats and vulnerabilities.     ## Dataset Structure  The dataset consists of 5 TSV files, each corresponding to a different task. Each TSV file contains a "Prompt" column used to pose questions to the LLM. Most files also include a "GT" column that contains the ground truth for the questions, except for "cti-taa.tsv". The evaluation scripts for the different tasks are available in the associated GitHub repository. ## Dataset Creation ### Curation Rationale  This dataset was curated to evaluate the ability of LLMs to understand and analyze various aspects of open-source CTI. ### Source Data  The dataset includes URLs indicating the sources from which the data was collected.   #### Personal and Sensitive Information  The dataset does not contain any personal or sensitive information.   ## Citation The paper can be found at: https://arxiv.org/abs/2406.07599 **BibTeX:** ```bibtex @misc{alam2024ctibench, title={CTIBench: A Benchmark for Evaluating LLMs in Cyber Threat Intelligence}, author={Md Tanvirul Alam and Dipkamal Bhushal and Le Nguyen and Nidhi Rastogi}, year={2024}, eprint={2406.07599}, archivePrefix={arXiv}, primaryClass={cs.CR} } ```     ## Dataset Card Contact Md Tanvirul Alam (ma8235 @ rit . edu)

arcee-ai/The-Tome

arcee-ai

2024-08-15T16:08:27Z

267

90

[ "license:mit", "size_categories:1M<n<10M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-07-22T01:51:38Z

null

--- license: mit --- <div align="center"> <img src="https://i.ibb.co/0jqCGH6/LEW5-CGBKRv-CWKNf-KYkf-k-Q.jpg" alt="The Tome" style="border-radius: 10px; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19); max-width: 100%; height: auto;"> </div> The Tome is a curated dataset designed for training large language models with a focus on instruction following. It was used in the training of our Arcee-Nova/Spark models, which was later merged with Qwen2-72B-Instruct (or 7B with the Spark model). ## Dataset Composition - **Total Samples**: 1.75 million - **Source**: Compiled from 9 publicly available datasets The Tome is comprised of the following datasets: ```markdown arcee-ai/infini-instruct-top-500k (BAAI/Infinity-Instruct) TIGER-Lab/WebInstructSub (top-500k) jondurbin/airoboros-3.2 gardner/glaive-function-calling-v2-sharegpt arcee-ai/reasoning-sharegpt (SkunkworksAI/reasoning-0.01) arcee-ai/self-instruct-sharegpt (bigcode/self-oss-instruct-sc2-exec-filter-50k) cognitivecomputations/ultrainteract_trajectories_sharegpt cognitivecomputations/SystemChat-2.0 arcee-ai/qwen2-72b-magpie-en ``` ## Curation Process The dataset underwent a curation process to ensure high-quality content: 1. **Reranker**: Applied for instruction following on Infini-Instruct and WebInstruct. 2. **Educational Value Scoring**: Used the fineweb-edu classifier on Infini-Instruct and WebInstruct 3. **Composite Scoring**: Scores from the custom reranker and fineweb-edu classifier were averaged. ## Usage in Model Training The Tome was instrumental in the development of the Nova model, which was subsequently merged with Qwen2-72B-Instruct: - **Merge Process**: - Lower layers primarily from Qwen2-72B-Instruct - Higher layers primarily from Nova-Premerge

NbAiLab/NPSC

NbAiLab

2024-08-14T13:57:39Z

557

8

[ "task_categories:automatic-speech-recognition", "task_categories:audio-classification", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:no", "language:nb", "language:nn", "license:cc0-1.0", "region:us", "speech-modeling" ]

[ "automatic-speech-recognition", "audio-classification" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - no-annotation language_creators: - found language: - 'no' - nb - nn license: - cc0-1.0 multilinguality: - monolingual size_categories: - 2G<n<1B source_datasets: - original task_categories: - automatic-speech-recognition - audio-classification pretty_name: NPSC tags: - speech-modeling --- # Dataset Card for NbAiLab/NPSC ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Data Fields](#data-fiels) - [Dataset Creation](#dataset-creation) - [Statistics](#statistics) - [Document Types](#document-types) - [Languages](#languages) - [Publish Periode](#publish-periode) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** https://www.nb.no/sprakbanken/ - **Repository:** https://www.nb.no/sprakbanken/ressurskatalog/oai-nb-no-sbr-58/ - **Paper:** https://www.nb.no/sprakbanken/ - **Point of Contact:** [Per Erik Solberg](mailto:[email protected]) The Norwegian Parliamentary Speech Corpus (NPSC) is a speech corpus made by the Norwegian Language Bank at the National Library of Norway in 2019-2021. The NPSC consists of recordings of speech from Stortinget, the Norwegian parliament, and corresponding orthographic transcriptions to Norwegian Bokmål and Norwegian Nynorsk. All transcriptions are done manually by trained linguists or philologists, and the manual transcriptions are subsequently proofread to ensure consistency and accuracy. Entire days of Parliamentary meetings are transcribed in the dataset. This repository contains a version of the NPSC in the 🤗 Dataset Format. Note that the official release of the dataset, which can be found in [the repository of the Norwegian Language Bank](https://www.nb.no/sprakbanken/ressurskatalog/oai-nb-no-sbr-58/), contains more information than the version found here, including word-level metadata, metadata about the speakers, and detailed documentation. ## How to Use ```python # Loads the 16K Bokmål corpus in streaming mode from datasets import load_dataset data = load_dataset("NbAiLab/NPSC", config="16K_mp3_bokmaal", streaming=True) ``` ## Dataset Summary The NPSC dataset contains JSON lines with language training data. The data loader will add audio data to this structure. Here is an example json object: ```json { "sentence_id": 49853, "sentence_order": 0, "speaker_id": 32, "meeting_date": "20170110", "speaker_name": "Olemic Thommessen", "sentence_text": "Stortingets møte er lovlig satt", "sentence_language_code": "nb-NO", "text": "Stortingets møte er lovlig satt", "start_time": 320246, "end_time": 323590, "normsentence_text": "Stortingets møte er lovlig satt", "transsentence_text": "Stortingets møte er lovleg sett", "translated": 1, "audio": {"path": "audio/20170110-095504_320246_323590.wav","array": [.......]} } ``` ## Data Fields |**Key** | **Type** | **Description** | |:-----------|:------------|:------------| |**sentence_id:** | Integer | Unique identifier of the sentence | |**sentence_order** | Integer | A number indicating the order of the sentences in the meeting | |**speaker_id** | Integer | The ID of the speaker. This can be linked to the original dataset containing thorough demographic and dialectal information about the speaker. | |**meeting_date** | String | The date for the meeting in the format __yyyymmdd__ | | **speaker_name** | String | Name of the speaker. All speakers were members of the Norwegian Parliament or members of the Norwegian Government at the meeting date | | **sentence_text** | String | The sentence text. The transcribed text string of the sentence in non-normalized form. This is the text of the manual transcriptions, without any postprocessing (apart from corrections of known errors). It may contain interrupted words, non-standard words and function words with a pronunciation deviating from the written form. Detailed metadata about the words in the sentence can be found in the word-tokenized version of the corpus in the official release of the dataset. | | **sentence_language_code** | String | The language code of the sentence. The following alternatives exists in the file: ['nb-NO'. 'nn-NO', 'en-US']| | **text** | String | sentence text. This is a copy of "sentence_text". It is included here to make it more convenient to interleave with other datasets.| | **start_time** | Integer | The start time of the sentence in milliseconds. This time is relative to the start of audiofile of the entire meeting, which can be accessed in the official release | | **end_time** | Integer | End time. See comment above. | | **normsentence_text** | String | Normalized sentence text. In this version of the transcription, numbers and dates are written in digits on standardized formats, and common abbreviations are used. These modifications to the original transcriptions are produced automatically using normalization grammars | | **transsentence_text** | String | Translated sentence text. Whenever the original transcription is in Bokmål (nb-NO), this field contains a machine-translated version in Nynorsk (nn-NO), and vice versa | | **translated** | Integer | A flag indicating whether a machine-translated version has been produced or not. Sentences in en-US have not been translated | | **audio** | Array | The dataloader will encode the accociated audio files and provide them as an array containing 'path', 'sound array','sampling_rate' | #### Initial Data Collection The procedure for the dataset creation is described in detail in our paper. ## Statistics | Feature | Value | |:---------|-----------:| | Duration, pauses included | 140,3 hours| | Duration, pauses not included | 125,7 hours | | Word count | 1,2 million | | Sentence count | 64.531 | | Language distribution | Nynorsk: 12,8%| | | Bokmål: 87,2%| | Gender distribution | Female: 38,3% | | | Male: 61.7% | ## Considerations for Using the Data This corpus contains speech data. All recordings are of members of Parliament in a public setting, and can be distributed without any restrains. ### Dataset Creators and Curators The content of the dataset was created by the Norwegian Language Bank (Språkbanken) at the National Library of Norway. [Javier de la Rosa](mailto:[email protected]), [Freddy Wetjen](mailto:[email protected]), [Per Egil Kummervold](mailto:[email protected]), and [Andre Kaasen](mailto:[email protected]) all contributed in making this into a HuggingFace Dataset. Thanks to the HuggingFace team for assistance. ## License The sound and the transcriptions are released under the [CC-ZERO-license](https://creativecommons.org/publicdomain/zero/1.0/). The curation of the HuggingFace Dataset is released under [CC-BY-SA-3-license](https://creativecommons.org/licenses/by-sa/3.0/). ### Citation Information The following article gives detailed information about the corpus. Please refer to the article and this page if you are using this dataset: ``` @inproceedings{solberg2022norwegian, title={The Norwegian Parliamentary Speech Corpus}, author={Solberg, Per Erik and Ortiz, Pablo}, booktitle={Proceedings of the 13th Language Resources and Evaluation Conference}, url={http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.106.pdf}, year={2022} } ```

keithito/lj_speech

keithito

2024-08-14T11:13:15Z

1,034

54

[ "task_categories:automatic-speech-recognition", "task_categories:text-to-speech", "task_categories:text-to-audio", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:unlicense", "size_categories:10K<n<100K", "region:us" ]

[ "automatic-speech-recognition", "text-to-speech", "text-to-audio" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - expert-generated language_creators: - found language: - en license: - unlicense multilinguality: - monolingual paperswithcode_id: ljspeech pretty_name: LJ Speech size_categories: - 10K<n<100K source_datasets: - original task_categories: - automatic-speech-recognition - text-to-speech - text-to-audio task_ids: [] train-eval-index: - config: main task: automatic-speech-recognition task_id: speech_recognition splits: train_split: train col_mapping: file: path text: text metrics: - type: wer name: WER - type: cer name: CER dataset_info: features: - name: id dtype: string - name: audio dtype: audio: sampling_rate: 22050 - name: file dtype: string - name: text dtype: string - name: normalized_text dtype: string config_name: main splits: - name: train num_bytes: 4667022 num_examples: 13100 download_size: 2748572632 dataset_size: 4667022 --- # Dataset Card for lj_speech ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [The LJ Speech Dataset](https://keithito.com/LJ-Speech-Dataset/) - **Repository:** [N/A] - **Paper:** [N/A] - **Leaderboard:** [Paperswithcode Leaderboard](https://paperswithcode.com/sota/text-to-speech-synthesis-on-ljspeech) - **Point of Contact:** [Keith Ito](mailto:[email protected]) ### Dataset Summary This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books in English. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours. The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded in 2016-17 by the LibriVox project and is also in the public domain. ### Supported Tasks and Leaderboards The dataset can be used to train a model for Automatic Speech Recognition (ASR) or Text-to-Speech (TTS). - `automatic-speech-recognition`: An ASR model is presented with an audio file and asked to transcribe the audio file to written text. The most common ASR evaluation metric is the word error rate (WER). - `text-to-speech`, `text-to-audio`: A TTS model is given a written text in natural language and asked to generate a speech audio file. A reasonable evaluation metric is the mean opinion score (MOS) of audio quality. The dataset has an active leaderboard which can be found at https://paperswithcode.com/sota/text-to-speech-synthesis-on-ljspeech ### Languages The transcriptions and audio are in English. ## Dataset Structure ### Data Instances A data point comprises the path to the audio file, called `file` and its transcription, called `text`. A normalized version of the text is also provided. ``` { 'id': 'LJ002-0026', 'file': '/datasets/downloads/extracted/05bfe561f096e4c52667e3639af495226afe4e5d08763f2d76d069e7a453c543/LJSpeech-1.1/wavs/LJ002-0026.wav', 'audio': {'path': '/datasets/downloads/extracted/05bfe561f096e4c52667e3639af495226afe4e5d08763f2d76d069e7a453c543/LJSpeech-1.1/wavs/LJ002-0026.wav', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 22050}, 'text': 'in the three years between 1813 and 1816,' 'normalized_text': 'in the three years between eighteen thirteen and eighteen sixteen,', } ``` Each audio file is a single-channel 16-bit PCM WAV with a sample rate of 22050 Hz. ### Data Fields - id: unique id of the data sample. - file: a path to the downloaded audio file in .wav format. - audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. - text: the transcription of the audio file. - normalized_text: the transcription with numbers, ordinals, and monetary units expanded into full words. ### Data Splits The dataset is not pre-split. Some statistics: - Total Clips: 13,100 - Total Words: 225,715 - Total Characters: 1,308,678 - Total Duration: 23:55:17 - Mean Clip Duration: 6.57 sec - Min Clip Duration: 1.11 sec - Max Clip Duration: 10.10 sec - Mean Words per Clip: 17.23 - Distinct Words: 13,821 ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization This dataset consists of excerpts from the following works: - Morris, William, et al. Arts and Crafts Essays. 1893. - Griffiths, Arthur. The Chronicles of Newgate, Vol. 2. 1884. - Roosevelt, Franklin D. The Fireside Chats of Franklin Delano Roosevelt. 1933-42. - Harland, Marion. Marion Harland's Cookery for Beginners. 1893. - Rolt-Wheeler, Francis. The Science - History of the Universe, Vol. 5: Biology. 1910. - Banks, Edgar J. The Seven Wonders of the Ancient World. 1916. - President's Commission on the Assassination of President Kennedy. Report of the President's Commission on the Assassination of President Kennedy. 1964. Some details about normalization: - The normalized transcription has the numbers, ordinals, and monetary units expanded into full words (UTF-8) - 19 of the transcriptions contain non-ASCII characters (for example, LJ016-0257 contains "raison d'être"). - The following abbreviations appear in the text. They may be expanded as follows: | Abbreviation | Expansion | |--------------|-----------| | Mr. | Mister | | Mrs. | Misess (*) | | Dr. | Doctor | | No. | Number | | St. | Saint | | Co. | Company | | Jr. | Junior | | Maj. | Major | | Gen. | General | | Drs. | Doctors | | Rev. | Reverend | | Lt. | Lieutenant | | Hon. | Honorable | | Sgt. | Sergeant | | Capt. | Captain | | Esq. | Esquire | | Ltd. | Limited | | Col. | Colonel | | Ft. | Fort | (*) there's no standard expansion for "Mrs." #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process - The audio clips range in length from approximately 1 second to 10 seconds. They were segmented automatically based on silences in the recording. Clip boundaries generally align with sentence or clause boundaries, but not always. - The text was matched to the audio manually, and a QA pass was done to ensure that the text accurately matched the words spoken in the audio. #### Who are the annotators? Recordings by Linda Johnson from LibriVox. Alignment and annotation by Keith Ito. ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations - The original LibriVox recordings were distributed as 128 kbps MP3 files. As a result, they may contain artifacts introduced by the MP3 encoding. ## Additional Information ### Dataset Curators The dataset was initially created by Keith Ito and Linda Johnson. ### Licensing Information Public Domain ([LibriVox](https://librivox.org/pages/public-domain/)) ### Citation Information ``` @misc{ljspeech17, author = {Keith Ito and Linda Johnson}, title = {The LJ Speech Dataset}, howpublished = {\url{https://keithito.com/LJ-Speech-Dataset/}}, year = 2017 } ``` ### Contributions Thanks to [@anton-l](https://github.com/anton-l) for adding this dataset.

openslr/librispeech_asr

openslr

2024-08-14T10:48:50Z

12,204

144

[ "task_categories:automatic-speech-recognition", "task_categories:audio-classification", "task_ids:speaker-identification", "annotations_creators:expert-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-4.0", "size_categories:100K<n<1M", "region:us" ]

[ "automatic-speech-recognition", "audio-classification" ]

2022-03-02T23:29:22Z

null

--- pretty_name: LibriSpeech annotations_creators: - expert-generated language_creators: - crowdsourced - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual paperswithcode_id: librispeech-1 size_categories: - 100K<n<1M source_datasets: - original task_categories: - automatic-speech-recognition - audio-classification task_ids: - speaker-identification dataset_info: - config_name: clean features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string splits: - name: train.100 num_bytes: 6619683041 num_examples: 28539 - name: train.360 num_bytes: 23898214592 num_examples: 104014 - name: validation num_bytes: 359572231 num_examples: 2703 - name: test num_bytes: 367705423 num_examples: 2620 download_size: 30121377654 dataset_size: 31245175287 - config_name: other features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string splits: - name: train.500 num_bytes: 31810256902 num_examples: 148688 - name: validation num_bytes: 337283304 num_examples: 2864 - name: test num_bytes: 352396474 num_examples: 2939 download_size: 31236565377 dataset_size: 32499936680 - config_name: all features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string splits: - name: train.clean.100 num_bytes: 6627791685 num_examples: 28539 - name: train.clean.360 num_bytes: 23927767570 num_examples: 104014 - name: train.other.500 num_bytes: 31852502880 num_examples: 148688 - name: validation.clean num_bytes: 359505691 num_examples: 2703 - name: validation.other num_bytes: 337213112 num_examples: 2864 - name: test.clean num_bytes: 368449831 num_examples: 2620 - name: test.other num_bytes: 353231518 num_examples: 2939 download_size: 61357943031 dataset_size: 63826462287 --- # Dataset Card for librispeech_asr ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [LibriSpeech ASR corpus](http://www.openslr.org/12) - **Repository:** [Needs More Information] - **Paper:** [LibriSpeech: An ASR Corpus Based On Public Domain Audio Books](https://www.danielpovey.com/files/2015_icassp_librispeech.pdf) - **Leaderboard:** [The 🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench) - **Point of Contact:** [Daniel Povey](mailto:[email protected]) ### Dataset Summary LibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned. ### Supported Tasks and Leaderboards - `automatic-speech-recognition`, `audio-speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active Hugging Face leaderboard which can be found at https://huggingface.co/spaces/huggingface/hf-speech-bench. The leaderboard ranks models uploaded to the Hub based on their WER. An external leaderboard at https://paperswithcode.com/sota/speech-recognition-on-librispeech-test-clean ranks the latest models from research and academia. ### Languages The audio is in English. There are two configurations: `clean` and `other`. The speakers in the corpus were ranked according to the WER of the transcripts of a model trained on a different dataset, and were divided roughly in the middle, with the lower-WER speakers designated as "clean" and the higher WER speakers designated as "other". ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided. ``` {'chapter_id': 141231, 'file': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac', 'audio': {'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'id': '1272-141231-0000', 'speaker_id': 1272, 'text': 'A MAN SAID TO THE UNIVERSE SIR I EXIST'} ``` ### Data Fields - file: A path to the downloaded audio file in .flac format. - audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. - text: the transcription of the audio file. - id: unique id of the data sample. - speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples. - chapter_id: id of the audiobook chapter which includes the transcription. ### Data Splits The size of the corpus makes it impractical, or at least inconvenient for some users, to distribute it as a single large archive. Thus the training portion of the corpus is split into three subsets, with approximate size 100, 360 and 500 hours respectively. A simple automatic procedure was used to select the audio in the first two sets to be, on average, of higher recording quality and with accents closer to US English. An acoustic model was trained on WSJ’s si-84 data subset and was used to recognize the audio in the corpus, using a bigram LM estimated on the text of the respective books. We computed the Word Error Rate (WER) of this automatic transcript relative to our reference transcripts obtained from the book texts. The speakers in the corpus were ranked according to the WER of the WSJ model’s transcripts, and were divided roughly in the middle, with the lower-WER speakers designated as "clean" and the higher-WER speakers designated as "other". For "clean", the data is split into train, validation, and test set. The train set is further split into train.100 and train.360 respectively accounting for 100h and 360h of the training data. For "other", the data is split into train, validation, and test set. The train set contains approximately 500h of recorded speech. | | Train.500 | Train.360 | Train.100 | Valid | Test | | ----- | ------ | ----- | ---- | ---- | ---- | | clean | - | 104014 | 28539 | 2703 | 2620| | other | 148688 | - | - | 2864 | 2939 | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators The dataset was initially created by Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur. ### Licensing Information [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @inproceedings{panayotov2015librispeech, title={Librispeech: an ASR corpus based on public domain audio books}, author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev}, booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on}, pages={5206--5210}, year={2015}, organization={IEEE} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

HuggingFaceTB/cosmopedia

HuggingFaceTB

2024-08-12T22:05:49Z

38,739

608

[ "language:en", "license:apache-2.0", "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2309.05463", "arxiv:2306.11644", "region:us", "synthetic" ]

[]

2024-02-18T20:23:48Z

null

--- dataset_info: - config_name: auto_math_text features: - name: prompt dtype: string - name: text_token_length dtype: int64 - name: text dtype: string - name: seed_data dtype: string - name: format dtype: string - name: audience dtype: string splits: - name: train num_bytes: 8777587297.907892 num_examples: 1949895 download_size: 4461401898 dataset_size: 8777587297.907892 - config_name: khanacademy features: - name: prompt dtype: string - name: text_token_length dtype: int64 - name: text dtype: string - name: seed_data dtype: string - name: format dtype: string - name: audience dtype: string splits: - name: train num_bytes: 108591354.09210858 num_examples: 24123 download_size: 49139761 dataset_size: 108591354.09210858 - config_name: openstax features: - name: text_token_length dtype: int64 - name: prompt dtype: string - name: text dtype: string - name: seed_data dtype: string - name: format dtype: string - name: audience dtype: string splits: - name: train num_bytes: 667837450 num_examples: 126332 download_size: 346992522 dataset_size: 667837450 - config_name: stanford features: - name: text_token_length dtype: int64 - name: prompt dtype: string - name: text dtype: string - name: seed_data dtype: string - name: format dtype: string - name: audience dtype: string splits: - name: train num_bytes: 6341291506 num_examples: 1020024 download_size: 3302284560 dataset_size: 6341291506 - config_name: stories features: - name: text dtype: string - name: prompt dtype: string - name: text_token_length dtype: int64 - name: seed_data dtype: string - name: format dtype: string - name: audience dtype: string splits: - name: train num_bytes: 21314739648 num_examples: 4992964 download_size: 11902294709 dataset_size: 21314739648 - config_name: web_samples_v1 features: - name: text_token_length dtype: int64 - name: prompt dtype: string - name: text dtype: string - name: seed_data dtype: string - name: format dtype: string - name: audience dtype: string splits: - name: train num_bytes: 69075726295 num_examples: 12426348 download_size: 38978124936 dataset_size: 69075726295 - config_name: web_samples_v2 features: - name: text_token_length dtype: int64 - name: prompt dtype: string - name: text dtype: string - name: seed_data dtype: string - name: format dtype: string - name: audience dtype: string splits: - name: train num_bytes: 58711802939 num_examples: 10345867 download_size: 32658254617 dataset_size: 58711802939 - config_name: wikihow features: - name: text_token_length dtype: int64 - name: prompt dtype: string - name: text dtype: string - name: seed_data dtype: string - name: format dtype: string - name: audience dtype: string splits: - name: train num_bytes: 892720528 num_examples: 179191 download_size: 502284600 dataset_size: 892720528 configs: - config_name: auto_math_text data_files: - split: train path: data/auto_math_text/train-* - config_name: khanacademy data_files: - split: train path: data/khanacademy/train-* - config_name: openstax data_files: - split: train path: data/openstax/train-* - config_name: stanford data_files: - split: train path: data/stanford/train-* - config_name: stories data_files: - split: train path: data/stories/train-* - config_name: web_samples_v1 data_files: - split: train path: data/web_samples_v1/train-* - config_name: web_samples_v2 data_files: - split: train path: data/web_samples_v2/train-* - config_name: wikihow data_files: - split: train path: data/wikihow/train-* license: apache-2.0 language: - en tags: - synthetic --- # Cosmopedia v0.1 <center> <img src="https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/8a9ZTW8sC4utjEPIrZegN.png" alt="Cosmopedia v0.1" width="600" height="300"> <p><em>Image generated by DALL-E, the <a href="https://huggingface.co/datasets/HuggingFaceTB/miscellaneous/blob/main/cosmopedia_dalle_prompt_by_mixtral.txt">prompt</a> was generated by Mixtral-8x7B-Instruct-v0.1</em></p> </center> **Note: Cosmopedia v0.2 is available at [smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus)** ``` User: What do you think "Cosmopedia" could mean? Hint: in our case it's not related to cosmology. Mixtral-8x7B-Instruct-v0.1: A possible meaning for "Cosmopedia" could be an encyclopedia or collection of information about different cultures, societies, and topics from around the world, emphasizing diversity and global connectedness. ``` **Cosmopedia** is a dataset of synthetic textbooks, blogposts, stories, posts and WikiHow articles generated by [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1).The dataset contains over **30 million files** and **25 billion tokens**, making it the largest open synthetic dataset to date. It covers a variety of topics; we tried to map world knowledge present in Web datasets like [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) and [RedPajama](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T), and generate synthetic content that covers them. This is the v0.1 of Cosmopedia, with ample room for improvement and topics to be more comprehensively covered. We hope this dataset will help the community's research efforts in the increasingly intriguing domain of synthetic data. You can find a clickable map by Nomic at [https://atlas.nomic.ai/map/cosmopedia](https://atlas.nomic.ai/map/cosmopedia). This work is inspired by the great work of [Phi1.5](https://huggingface.co/papers/2309.05463). You can find more details about the dataset in our **blog post**: https://huggingface.co/blog/cosmopedia # TL;DR This is a synthetic dataset of 30M samples generated by [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1). It contains 8 splits depending on the source of the seed samples we use in the prompts, the model is asked to generate content related to them. The splits range from web samples to educational resources like Stanford, OpenStax and KhanAcademy, we also use some instruction-tuning datasets as seed samples for stories. Here's how you can load a dataset split: ```python from datasets import load_dataset ds = load_dataset("HuggingFaceTB/cosmopedia", "stories", split="train", num_proc=12) ds[0] ``` If you want a smaller subset of the dataset check [Cosmopedia-100k](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia-100k). We also trained a 1.8B model on Cosmopedia [Cosmo-1B](https://huggingface.co/HuggingFaceTB/cosmopedian-1b). # Dataset splits The prompts are all based on the concept of using a seed sample (for example an extract from a web page) and asking the model to generate new content (textbook, story, blogpost..) related to that seed sample. The dataset consist of 8 splits depending on the source of the seed data used in the split. Some seed samples may appear more than once when we ask for a different style (e.g academic textbook vs blogpost) or audience (e.g young children vs college students). For example, each sample in `stanford` was used with 4 different prompt styles and audiences, check the `format` and `audience` columns for more details. We observed that tailoring the audience and prompt style accordingly significantly enhances diversity; the proportion of duplicates eliminated via MinHash was under 1%. The graph below shows the distribution of seed datasets, generations formats and audiences in Cosmopedia: <center> <img src="https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/V7MGV2OrCfLO5TxKPUXs4.png" alt="distributions" width="1000" height="500"> </center> Below are the 8 splits: - `web_samples_v1`: this and `web_samples_v2` are the largest splits (they make up~75% of the dataset), where we use samples from an internal web dataset similar to [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb). These samples were selected based on their topic, using a clustering method explained in the section below. - `web_samples_v2`: similar to `web_samples_v2` using different samples. We call it v2 because we refined the prompts for this split (e.g asking for more depth over breadth in the concepts explanations and requesting the model to not generate a title and introductory sentences, which might be redundant across samples). - `stanford`: we scraped course outlines from [stanford.edu](https://explorecourses.stanford.edu/search?q=all%20courses), and each time we prompt the model with one of the course units. - `stories`: we generated stories to add some commonsense and day-to-day knowledge aspect to the dataset. For this split we use samples from [UltraChat](https://huggingface.co/datasets/stingning/ultrachat) -only questions about the world [subset](https://huggingface.co/datasets/loubnabnl/ultrachat_questions_about_world)- and [OpenHermes2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5). These are synthetic instruction-tuning datasets that are already curated and cover a wide range of topics. - `wikihow`: in this split, we asked the model to generate WikiHow articles from WikiHow titles that we scraped, the list is avilable [here](https://github.com/huggingface/cosmopedia/blob/main/prompts/wikihow/wikihowcom-20231012-titles.txt). Note that you can find more WikiHow articles in the other splits by looking for it in the `format` column. - `openstax`: we scraped course outlines with unit introductions from [OpenStax](https://openstax.org/), a resource suggested by [AFAIK](https://afaik.io/) team. - `khanacademy`: we scraped the outlines for the courses on [KhanAcademy](https://www.khanacademy.org), and asked the model to genrate a textbook for each. - `automathtext`: to improve the science knowledge of the model, we use samples from [AutoMathText](https://huggingface.co/datasets/math-ai/AutoMathText/) dataset as seed samples. The dataset covers more than just math. See this clustering [plot](https://huggingface.co/datasets/HuggingFaceTB/miscellaneous/blob/main/AMT_plots/topics_distpng.png) we made. ### Dataset features The dataset has the following features: - prompt: the prompt we used to generate the content with Mixtral-8x7B-Instruct-v0.1. - text: the synthetic generated content. - seed_data: the prompts include some text fromanother dataset/an external source, `seed_data` is the name of that dataset (e.g web, Stanford courses...) - token_length: the number of tokens in `text`, computed using [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1)'s tokenizer - format: the style of `text`, this can for example be a textbook, a blogpost, a story.. It can also be inferred from the prompt. - audience: the target audience defined in the prompt # Dataset creation The "Dataset splits" section already provides an overview of the data creation pipeline. In this section, we will explain the topic clustering method for web samples and our iterative process for refining the prompts, in addition to decontamination. ### Topic clustering Our goal was to generate a vast quantity of synthetic data covering a wide range of topics (essentially, anything useful found on the web) in a cleaner format like textbooks. A natural strategy was to begin with web samples, using them as seeds for the generation. This approach, employed by Li et al. in [Phi-1.5](https://huggingface.co/papers/2309.05463), appears to be the most scalable method for synthetic data generation, given the availability of web datasets with trillions of tokens. The prompted model will use an extract from these seed samples as a reference for generation, so the topic might matter more than the actual content of the file. To filter out less relevant topics and to provide the model with context for generating content, we first clustered millions of files from a web dataset. Then we prompted Mixtral 8x7B with extracts from 10 random samples in each cluster and asked it to find the topic they have in common and to provide an educational score for that topic. The dataset with clusters and topics is available in this [demo](https://huggingface.co/spaces/HuggingFaceTB/inspect_web_clusters), the code is available in [text-clustering]( https://github.com/huggingface/text-clustering ) and a [demo](https://huggingface.co/spaces/HuggingFaceTB/inspect_web_clusters) for inspection. The educational score seems to work for "very uneducational" topics like adult content and "highly educational" topics like College Mathematics, but isn't very relevant in-between. So we manually inspect the 145 clusters we find, and discard 35 of them. The final list of topics is available [here](https://github.com/huggingface/cosmopedia/blob/dd5cd1f7fcfae255c9cfbe704ba2187965523457/prompts/web_samples/filter_and_classify_clusters.py#L8). We don't do any further filtering inside the clusters but we include the topic of the sample in the prompt 100% of the time for `web_samples_v1`, but only 50% of the time in `web_samples_v2`, where we tried to refine the prompts, in case the topic isn't accurate or the topic list isn't comprehensive. Below are the clusters found in Cosmopedia: <center> <img src="https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/jMKGaE_UnEfH3j8iZYXVN.png" alt="Cosmopedia clusters" width="1200" height="750"> <p><em>Cosmopedia clusters.</em></p> </center> ### Diversity We find that when using the same seed sample multiple times, changing the generation style and/or the audience and their target format results in different generations, covering the same topic from different angles. For example when asking the model for a children's textbook, we needed to remind it that it can't use complex concepts and that the tone should be adapted to children. The same goes when asking for textbooks for college students vs for researchers, we had to emphasize the level of depth we wanted for each, and how acadmeic the textbooks should be. By carefully iterating on the prompts using [HuggingChat](https://huggingface.co/chat/) and then generating few hundreds samples, we managed to reduce the redundancy. For example, we noticed that the model always started the stories with "Once upon a time" and the forums posts with "A few years back", asking it to explicitly avoid these sentences when starting the generation results in more diverse beginnings (don't worry "Once upon a time" still appears in stories!). Same goes for blogposts and textbooks where the introductory sentences were initially repetitive. Running MinHash deduplication on the splits detects less than 1% of the files as duplicates. ### Decontamination Given how we generate synthetic content, there is a possibility that the seed samples or the model's training data could have benchmarks contamination. Therefore, we run a decontamination piepline to make sure we don't have any samples from the test benchmarks in our dataset. We use a 10-gram overlap to retrieve potentially contaminated samples, similarly to [Phi-1](https://huggingface.co/papers/2306.11644). After retrieving the candidates, we run a diff between the dataset sample and the benchmark sample using `difflib.SequenceMatcher` and discard the sample if `len(matched_substrings)/len(benchmark_sample) > 0.5`. We run decontamination against all the benchmarks we evaluated the Cosmo-1B model on: MMLU, HellaSwag, PIQA, SIQA, Winogrande, OpenBookQA, ARC-easy, ARC-challenge. We report the number of contaminated samples removed from each dataset split, as well as the number of unique benchmark samples that they correspond to (in brackets): | Dataset group | ARC Easy | ARC Challenge | BoolQ | HellaSwag | MMLU | OpenBookQA | PIQA | WinoGrande | |-----------------------------------------------|----------|---------------|----------------|-----------|------|------------|------|------------| | web_samples_v1 + web_samples_v2 + stanford + openstax | 30 (13) | 19 (3) | 386 (41) | 6 (5) | 1 (1) | 0 (0) | 5 (3) | 0 (0) | | auto_math_text + khanacademy | 4 (4) | 13 (2) | 34 (7) | 1 (1) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | | stories | 33 (20) | 20 (12) | 27 (21) | 3 (3) | 1 (1) | 2 (2) | 6 (4) | 3 (2) | ## Code The code for topic clustering of the web samples, building the prompts, content generation and data deduplication & decontamination can be found in the [Cosmopedia GitHub repository](https://github.com/huggingface/cosmopedia). ## Citation ``` @software{benallal2024cosmopedia, author = {Ben Allal, Loubna and Lozhkov, Anton and Penedo, Guilherme and Wolf, Thomas and von Werra, Leandro}, title = {Cosmopedia}, month = February, year = 2024, url = {https://huggingface.co/datasets/HuggingFaceTB/cosmopedia} } ```

facebook/multilingual_librispeech

facebook

2024-08-12T16:50:57Z

6,300

131

[ "task_categories:automatic-speech-recognition", "task_categories:text-to-speech", "task_categories:text-to-audio", "annotations_creators:expert-generated", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:multilingual", "source_datasets:original", "language:de", "language:nl", "language:fr", "language:it", "language:es", "language:pt", "language:pl", "language:en", "license:cc-by-4.0", "size_categories:1M<n<10M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2012.03411", "region:us" ]

[ "automatic-speech-recognition", "text-to-speech", "text-to-audio" ]

2022-03-02T23:29:22Z

null

--- annotations_creators: - expert-generated language_creators: - crowdsourced - expert-generated language: - de - nl - fr - it - es - pt - pl - en license: - cc-by-4.0 multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - automatic-speech-recognition - text-to-speech - text-to-audio paperswithcode_id: multilingual-librispeech pretty_name: MultiLingual LibriSpeech dataset_info: - config_name: dutch features: - name: audio dtype: audio - name: original_path dtype: string - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: transcript dtype: string - name: audio_duration dtype: float64 - name: speaker_id dtype: string - name: chapter_id dtype: string - name: file dtype: string - name: id dtype: string splits: - name: dev num_bytes: 199959986 num_examples: 3095 - name: test num_bytes: 199298575 num_examples: 3075 - name: train num_bytes: 23931679031 num_examples: 374287 - name: 9_hours num_bytes: 139884664.668 num_examples: 2153 - name: 1_hours num_bytes: 15462181 num_examples: 234 download_size: 24376256629 dataset_size: 24486284437.668 - config_name: french features: - name: audio dtype: audio - name: original_path dtype: string - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: transcript dtype: string - name: audio_duration dtype: float64 - name: speaker_id dtype: string - name: chapter_id dtype: string - name: file dtype: string - name: id dtype: string splits: - name: dev num_bytes: 157923970.696 num_examples: 2416 - name: test num_bytes: 158352158.582 num_examples: 2426 - name: train num_bytes: 16984935842.04 num_examples: 258213 - name: 9_hours num_bytes: 142796680.609 num_examples: 2167 - name: 1_hours num_bytes: 15675831 num_examples: 241 download_size: 17381581776 dataset_size: 17459684482.927002 - config_name: german features: - name: audio dtype: audio - name: original_path dtype: string - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: transcript dtype: string - name: audio_duration dtype: float64 - name: speaker_id dtype: string - name: chapter_id dtype: string - name: file dtype: string - name: id dtype: string splits: - name: dev num_bytes: 224293581.302 num_examples: 3469 - name: test num_bytes: 225756069.096 num_examples: 3394 - name: train num_bytes: 31050881388 num_examples: 469942 - name: 9_hours num_bytes: 142777983.118 num_examples: 2194 - name: 1_hours num_bytes: 15714704 num_examples: 241 download_size: 31526161821 dataset_size: 31659423725.516 - config_name: italian features: - name: audio dtype: audio - name: original_path dtype: string - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: transcript dtype: string - name: audio_duration dtype: float64 - name: speaker_id dtype: string - name: chapter_id dtype: string - name: file dtype: string - name: id dtype: string splits: - name: dev num_bytes: 81607596.048 num_examples: 1248 - name: test num_bytes: 83216752.046 num_examples: 1262 - name: train num_bytes: 3896742625 num_examples: 59623 - name: 9_hours num_bytes: 141671904.428 num_examples: 2173 - name: 1_hours num_bytes: 15560398 num_examples: 240 download_size: 4200633596 dataset_size: 4218799275.522 - config_name: polish features: - name: audio dtype: audio - name: original_path dtype: string - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: transcript dtype: string - name: audio_duration dtype: float64 - name: speaker_id dtype: string - name: chapter_id dtype: string - name: file dtype: string - name: id dtype: string splits: - name: dev num_bytes: 32746725 num_examples: 512 - name: test num_bytes: 33735044 num_examples: 520 - name: train num_bytes: 1638889846 num_examples: 25043 - name: 9_hours num_bytes: 142005461 num_examples: 2173 - name: 1_hours num_bytes: 15681216 num_examples: 238 download_size: 1855342312 dataset_size: 1863058292 - config_name: portuguese features: - name: audio dtype: audio - name: original_path dtype: string - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: transcript dtype: string - name: audio_duration dtype: float64 - name: speaker_id dtype: string - name: chapter_id dtype: string - name: file dtype: string - name: id dtype: string splits: - name: dev num_bytes: 57533473 num_examples: 826 - name: test num_bytes: 59141979 num_examples: 871 - name: train num_bytes: 2518553713.946 num_examples: 37533 - name: 9_hours num_bytes: 141641902.42 num_examples: 2116 - name: 1_hours num_bytes: 15697139 num_examples: 236 download_size: 2780836500 dataset_size: 2792568207.366 - config_name: spanish features: - name: audio dtype: audio - name: original_path dtype: string - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: transcript dtype: string - name: audio_duration dtype: float64 - name: speaker_id dtype: string - name: chapter_id dtype: string - name: file dtype: string - name: id dtype: string splits: - name: dev num_bytes: 157804903.144 num_examples: 2408 - name: test num_bytes: 158526899.32 num_examples: 2385 - name: train num_bytes: 14562584188 num_examples: 220701 - name: 9_hours num_bytes: 142473624.48 num_examples: 2110 - name: 1_hours num_bytes: 15702048 num_examples: 233 download_size: 14971394533 dataset_size: 15037091662.944 configs: - config_name: dutch data_files: - split: dev path: dutch/dev-* - split: test path: dutch/test-* - split: train path: dutch/train-* - split: 9_hours path: dutch/9_hours-* - split: 1_hours path: dutch/1_hours-* - config_name: french data_files: - split: dev path: french/dev-* - split: test path: french/test-* - split: train path: french/train-* - split: 9_hours path: french/9_hours-* - split: 1_hours path: french/1_hours-* - config_name: german data_files: - split: dev path: german/dev-* - split: test path: german/test-* - split: train path: german/train-* - split: 9_hours path: german/9_hours-* - split: 1_hours path: german/1_hours-* - config_name: italian data_files: - split: dev path: italian/dev-* - split: test path: italian/test-* - split: train path: italian/train-* - split: 9_hours path: italian/9_hours-* - split: 1_hours path: italian/1_hours-* - config_name: polish data_files: - split: dev path: polish/dev-* - split: test path: polish/test-* - split: train path: polish/train-* - split: 9_hours path: polish/9_hours-* - split: 1_hours path: polish/1_hours-* - config_name: portuguese data_files: - split: dev path: portuguese/dev-* - split: test path: portuguese/test-* - split: train path: portuguese/train-* - split: 9_hours path: portuguese/9_hours-* - split: 1_hours path: portuguese/1_hours-* - config_name: spanish data_files: - split: dev path: spanish/dev-* - split: test path: spanish/test-* - split: train path: spanish/train-* - split: 9_hours path: spanish/9_hours-* - split: 1_hours path: spanish/1_hours-* --- # Dataset Card for MultiLingual LibriSpeech ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [MultiLingual LibriSpeech ASR corpus](http://www.openslr.org/94) - **Repository:** [Needs More Information] - **Paper:** [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://arxiv.org/abs/2012.03411) - **Leaderboard:** [🤗 Autoevaluate Leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=facebook%2Fmultilingual_librispeech&only_verified=0&task=automatic-speech-recognition&config=-unspecified-&split=-unspecified-&metric=wer) ### Dataset Summary This is a streamable version of the Multilingual LibriSpeech (MLS) dataset. The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94) to make it easier to stream. MLS dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish. It includes about 44.5K hours of English and a total of about 6K hours for other languages. ### Supported Tasks and Leaderboards - `automatic-speech-recognition`, `speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://paperswithcode.com/dataset/multilingual-librispeech and ranks models based on their WER. - `text-to-speech`, `text-to-audio`: The dataset can also be used to train a model for Text-To-Speech (TTS). ### Languages The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish ### How to use The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. For example, to download the German config, simply specify the corresponding language config name (i.e., "german" for German): ```python from datasets import load_dataset mls = load_dataset("facebook/multilingual_librispeech", "german", split="train") ``` Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk. ```python from datasets import load_dataset mls = load_dataset("facebook/multilingual_librispeech", "german", split="train", streaming=True) print(next(iter(mls))) ``` *Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed). Local: ```python from datasets import load_dataset from torch.utils.data.sampler import BatchSampler, RandomSampler mls = load_dataset("facebook/multilingual_librispeech", "german", split="train") batch_sampler = BatchSampler(RandomSampler(mls), batch_size=32, drop_last=False) dataloader = DataLoader(mls, batch_sampler=batch_sampler) ``` Streaming: ```python from datasets import load_dataset from torch.utils.data import DataLoader mls = load_dataset("facebook/multilingual_librispeech", "german", split="train", streaming=True) dataloader = DataLoader(mls, batch_size=32) ``` To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets). ### Example scripts Train your own CTC or Seq2Seq Automatic Speech Recognition models on MultiLingual Librispeech with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition). ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided. ``` {'file': '10900_6473_000030.flac', 'audio': {'path': '10900_6473_000030.flac', 'array': array([-1.52587891e-04, 6.10351562e-05, 0.00000000e+00, ..., 4.27246094e-04, 5.49316406e-04, 4.57763672e-04]), 'sampling_rate': 16000}, 'text': 'więc czego chcecie odemnie spytałem wysłuchawszy tego zadziwiającego opowiadania broń nas stary człowieku broń zakrzyknęli równocześnie obaj posłowie\n', 'speaker_id': 10900, 'chapter_id': 6473, 'id': '10900_6473_000030'} ``` ### Data Fields - file: A filename .flac format. - audio: A dictionary containing the audio filename, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. - text: the transcription of the audio file. - id: unique id of the data sample. - speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples. - chapter_id: id of the audiobook chapter which includes the transcription. ### Data Splits | Number of samples | Train | Train.9h | Train.1h | Dev | Test | | ----- | ------ | ----- | ---- | ---- | ---- | | german | 469942 | 2194 | 241 | 3469 | 3394 | | dutch | 374287 | 2153 | 234 | 3095 | 3075 | | french | 258213 | 2167 | 241 | 2416 | 2426 | | spanish | 220701 | 2110 | 233 | 2408 | 2385 | | italian | 59623 | 2173 | 240 | 1248 | 1262 | | portuguese | 37533 | 2116 | 236 | 826 | 871 | | polish | 25043 | 2173 | 238 | 512 | 520 | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information Public Domain, Creative Commons Attribution 4.0 International Public License ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode)) ### Citation Information ``` @article{Pratap2020MLSAL, title={MLS: A Large-Scale Multilingual Dataset for Speech Research}, author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert}, journal={ArXiv}, year={2020}, volume={abs/2012.03411} } ``` ### Data Statistics | Duration (h) | Train | Dev | Test | |--------------|-----------|-------|-------| | English | 44,659.74 | 15.75 | 15.55 | | German | 1,966.51 | 14.28 | 14.29 | | Dutch | 1,554.24 | 12.76 | 12.76 | | French | 1,076.58 | 10.07 | 10.07 | | Spanish | 917.68 | 9.99 | 10 | | Italian | 247.38 | 5.18 | 5.27 | | Portuguese | 160.96 | 3.64 | 3.74 | | Polish | 103.65 | 2.08 | 2.14 | | # Speakers | Train | | Dev | | Test | | |------------|-------|------|-----|----|------|----| | Gender | M | F | M | F | M | F | | English | 2742 | 2748 | 21 | 21 | 21 | 21 | | German | 81 | 95 | 15 | 15 | 15 | 15 | | Dutch | 9 | 31 | 3 | 3 | 3 | 3 | | French | 62 | 80 | 9 | 9 | 9 | 9 | | Spanish | 36 | 50 | 10 | 10 | 10 | 10 | | Italian | 22 | 43 | 5 | 5 | 5 | 5 | | Portuguese | 26 | 16 | 5 | 5 | 5 | 5 | | Polish | 6 | 5 | 2 | 2 | 2 | 2 | | # Hours / Gender | Dev | | Test | | |------------------|------|------|------|------| | Gender | M | F | M | F | | English | 7.76 | 7.99 | 7.62 | 7.93 | | German | 7.06 | 7.22 | 7 | 7.29 | | Dutch | 6.44 | 6.32 | 6.72 | 6.04 | | French | 5.13 | 4.94 | 5.04 | 5.02 | | Spanish | 4.91 | 5.08 | 4.78 | 5.23 | | Italian | 2.5 | 2.68 | 2.38 | 2.9 | | Portuguese | 1.84 | 1.81 | 1.83 | 1.9 | | Polish | 1.12 | 0.95 | 1.09 | 1.05 | ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) and [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.

turkish-nlp-suite/InstrucTurca

turkish-nlp-suite

2024-08-12T10:13:04Z

254

25

[ "task_categories:text-generation", "task_categories:text2text-generation", "task_categories:question-answering", "language:tr", "license:cc-by-sa-4.0", "size_categories:1M<n<10M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[ "text-generation", "text2text-generation", "question-answering", "conversational" ]

2024-07-28T10:53:18Z

2

--- name: InstrucTurca tagline: Diverse Instructional Content Dataset for Turkish description: > InstrucTurca is a rich Turkish instruction tuning dataset from various fields. It includes tasks, code, poems, math, essays, medical texts, and more. This diversity makes it ideal for various NLP tasks such as summarization, question answering, generation, translation, and classification. However the main usage is intended to instruction train Turkish LLMs. dataset_version: 1.0.0 authors: - name: Duygu Altinok email: [email protected] license: cc-by-sa-4.0 task_categories: - text-generation - text2text-generation - question-answering - conversational language: - tr pretty_name: InstructTurca size_categories: - 1M<n<10M --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/635c4736177df3f16e99db63/ygAnIjc7zWWp72_q0t985.png) InstrucTurca v1.0.0 is a diverse synthetic instruction tuning dataset crafted for instruction-tuning Turkish LLMs. The data is compiled data various English datasets and sources, such as code instructions, poems, summarized texts, medical texts, and more. ## Dataset content * BI55/MedText * checkai/instruction-poems * garage-bAInd/Open-Platypus * Locutusque/ColumnedChatCombined * nampdn-ai/tiny-codes * Open-Orca/OpenOrca * pubmed_qa * TIGER-Lab/MathInstruct * TokenBender/code_instructions_120k_alpaca_style Each dataset instance is a pair of text representing user input and bot output. ## Dataset creation All the data is translated by Snowflake Artic Instruct. We used a very basic prompt and asked Arctic to translate both the input and output text. After that, we made a pass over the dataset to eliminate hallucination looking translations. More info about the creation process can be found at our [Medium article](https://medium.com/google-developer-experts/instructurca-an-open-source-instruction-tuning-dataset-for-turkish-18c37b0e99b9). ## Licence The dataset is suitable for commercial use, thanks to [Snowflake Arctic's Apache 2.0 licence](https://github.com/Snowflake-Labs/snowflake-arctic/blob/main/LICENSE). ## Acknowledgements: Huge thanks go to original dataset creators. Even a bigger thanks go to Snowflake team to creating a truly open source LLM! Without any of them, this dataset wouldn't be possible. ## Citation If you find this work helpful, feel free to give us a cite. ``` @misc{instructurca, author={Duygu Altinok}, title={InstrucTurca: A Diverse Instructional Content Dataset for Turkish}, year={2024} } ```

deepset/covid_qa_deepset

deepset

2024-08-08T06:11:28Z

294

8

[ "task_categories:question-answering", "task_ids:closed-domain-qa", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[ "question-answering" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - expert-generated language_creators: - found language: - en license: - apache-2.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - closed-domain-qa - extractive-qa pretty_name: COVID-QA dataset_info: config_name: covid_qa_deepset features: - name: document_id dtype: int32 - name: context dtype: string - name: question dtype: string - name: is_impossible dtype: bool - name: id dtype: int32 - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: train num_bytes: 65151242 num_examples: 2019 download_size: 2274275 dataset_size: 65151242 configs: - config_name: covid_qa_deepset data_files: - split: train path: covid_qa_deepset/train-* default: true --- # Dataset Card for COVID-QA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** https://github.com/deepset-ai/COVID-QA - **Paper:** https://openreview.net/forum?id=JENSKEEzsoU - **Point of Contact:** [deepset AI](https://github.com/deepset-ai) ### Dataset Summary COVID-QA is a Question Answering dataset consisting of 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles related to COVID-19. A total of 147 scientific articles from the CORD-19 dataset were annotated by 15 experts. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances **What do the instances that comprise the dataset represent?** Each represents a question, a context (document passage from the CORD19 dataset) and an answer. **How many instances are there in total?** 2019 instances **What data does each instance consist of?** Each instance is a question, a set of answers, and an id associated with each answer. [More Information Needed] ### Data Fields The data was annotated in SQuAD style fashion, where each row contains: * **question**: Query question * **context**: Context text to obtain the answer from * **document_id** The document ID of the context text * **answer**: Dictionary containing the answer string and the start index ### Data Splits **data/COVID-QA.json**: 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles related to COVID-19. [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The inital data collected comes from 147 scientific articles from the CORD-19 dataset. Question and answers were then annotated afterwards. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process While annotators were volunteers, they were required to have at least a Master’s degree in biomedical sciences. The annotation team was led by a medical doctor (G.A.R.) who vetted the volunteer’s credentials and manually verified each question/answer pair produced. We used an existing, web-based annotation tool that had been created by deepset and is available at their Neural Search framework [haystack](https://github.com/deepset-ai/haystack). #### Who are the annotators? The annotators are 15 volunteer biomedical experts on scientific articles related to COVID-19. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset The dataset aims to help build question answering models serving clinical and scientific researchers, public health authorities, and frontline workers. These QA systems can help them find answers and patterns in research papers by locating relevant answers to common questions from scientific articles. ### Discussion of Biases [More Information Needed] ### Other Known Limitations ## Additional Information The listed authors in the homepage are maintaining/supporting the dataset. ### Dataset Curators [More Information Needed] ### Licensing Information The Proto_qa dataset is licensed under the [Apache License 2.0](https://github.com/deepset-ai/COVID-QA/blob/master/LICENSE) ### Citation Information ``` @inproceedings{moller2020covid, title={COVID-QA: A Question Answering Dataset for COVID-19}, author={M{\"o}ller, Timo and Reina, Anthony and Jayakumar, Raghavan and Pietsch, Malte}, booktitle={Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020}, year={2020} } ``` ### Contributions Thanks to [@olinguyen](https://github.com/olinguyen) for adding this dataset.

sonos-nlu-benchmark/snips_built_in_intents

sonos-nlu-benchmark

2024-08-08T06:03:47Z

515

11

[ "task_categories:text-classification", "task_ids:intent-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc0-1.0", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1805.10190", "region:us" ]

[ "text-classification" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - cc0-1.0 multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - text-classification task_ids: - intent-classification paperswithcode_id: snips pretty_name: SNIPS Natural Language Understanding benchmark dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': ComparePlaces '1': RequestRide '2': GetWeather '3': SearchPlace '4': GetPlaceDetails '5': ShareCurrentLocation '6': GetTrafficInformation '7': BookRestaurant '8': GetDirections '9': ShareETA splits: - name: train num_bytes: 19427 num_examples: 328 download_size: 11158 dataset_size: 19427 configs: - config_name: default data_files: - split: train path: data/train-* train-eval-index: - config: default task: text-classification task_id: multi_class_classification train_split: train col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for Snips Built In Intents ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/sonos/nlu-benchmark/tree/master/2016-12-built-in-intents - **Repository:** https://github.com/sonos/nlu-benchmark/tree/master/2016-12-built-in-intents - **Paper:** https://arxiv.org/abs/1805.10190 - **Point of Contact:** The Snips team has joined Sonos in November 2019. These open datasets remain available and their access is now managed by the Sonos Voice Experience Team. Please email [email protected] with any question. ### Dataset Summary Snips' built in intents dataset was initially used to compare different voice assistants and released as a public dataset hosted at https://github.com/sonos/nlu-benchmark in folder 2016-12-built-in-intents. The dataset contains 328 utterances over 10 intent classes. A related Medium post is https://medium.com/snips-ai/benchmarking-natural-language-understanding-systems-d35be6ce568d. ### Supported Tasks and Leaderboards There are no related shared tasks that we are aware of. ### Languages English ## Dataset Structure ### Data Instances The dataset contains 328 utterances over 10 intent classes. Each sample looks like: `{'label': 8, 'text': 'Transit directions to Barcelona Pizza.'}` ### Data Fields - `text`: The text utterance expressing some user intent. - `label`: The intent label of the piece of text utterance. ### Data Splits The source data is not split. ## Dataset Creation ### Curation Rationale The dataset was originally created to compare the performance of a number of voice assistants. However, the labelled utterances are useful for developing and benchmarking text chatbots as well. ### Source Data #### Initial Data Collection and Normalization It is not clear how the data was collected. From the Medium post: `The benchmark relies on a set of 328 queries built by the business team at Snips, and kept secret from data scientists and engineers throughout the development of the solution.` #### Who are the source language producers? Originally prepared by snips.ai. The Snips team has since joined Sonos in November 2019. These open datasets remain available and their access is now managed by the Sonos Voice Experience Team. Please email [email protected] with any question. ### Annotations #### Annotation process It is not clear how the data was collected. From the Medium post: `The benchmark relies on a set of 328 queries built by the business team at Snips, and kept secret from data scientists and engineers throughout the development of the solution.` #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Originally prepared by snips.ai. The Snips team has since joined Sonos in November 2019. These open datasets remain available and their access is now managed by the Sonos Voice Experience Team. Please email [email protected] with any question. ### Licensing Information The source data is licensed under Creative Commons Zero v1.0 Universal. ### Citation Information Any publication based on these datasets must include a full citation to the following paper in which the results were published by the Snips Team: Coucke A. et al., "Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces." CoRR 2018, https://arxiv.org/abs/1805.10190 ### Contributions Thanks to [@bduvenhage](https://github.com/bduvenhage) for adding this dataset.

KorQuAD/squad_kor_v1

KorQuAD

2024-08-08T06:03:11Z

1,166

28

[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:ko", "license:cc-by-nd-4.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1909.07005", "region:us" ]

[ "question-answering" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - crowdsourced language_creators: - found language: - ko license: - cc-by-nd-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: korquad pretty_name: The Korean Question Answering Dataset dataset_info: config_name: squad_kor_v1 features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: train num_bytes: 83380141 num_examples: 60407 - name: validation num_bytes: 8261701 num_examples: 5774 download_size: 12713412 dataset_size: 91641842 configs: - config_name: squad_kor_v1 data_files: - split: train path: squad_kor_v1/train-* - split: validation path: squad_kor_v1/validation-* default: true --- # Dataset Card for KorQuAD v1.0 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://korquad.github.io/KorQuad%201.0/ - **Repository:** https://github.com/korquad/korquad.github.io/tree/master/dataset - **Paper:** https://arxiv.org/abs/1909.07005 ### Dataset Summary KorQuAD 1.0 is a large-scale question-and-answer dataset constructed for Korean machine reading comprehension, and investigate the dataset to understand the distribution of answers and the types of reasoning required to answer the question. This dataset benchmarks the data generating process of SQuAD v1.0 to meet the standard. ### Supported Tasks and Leaderboards `question-answering` ### Languages Korean ## Dataset Structure Follows the standars SQuAD format. ### Data Instances An example from the data set looks as follows: ``` {'answers': {'answer_start': [54], 'text': ['교향곡']}, 'context': '1839년 바그너는 괴테의 파우스트을 처음 읽고 그 내용에 마음이 끌려 이를 소재로 해서 하나의 교향곡을 쓰려는 뜻을 갖는다. 이 시기 바그너는 1838년에 빛 독촉으로 산전수전을 다 걲은 상황이라 좌절과 실망에 가득했으며 메피스토펠레스를 만나는 파우스트의 심경에 공감했다고 한다. 또한 파리에서 아브네크의 지휘로 파리 음악원 관현악단이 연주하는 베토벤의 교향곡 9번을 듣고 깊은 감명을 받았는데, 이것이 이듬해 1월에 파우스트의 서곡으로 쓰여진 이 작품에 조금이라도 영향을 끼쳤으리라는 것은 의심할 여지가 없다. 여기의 라단조 조성의 경우에도 그의 전기에 적혀 있는 것처럼 단순한 정신적 피로나 실의가 반영된 것이 아니라 베토벤의 합창교향곡 조성의 영향을 받은 것을 볼 수 있다. 그렇게 교향곡 작곡을 1839년부터 40년에 걸쳐 파리에서 착수했으나 1악장을 쓴 뒤에 중단했다. 또한 작품의 완성과 동시에 그는 이 서곡(1악장)을 파리 음악원의 연주회에서 연주할 파트보까지 준비하였으나, 실제로는 이루어지지는 않았다. 결국 초연은 4년 반이 지난 후에 드레스덴에서 연주되었고 재연도 이루어졌지만, 이후에 그대로 방치되고 말았다. 그 사이에 그는 리엔치와 방황하는 네덜란드인을 완성하고 탄호이저에도 착수하는 등 분주한 시간을 보냈는데, 그런 바쁜 생활이 이 곡을 잊게 한 것이 아닌가 하는 의견도 있다.', 'id': '6566495-0-0', 'question': '바그너는 괴테의 파우스트를 읽고 무엇을 쓰고자 했는가?', 'title': '파우스트_서곡'} ``` ### Data Fields ``` {'id': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'context': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'answers': Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None)} ``` ### Data Splits - Train: 60407 - Validation: 5774 ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data Wikipedia #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [CC BY-ND 2.0 KR](https://creativecommons.org/licenses/by-nd/2.0/kr/deed.en) ### Citation Information ``` @article{lim2019korquad1, title={Korquad1. 0: Korean qa dataset for machine reading comprehension}, author={Lim, Seungyoung and Kim, Myungji and Lee, Jooyoul}, journal={arXiv preprint arXiv:1909.07005}, year={2019} ``` ### Contributions Thanks to [@cceyda](https://github.com/cceyda) for adding this dataset.

s3prl/superb

s3prl

2024-08-08T06:02:44Z

1,280

30

[ "task_categories:automatic-speech-recognition", "task_categories:audio-classification", "task_ids:keyword-spotting", "task_ids:speaker-identification", "task_ids:audio-intent-classification", "task_ids:audio-emotion-recognition", "annotations_creators:other", "language_creators:other", "multilinguality:monolingual", "source_datasets:original", "source_datasets:extended|librispeech_asr", "source_datasets:extended|other-librimix", "source_datasets:extended|other-speech_commands", "language:en", "license:unknown", "arxiv:2105.01051", "region:us", "query-by-example-spoken-term-detection", "audio-slot-filling", "speaker-diarization", "automatic-speaker-verification" ]

[ "automatic-speech-recognition", "audio-classification" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - other language_creators: - other language: - en license: - unknown multilinguality: - monolingual size_categories: - unknown source_datasets: - original - extended|librispeech_asr - extended|other-librimix - extended|other-speech_commands task_categories: - automatic-speech-recognition - audio-classification task_ids: - keyword-spotting - speaker-identification - audio-intent-classification - audio-emotion-recognition pretty_name: SUPERB tags: - query-by-example-spoken-term-detection - audio-slot-filling - speaker-diarization - automatic-speaker-verification dataset_info: - config_name: asr features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string splits: - name: train num_bytes: 11852430 num_examples: 28539 - name: validation num_bytes: 897213 num_examples: 2703 - name: test num_bytes: 871234 num_examples: 2620 download_size: 7071899769 dataset_size: 13620877 - config_name: sd features: - name: record_id dtype: string - name: file dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: speakers list: - name: speaker_id dtype: string - name: start dtype: int64 - name: end dtype: int64 splits: - name: train num_bytes: 4622013 num_examples: 13901 - name: dev num_bytes: 860472 num_examples: 3014 - name: test num_bytes: 847803 num_examples: 3002 download_size: 7190370211 dataset_size: 6330288 - config_name: ks features: - name: file dtype: string - name: label dtype: class_label: names: '0': 'yes' '1': 'no' '2': up '3': down '4': left '5': right '6': 'on' '7': 'off' '8': stop '9': go '10': _silence_ '11': _unknown_ splits: - name: train num_bytes: 8467781 num_examples: 51094 - name: validation num_bytes: 1126476 num_examples: 6798 - name: test num_bytes: 510619 num_examples: 3081 download_size: 1560367713 dataset_size: 10104876 - config_name: ic features: - name: file dtype: string - name: speaker_id dtype: string - name: text dtype: string - name: action dtype: class_label: names: '0': activate '1': bring '2': change language '3': deactivate '4': decrease '5': increase - name: object dtype: class_label: names: '0': Chinese '1': English '2': German '3': Korean '4': heat '5': juice '6': lamp '7': lights '8': music '9': newspaper '10': none '11': shoes '12': socks '13': volume - name: location dtype: class_label: names: '0': bedroom '1': kitchen '2': none '3': washroom splits: - name: train num_bytes: 7071466 num_examples: 23132 - name: validation num_bytes: 953622 num_examples: 3118 - name: test num_bytes: 1158347 num_examples: 3793 download_size: 1544093324 dataset_size: 9183435 - config_name: si features: - name: file dtype: string - name: label dtype: class_label: names: '0': id10001 '1': id10002 '2': id10003 '3': id10004 '4': id10005 '5': id10006 '6': id10007 '7': id10008 '8': id10009 '9': id10010 '10': id10011 '11': id10012 '12': id10013 '13': id10014 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of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [http://superbbenchmark.org](http://superbbenchmark.org) - **Repository:** [https://github.com/s3prl/s3prl](https://github.com/s3prl/s3prl) - **Paper:** [SUPERB: Speech processing Universal PERformance Benchmark](https://arxiv.org/abs/2105.01051) - **Leaderboard:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [Lewis Tunstall](mailto:[email protected]) and [Albert Villanova](mailto:[email protected]) ### Dataset Summary SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data. ### Supported Tasks and Leaderboards The SUPERB leaderboard can be found here https://superbbenchmark.org/leaderboard and consists of the following tasks: #### pr Phoneme Recognition (PR) transcribes an utterance into the smallest content units. This task includes alignment modeling to avoid potentially inaccurate forced alignment. [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) train-clean-100/dev-clean/test-clean subsets are adopted in SUPERB for training/validation/testing. Phoneme transcriptions are obtained from the LibriSpeech official g2p-model-5 and the conversion script in Kaldi librispeech s5 recipe. The evaluation metric is phone error rate (PER). #### asr Automatic Speech Recognition (ASR) transcribes utterances into words. While PR analyzes the improvement in modeling phonetics, ASR reflects the significance of the improvement in a real-world scenario. [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) train-clean-100/devclean/test-clean subsets are used for training/validation/testing. The evaluation metric is word error rate (WER). #### ks Keyword Spotting (KS) detects preregistered keywords by classifying utterances into a predefined set of words. The task is usually performed on-device for the fast response time. Thus, accuracy, model size, and inference time are all crucial. SUPERB uses the widely used [Speech Commands dataset v1.0](https://www.tensorflow.org/datasets/catalog/speech_commands) for the task. The dataset consists of ten classes of keywords, a class for silence, and an unknown class to include the false positive. The evaluation metric is accuracy (ACC) ##### Example of usage: Use these auxillary functions to: - load the audio file into an audio data array - sample from long `_silence_` audio clips For other examples of handling long `_silence_` clips see the [S3PRL](https://github.com/s3prl/s3prl/blob/099ce807a6ffa6bf2482ceecfcaf83dea23da355/s3prl/downstream/speech_commands/dataset.py#L80) or [TFDS](https://github.com/tensorflow/datasets/blob/6b8cfdb7c3c0a04e731caaa8660ce948d0a67b1e/tensorflow_datasets/audio/speech_commands.py#L143) implementations. ```python def map_to_array(example): import soundfile as sf speech_array, sample_rate = sf.read(example["file"]) example["speech"] = speech_array example["sample_rate"] = sample_rate return example def sample_noise(example): # Use this function to extract random 1 sec slices of each _silence_ utterance, # e.g. inside `torch.utils.data.Dataset.__getitem__()` from random import randint if example["label"] == "_silence_": random_offset = randint(0, len(example["speech"]) - example["sample_rate"] - 1) example["speech"] = example["speech"][random_offset : random_offset + example["sample_rate"]] return example ``` #### qbe Query by Example Spoken Term Detection (QbE) detects a spoken term (query) in an audio database (documents) by binary discriminating a given pair of query and document into a match or not. The English subset in [QUESST 2014 challenge](https://github.com/s3prl/s3prl/tree/master/downstream#qbe-query-by-example-spoken-term-detection) is adopted since we focus on investigating English as the first step. The evaluation metric is maximum term weighted value (MTWV) which balances misses and false alarms. #### ic Intent Classification (IC) classifies utterances into predefined classes to determine the intent of speakers. SUPERB uses the [Fluent Speech Commands dataset](https://github.com/s3prl/s3prl/tree/master/downstream#ic-intent-classification---fluent-speech-commands), where each utterance is tagged with three intent labels: action, object, and location. The evaluation metric is accuracy (ACC). #### sf Slot Filling (SF) predicts a sequence of semantic slot-types from an utterance, like a slot-type FromLocation for a spoken word Taipei, which is known as a slot-value. Both slot-types and slot-values are essential for an SLU system to function. The evaluation metrics thus include slot-type F1 score and slotvalue CER. [Audio SNIPS](https://github.com/s3prl/s3prl/tree/master/downstream#sf-end-to-end-slot-filling) is adopted, which synthesized multi-speaker utterances for SNIPS. Following the standard split in SNIPS, US-accent speakers are further selected for training, and others are for validation/testing. #### si Speaker Identification (SI) classifies each utterance for its speaker identity as a multi-class classification, where speakers are in the same predefined set for both training and testing. The widely used [VoxCeleb1 dataset](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html) is adopted, and the evaluation metric is accuracy (ACC). #### asv Automatic Speaker Verification (ASV) verifies whether the speakers of a pair of utterances match as a binary classification, and speakers in the testing set may not appear in the training set. Thus, ASV is more challenging than SID. VoxCeleb1 is used without VoxCeleb2 training data and noise augmentation. The evaluation metric is equal error rate (EER). #### sd Speaker Diarization (SD) predicts *who is speaking when* for each timestamp, and multiple speakers can speak simultaneously. The model has to encode rich speaker characteristics for each frame and should be able to represent mixtures of signals. [LibriMix](https://github.com/s3prl/s3prl/tree/master/downstream#sd-speaker-diarization) is adopted where LibriSpeech train-clean-100/dev-clean/test-clean are used to generate mixtures for training/validation/testing. We focus on the two-speaker scenario as the first step. The time-coded speaker labels were generated using alignments from Kaldi LibriSpeech ASR model. The evaluation metric is diarization error rate (DER). ##### Example of usage Use these auxiliary functions to: - load the audio file into an audio data array - generate the label array ```python def load_audio_file(example, frame_shift=160): import soundfile as sf example["array"], example["sample_rate"] = sf.read( example["file"], start=example["start"] * frame_shift, stop=example["end"] * frame_shift ) return example def generate_label(example, frame_shift=160, num_speakers=2, rate=16000): import numpy as np start = example["start"] end = example["end"] frame_num = end - start speakers = sorted({speaker["speaker_id"] for speaker in example["speakers"]}) label = np.zeros((frame_num, num_speakers), dtype=np.int32) for speaker in example["speakers"]: speaker_index = speakers.index(speaker["speaker_id"]) start_frame = np.rint(speaker["start"] * rate / frame_shift).astype(int) end_frame = np.rint(speaker["end"] * rate / frame_shift).astype(int) rel_start = rel_end = None if start <= start_frame < end: rel_start = start_frame - start if start < end_frame <= end: rel_end = end_frame - start if rel_start is not None or rel_end is not None: label[rel_start:rel_end, speaker_index] = 1 example["label"] = label return example ``` #### er Emotion Recognition (ER) predicts an emotion class for each utterance. The most widely used ER dataset [IEMOCAP](https://github.com/s3prl/s3prl/tree/master/downstream#er-emotion-recognition) is adopted, and we follow the conventional evaluation protocol: we drop the unbalance emotion classes to leave the final four classes with a similar amount of data points and cross-validates on five folds of the standard splits. The evaluation metric is accuracy (ACC). ### Languages The language data in SUPERB is in English (BCP-47 `en`) ## Dataset Structure ### Data Instances #### pr [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### asr An example from each split looks like: ```python {'chapter_id': 1240, 'file': 'path/to/file.flac', 'audio': {'path': 'path/to/file.flac', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'id': '103-1240-0000', 'speaker_id': 103, 'text': 'CHAPTER ONE MISSUS RACHEL LYNDE IS SURPRISED MISSUS RACHEL LYNDE ' 'LIVED JUST WHERE THE AVONLEA MAIN ROAD DIPPED DOWN INTO A LITTLE ' 'HOLLOW FRINGED WITH ALDERS AND LADIES EARDROPS AND TRAVERSED BY A ' 'BROOK'} ``` #### ks An example from each split looks like: ```python { 'file': '/path/yes/af7a8296_nohash_1.wav', 'audio': {'path': '/path/yes/af7a8296_nohash_1.wav', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'label': 0 # 'yes' } ``` #### qbe [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### ic ```python { 'file': "/path/wavs/speakers/2BqVo8kVB2Skwgyb/063aa8f0-4479-11e9-a9a5-5dbec3b8816a.wav", 'audio': {'path': '/path/wavs/speakers/2BqVo8kVB2Skwgyb/063aa8f0-4479-11e9-a9a5-5dbec3b8816a.wav', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'speaker_id': '2BqVo8kVB2Skwgyb', 'text': 'Turn the bedroom lights off', 'action': 3, # 'deactivate' 'object': 7, # 'lights' 'location': 0 # 'bedroom' } ``` #### sf [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### si ```python { 'file': '/path/wav/id10003/na8-QEFmj44/00003.wav', 'audio': {'path': '/path/wav/id10003/na8-QEFmj44/00003.wav', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'label': 2 # 'id10003' } ``` #### asv [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### sd An example from each split looks like: ```python { 'record_id': '1578-6379-0038_6415-111615-0009', 'file': 'path/to/file.wav', 'audio': {'path': 'path/to/file.wav', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'start': 0, 'end': 1590, 'speakers': [ {'speaker_id': '1578', 'start': 28, 'end': 657}, {'speaker_id': '6415', 'start': 28, 'end': 1576} ] } ``` #### er [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Data Fields ####Note abouth the `audio` fields When accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. #### pr [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### asr - `file` (`string`): Path to the WAV audio file. - `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. - `text` (`string`): The transcription of the audio file. - `speaker_id` (`integer`): A unique ID of the speaker. The same speaker id can be found for multiple data samples. - `chapter_id` (`integer`): ID of the audiobook chapter which includes the transcription. - `id` (`string`): A unique ID of the data sample. #### ks - `file` (`string`): Path to the WAV audio file. - `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. - `label` (`ClassLabel`): Label of the spoken command. Possible values: - `0: "yes", 1: "no", 2: "up", 3: "down", 4: "left", 5: "right", 6: "on", 7: "off", 8: "stop", 9: "go", 10: "_silence_", 11: "_unknown_"` #### qbe [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### ic - `file` (`string`): Path to the WAV audio file. - `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. - `speaker_id` (`string`): ID of the speaker. - `text` (`string`): Transcription of the spoken command. - `action` (`ClassLabel`): Label of the command's action. Possible values: - `0: "activate", 1: "bring", 2: "change language", 3: "deactivate", 4: "decrease", 5: "increase"` - `object` (`ClassLabel`): Label of the command's object. Possible values: - `0: "Chinese", 1: "English", 2: "German", 3: "Korean", 4: "heat", 5: "juice", 6: "lamp", 7: "lights", 8: "music", 9: "newspaper", 10: "none", 11: "shoes", 12: "socks", 13: "volume"` - `location` (`ClassLabel`): Label of the command's location. Possible values: - `0: "bedroom", 1: "kitchen", 2: "none", 3: "washroom"` #### sf [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### si - `file` (`string`): Path to the WAV audio file. - `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. - `label` (`ClassLabel`): Label (ID) of the speaker. Possible values: - `0: "id10001", 1: "id10002", 2: "id10003", ..., 1250: "id11251"` #### asv [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### sd The data fields in all splits are: - `record_id` (`string`): ID of the record. - `file` (`string`): Path to the WAV audio file. - `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. - `start` (`integer`): Start frame of the audio. - `end` (`integer`): End frame of the audio. - `speakers` (`list` of `dict`): List of speakers in the audio. Each item contains the fields: - `speaker_id` (`string`): ID of the speaker. - `start` (`integer`): Frame when the speaker starts speaking. - `end` (`integer`): Frame when the speaker stops speaking. #### er - `file` (`string`): Path to the WAV audio file. - `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. - `label` (`ClassLabel`): Label of the speech emotion. Possible values: - `0: "neu", 1: "hap", 2: "ang", 3: "sad"` ### Data Splits #### pr [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### asr | | train | validation | test | |-----|------:|-----------:|-----:| | asr | 28539 | 2703 | 2620 | #### ks | | train | validation | test | |----|------:|-----------:|-----:| | ks | 51094 | 6798 | 3081 | #### qbe [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### ic | | train | validation | test | |----|------:|-----------:|-----:| | ic | 23132 | 3118 | 3793 | #### sf [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### si | | train | validation | test | |----|-------:|-----------:|-----:| | si | 138361 | 6904 | 8251 | #### asv [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### sd The data is split into "train", "dev" and "test" sets, each containing the following number of examples: | | train | dev | test | |----|------:|-----:|-----:| | sd | 13901 | 3014 | 3002 | #### er The data is split into 5 sets intended for 5-fold cross-validation: | | session1 | session2 | session3 | session4 | session5 | |----|---------:|---------:|---------:|---------:|---------:| | er | 1085 | 1023 | 1151 | 1031 | 1241 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information #### pr and asr The license for Librispeech is the Creative Commons Attribution 4.0 International license ((CC-BY-4.0)[https://creativecommons.org/licenses/by/4.0/]). #### ks The license for Speech Commands is [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode) #### qbe The license for QUESST 2014 is not known. #### ic The license for Fluent Speech Commands dataset is the [Fluent Speech Commands Public License](https://fluent.ai/wp-content/uploads/2021/04/Fluent_Speech_Commands_Public_License.pdf) #### sf The license for Audio SNIPS dataset is not known. #### si and asv The license for VoxCeleb1 dataset is the Creative Commons Attribution 4.0 International license ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)). #### sd LibriMix is based on the LibriSpeech (see above) and Wham! noises datasets. The Wham! noises dataset is distributed under the Attribution-NonCommercial 4.0 International ([CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)) license. #### er The IEMOCAP license is ditributed under [its own license](https://sail.usc.edu/iemocap/Data_Release_Form_IEMOCAP.pdf). ### Citation Information ``` @article{DBLP:journals/corr/abs-2105-01051, author = {Shu{-}Wen Yang and Po{-}Han Chi and Yung{-}Sung Chuang and Cheng{-}I Jeff Lai and Kushal Lakhotia and Yist Y. Lin and Andy T. Liu and Jiatong Shi and Xuankai Chang and Guan{-}Ting Lin and Tzu{-}Hsien Huang and Wei{-}Cheng Tseng and Ko{-}tik Lee and Da{-}Rong Liu and Zili Huang and Shuyan Dong and Shang{-}Wen Li and Shinji Watanabe and Abdelrahman Mohamed and Hung{-}yi Lee}, title = {{SUPERB:} Speech processing Universal PERformance Benchmark}, journal = {CoRR}, volume = {abs/2105.01051}, year = {2021}, url = {https://arxiv.org/abs/2105.01051}, archivePrefix = {arXiv}, eprint = {2105.01051}, timestamp = {Thu, 01 Jul 2021 13:30:22 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2105-01051.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } Note that each SUPERB dataset has its own citation. Please see the source to see the correct citation for each contained dataset. ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova) and [@anton-l](https://github.com/anton-l) for adding this dataset.

fthbrmnby/turkish_product_reviews

fthbrmnby

2024-08-08T05:57:51Z

99

10

[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:tr", "license:unknown", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[ "text-classification" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - found language_creators: - found language: - tr license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: Turkish Product Reviews dataset_info: features: - name: sentence dtype: string - name: sentiment dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 43369614 num_examples: 235165 download_size: 24354762 dataset_size: 43369614 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for Turkish Product Reviews ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [turkish-text-data](https://github.com/fthbrmnby/turkish-text-data) - **Point of Contact:** [Fatih Barmanbay](https://github.com/fthbrmnby) ### Dataset Summary This Turkish Product Reviews Dataset contains 235.165 product reviews collected online. There are 220.284 positive, 14881 negative reviews. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset is based on Turkish. ## Dataset Structure ### Data Instances **Example 1:** **sentence:** beklentimin altında bir ürün kaliteli değil **sentiment:** 0 (negative) **Example 2:** **sentence:** fiyat ve performans olarak gayet iyi **sentiment:** 1 (positive) ### Data Fields - **sentence**(string) : Contatins turkish product review - **sentiment**(int) : 0 (negative) or 1 (positive) ### Data Splits It is not divided into Train set and Test set. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations The dataset does not contain any additional annotations. #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The dataset was created by [Fatih Barmanbay](https://github.com/fthbrmnby). ### Licensing Information The data is under the [CC-BY-SA-4.0 License](https://github.com/fthbrmnby/turkish-text-data/blob/master/LICENCE) ### Citation Information No citation available for this dataset. ### Contributions Thanks to [@basakbuluz](https://github.com/basakbuluz) for adding this dataset.

ccdv/arxiv-classification

ccdv

2024-08-08T05:50:57Z

652

23

[ "task_categories:text-classification", "task_ids:multi-class-classification", "task_ids:topic-classification", "language:en", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "long context" ]

[ "text-classification" ]

2022-03-02T23:29:22Z

1

--- language: en size_categories: 10K<n<100K task_categories: - text-classification task_ids: - multi-class-classification - topic-classification tags: - long context dataset_info: - config_name: default features: - name: text dtype: string - name: label dtype: class_label: names: '0': math.AC '1': cs.CV '2': cs.AI '3': cs.SY '4': math.GR '5': cs.CE '6': cs.PL '7': cs.IT '8': cs.DS '9': cs.NE '10': math.ST splits: - name: train num_bytes: 1650691690 num_examples: 28388 - name: validation num_bytes: 144469423 num_examples: 2500 - name: test num_bytes: 141183324 num_examples: 2500 download_size: 1011043995 dataset_size: 1936344437 - config_name: no_ref features: - name: text dtype: string - name: label dtype: class_label: names: '0': math.AC '1': cs.CV '2': cs.AI '3': cs.SY '4': math.GR '5': cs.CE '6': cs.PL '7': cs.IT '8': cs.DS '9': cs.NE '10': math.ST splits: - name: train num_bytes: 1650587077 num_examples: 28388 - name: validation num_bytes: 144460101 num_examples: 2500 - name: test num_bytes: 141174323 num_examples: 2500 download_size: 1010911333 dataset_size: 1936221501 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* - config_name: no_ref data_files: - split: train path: no_ref/train-* - split: validation path: no_ref/validation-* - split: test path: no_ref/test-* --- **Arxiv Classification: a classification of Arxiv Papers (11 classes).** This dataset is intended for long context classification (documents have all > 4k tokens). \ Copied from "Long Document Classification From Local Word Glimpses via Recurrent Attention Learning" ``` @ARTICLE{8675939, author={He, Jun and Wang, Liqun and Liu, Liu and Feng, Jiao and Wu, Hao}, journal={IEEE Access}, title={Long Document Classification From Local Word Glimpses via Recurrent Attention Learning}, year={2019}, volume={7}, number={}, pages={40707-40718}, doi={10.1109/ACCESS.2019.2907992} } ``` * See: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8675939 * See: https://github.com/LiqunW/Long-document-dataset It contains 11 slightly unbalanced classes, 33k Arxiv Papers divided into 3 splits: train (28k), val (2.5k) and test (2.5k). 2 configs: * default * no_ref, removes references to the class inside the document (eg: [cs.LG] -> []) Compatible with [run_glue.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) script: ``` export MODEL_NAME=roberta-base export MAX_SEQ_LENGTH=512 python run_glue.py \ --model_name_or_path $MODEL_NAME \ --dataset_name ccdv/arxiv-classification \ --do_train \ --do_eval \ --max_seq_length $MAX_SEQ_LENGTH \ --per_device_train_batch_size 8 \ --gradient_accumulation_steps 4 \ --learning_rate 2e-5 \ --num_train_epochs 1 \ --max_eval_samples 500 \ --output_dir tmp/arxiv ```

ccdv/arxiv-summarization

ccdv

2024-08-08T05:49:50Z

2,683

90

[ "task_categories:summarization", "task_categories:text-generation", "multilinguality:monolingual", "language:en", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "conditional-text-generation" ]

[ "summarization", "text-generation" ]

2022-03-02T23:29:22Z

null

--- language: - en multilinguality: - monolingual size_categories: - 100K<n<1M task_categories: - summarization - text-generation task_ids: [] tags: - conditional-text-generation dataset_info: - config_name: document features: - name: article dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 7118099012 num_examples: 203037 - name: validation num_bytes: 216177092 num_examples: 6436 - name: test num_bytes: 216560120 num_examples: 6440 download_size: 3614462676 dataset_size: 7550836224 - config_name: section features: - name: article dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 7201498856 num_examples: 203037 - name: validation num_bytes: 218793173 num_examples: 6436 - name: test num_bytes: 219196348 num_examples: 6440 download_size: 3649452807 dataset_size: 7639488377 configs: - config_name: document data_files: - split: train path: document/train-* - split: validation path: document/validation-* - split: test path: document/test-* - config_name: section data_files: - split: train path: section/train-* - split: validation path: section/validation-* - split: test path: section/test-* default: true train-eval-index: - config: document task: summarization task_id: summarization splits: eval_split: test col_mapping: article: text abstract: target --- # Arxiv dataset for summarization Dataset for summarization of long documents.\ Adapted from this [repo](https://github.com/armancohan/long-summarization).\ Note that original data are pre-tokenized so this dataset returns " ".join(text) and add "\n" for paragraphs. \ This dataset is compatible with the [`run_summarization.py`](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) script from Transformers if you add this line to the `summarization_name_mapping` variable: ```python "ccdv/arxiv-summarization": ("article", "abstract") ``` ### Data Fields - `id`: paper id - `article`: a string containing the body of the paper - `abstract`: a string containing the abstract of the paper ### Data Splits This dataset has 3 splits: _train_, _validation_, and _test_. \ Token counts are white space based. | Dataset Split | Number of Instances | Avg. tokens | | ------------- | --------------------|:----------------------| | Train | 203,037 | 6038 / 299 | | Validation | 6,436 | 5894 / 172 | | Test | 6,440 | 5905 / 174 | # Cite original article ``` @inproceedings{cohan-etal-2018-discourse, title = "A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents", author = "Cohan, Arman and Dernoncourt, Franck and Kim, Doo Soon and Bui, Trung and Kim, Seokhwan and Chang, Walter and Goharian, Nazli", booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)", month = jun, year = "2018", address = "New Orleans, Louisiana", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/N18-2097", doi = "10.18653/v1/N18-2097", pages = "615--621", abstract = "Neural abstractive summarization models have led to promising results in summarizing relatively short documents. We propose the first model for abstractive summarization of single, longer-form documents (e.g., research papers). Our approach consists of a new hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. Empirical results on two large-scale datasets of scientific papers show that our model significantly outperforms state-of-the-art models.", } ```

microsoft/cats_vs_dogs

microsoft

2024-08-08T05:35:11Z

1,810

44

[ "task_categories:image-classification", "task_ids:multi-class-image-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:unknown", "size_categories:10K<n<100K", "format:parquet", "modality:image", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[ "image-classification" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - image-classification task_ids: - multi-class-image-classification paperswithcode_id: cats-vs-dogs pretty_name: Cats Vs. Dogs dataset_info: features: - name: image dtype: image - name: labels dtype: class_label: names: '0': cat '1': dog splits: - name: train num_bytes: 667071605.79 num_examples: 23410 download_size: 721642420 dataset_size: 667071605.79 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for Cats Vs. Dogs ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Cats vs Dogs Dataset](https://www.microsoft.com/en-us/download/details.aspx?id=54765) - **Repository:** - **Paper:** [Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization](https://www.microsoft.com/en-us/research/wp-content/uploads/2007/10/CCS2007.pdf) - **Leaderboard:** [Dogs vs. Cats](https://www.kaggle.com/competitions/dogs-vs-cats) - **Point of Contact:** ### Dataset Summary A large set of images of cats and dogs. There are 1738 corrupted images that are dropped. This dataset is part of a now-closed Kaggle competition and represents a subset of the so-called Asirra dataset. From the competition page: > The Asirra data set > > Web services are often protected with a challenge that's supposed to be easy for people to solve, but difficult for computers. Such a challenge is often called a [CAPTCHA](http://www.captcha.net/) (Completely Automated Public Turing test to tell Computers and Humans Apart) or HIP (Human Interactive Proof). HIPs are used for many purposes, such as to reduce email and blog spam and prevent brute-force attacks on web site passwords. > > Asirra (Animal Species Image Recognition for Restricting Access) is a HIP that works by asking users to identify photographs of cats and dogs. This task is difficult for computers, but studies have shown that people can accomplish it quickly and accurately. Many even think it's fun! Here is an example of the Asirra interface: > > Asirra is unique because of its partnership with [Petfinder.com](https://www.petfinder.com/), the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States. Kaggle is fortunate to offer a subset of this data for fun and research. ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given image as either containing a cat or a dog. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-cats-vs-dogs). ### Languages English. ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x375 at 0x29CEAD71780>, 'labels': 0 } ``` ### Data Fields The data instances have the following fields: - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`. - `labels`: an `int` classification label. Class Label Mappings: ``` { "cat": 0, "dog": 1, } ``` ### Data Splits | | train | |---------------|------:| | # of examples | 23410 | ## Dataset Creation ### Curation Rationale This subset was to built to test whether computer vision algorithms can beat the Asirra CAPTCHA: From the competition page: > Image recognition attacks > > While random guessing is the easiest form of attack, various forms of image recognition can allow an attacker to make guesses that are better than random. There is enormous diversity in the photo database (a wide variety of backgrounds, angles, poses, lighting, etc.), making accurate automatic classification difficult. In an informal poll conducted many years ago, computer vision experts posited that a classifier with better than 60% accuracy would be difficult without a major advance in the state of the art. For reference, a 60% classifier improves the guessing probability of a 12-image HIP from 1/4096 to 1/459. ### Source Data #### Initial Data Collection and Normalization This dataset is a subset of the Asirra dataset. From the competition page: > Asirra is unique because of its partnership with Petfinder.com, the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States. #### Who are the source language producers? The users of [Petfinder.com](https://www.petfinder.com/). ### Annotations #### Annotation process The images were annotated by selecting a pet category on [Petfinder.com](https://www.petfinder.com/). #### Who are the annotators? The users of [Petfinder.com](https://www.petfinder.com/). ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases From the paper: > Unlike many image-based CAPTCHAs which are abstract or subjective, Asirra’s challenges are concrete, inoffensive (cute, by some accounts), require no specialized or culturally biased knowledge, and have definite ground truth. This makes Asirra less frustrating for humans. Some beta-testers found it fun. The four-year-old child of one asked several times to “play the cat and dog game again.” ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ```bibtex @Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization, author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared}, title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization}, booktitle = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)}, year = {2007}, month = {October}, publisher = {Association for Computing Machinery, Inc.}, url = {https://www.microsoft.com/en-us/research/publication/asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization/}, edition = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)}, } ``` ### Contributions Thanks to [@nateraw](https://github.com/nateraw) for adding this dataset.

applied-ai-018/pretraining_v1-omega_books

applied-ai-018

2024-08-05T19:01:31Z

37,194

1

[ "size_categories:100M<n<1B", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-07-31T08:53:54Z

null

--- dataset_info: config_name: CC-MAIN-2013-20 features: - name: text dtype: string - name: id dtype: string - name: dump dtype: string - name: url dtype: string - name: file_path dtype: string - name: language dtype: string - name: language_score dtype: float64 - name: token_count dtype: int64 - name: score dtype: float64 - name: int_score dtype: int64 splits: - name: train num_bytes: 235476901236 num_examples: 51901183 download_size: 138494178972 dataset_size: 235476901236 configs: - config_name: CC-MAIN-2013-20 data_files: - split: train path: CC-MAIN-2013-20/train-* ---

fixie-ai/librispeech_asr

fixie-ai

2024-08-05T18:38:33Z

12,668

3

[ "language:en", "size_categories:100K<n<1M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-07-19T02:34:30Z

null

--- language: - en dataset_info: - config_name: clean features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string - name: continuation dtype: string splits: - name: test num_bytes: 623948478.48 num_examples: 2620 - name: validation num_bytes: 622190064.956 num_examples: 2703 - name: train.360 num_bytes: 41953890926.124 num_examples: 104014 - name: train.100 num_bytes: 11606313661.774 num_examples: 28539 download_size: 53886816833 dataset_size: 54806343131.334 - config_name: other features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string - name: continuation dtype: string splits: - name: train.500 num_bytes: 57330687390.808 num_examples: 148688 - name: validation num_bytes: 591511495.496 num_examples: 2864 - name: test num_bytes: 616939198.113 num_examples: 2939 download_size: 57019309170 dataset_size: 58539138084.417 configs: - config_name: clean data_files: - split: test path: clean/test-* - split: validation path: clean/validation-* - split: train.360 path: clean/train.360-* - split: train.100 path: clean/train.100-* - config_name: other data_files: - split: train.500 path: other/train.500-* - split: validation path: other/validation-* - split: test path: other/test-* ---

Vezora/Open-Critic-GPT

Vezora

2024-07-28T21:00:25Z

119

95

[ "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-07-27T21:50:27Z

null

--- {vezora license} --- <img src="https://huggingface.co/Vezora/Agent-7b-v1/resolve/main/Designer.png" width="200" height="200" /> # Open-Critic-GPT Dataset ## Overview **Creator** [Nicolas Mejia-Petit](https://twitter.com/mejia_petit) [My Kofi](https://ko-fi.com/nicolasmejiapetit) The Open-Critic-GPT dataset is a synthetic dataset created to train models in both identifying and fixing bugs in code. The dataset is generated using a unique synthetic data pipeline which involves: 1. Prompting a local model with an existing code example. 2. Introducing bugs into the code. While also having the model, from a first-person perspective, find the bugs and explain them. 3. Manipulating the data by shifting around where the broken code and working code is, and removing the # bug// and # error// comments from the code. This process allows the creation of two distinct datasets within Open-Critic-GPT: - **Code-Preference-Pairs Dataset**: (SFT) Contains pairs of duplicate code examples, with the only difference being one the rejected example has the bugged code 'surgically transplanted in' while the accepted is left the same. - **Open-Critic-GPT Dataset**: (DPO) Trains the model to find bugs and produce working code from broken code. - Both dataset's spans a total of 127 different language/structures, (some may have been lost in conversion started with 122k ended with 55k, due to lack of structured output, a finetuned model may preform better structured outputs.) - Both datasets contain of ~55K examples each (which both come from the same parent example) ## Dataset Structure The dataset is organized as follows: - **Code Examples**: Each code example consists of a a given snippet of bugged code and asked to find the bugs and fix them: - **Bugged Code**: The version of the code with introduced bugs and no comments, to avoid the model from learning from comments that say "Bug" or "Error". - **Explanation**: Explanation are provided for each bugged code example, detailing the nature of the bug, what the bug does to the code, and tips to avoid it. - **Fixed Code**: Lastly the model write the fully working code, with bugs fixed and comments added to the code. ## Usage - Just give me credit :) - Oh and current employee's of 'Open AI' and or the company as a whole is NOT permitted use this dataset or any derivative work that may come for training. It is mentioned in the custom apache license. - Otherwise to everyone else, it falls under Apache 2.0 :). ### Training Models When training models with the Open-Critic-GPT dataset, it is essential to use a data collator to ensure that the loss is not calculated on the bugged code. The data collator manages the dataset during training to provide the model with the correct inputs and outputs for loss calculation. ### Crediting dataset creators: - This dataset was created using 'm-a-p/CodeFeedback-Filtered-Instruction' Which contiains data from several different sources - Here are the orginal authors of the oringal sources, Thank you to the following authors: Nick Roshdieh for evol Instruct, Ajinkya Bawase for Python shareGPT 23k, Intellligent Software Engineering for Magicoder, and Multimodal Art Projection for the compiled and filtered m-a-p/CodeFeedback-Filtered-Instruction. ### Begging for money section. - I created this dataset off a single 3090. Imagine what I could do with two. - I can't continue to work on these open source projects, with out receiving a sponsorship or a form of compensation, all the money I make from this will go dirrectly back into helping the open source community. - If you can, It would mean the world to me any donation helps me release this work for free. thank you :) - [Kofi](https://ko-fi.com/nicolasmejiapetit)

google/docci

google

2024-07-24T16:00:33Z

176

72

[ "task_categories:text-to-image", "task_categories:image-to-text", "task_ids:image-captioning", "annotations_creators:expert-generated", "annotations_creators:crowdsourced", "language_creators:other", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-4.0", "size_categories:10K<n<100K", "arxiv:2404.19753", "region:us" ]

[ "text-to-image", "image-to-text" ]

2023-10-31T14:46:10Z

2

--- annotations_creators: - expert-generated - crowdsourced language: - en language_creators: - other license: - cc-by-4.0 multilinguality: - monolingual pretty_name: DOCCI size_categories: - 10K<n<100K source_datasets: - original tags: [] task_categories: - text-to-image - image-to-text task_ids: - image-captioning --- # Dataset Card for DOCCI ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://google.github.io/docci - **Paper:** [arXiv](https://arxiv.org/pdf/2404.19753) - **Data Explorer:** [Check images and descriptions](https://google.github.io/docci/viz.html?c=&p=1) - **Point of Contact:** [email protected] - **Report an Error:** [Google Forms](https://forms.gle/v8sUoXWHvuqrWyfe9) ### Dataset Summary DOCCI (Descriptions of Connected and Contrasting Images) is a collection of images paired with detailed descriptions. The descriptions explain the key elements of the images, as well as secondary information such as background, lighting, and settings. The images are specifically taken to help assess the precise visual properties of images. DOCCI also includes many related images that vary in having key differences from the others. All descriptions are manually annotated to ensure they adequately distinguish each image from its counterparts. ### Supported Tasks Text-to-Image and Image-to-Text generation ### Languages English ## Dataset Structure ### Data Instances ``` { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1536x2048>, 'example_id': 'qual_dev_00000', 'description': 'An indoor angled down medium close-up front view of a real sized stuffed dog with white and black colored fur wearing a blue hard hat with a light on it. A couple inches to the right of the dog is a real sized black and white penguin that is also wearing a blue hard hat with a light on it. The dog is sitting, and is facing slightly towards the right while looking to its right with its mouth slightly open, showing its pink tongue. The dog and penguin are placed on a gray and white carpet, and placed against a white drawer that has a large gray cushion on top of it. Behind the gray cushion is a transparent window showing green trees on the outside.' } ``` ### Data Fields Name | Explanation --- | --- `image` | PIL.JpegImagePlugin.JpegImageFile `example_id` | The unique ID of an example follows this format: `<SPLIT_NAME>_<EXAMPLE_NUMBER>`. `description` | Text description of the associated image. ### Data Splits Dataset | Train | Test | Qual Dev | Qual Test ---| ---: | ---: | ---: | ---: DOCCI | 9,647 | 5,000 | 100 | 100 DOCCI-AAR | 4,932 | 5,000 | -- | -- ## Dataset Creation ### Curation Rationale DOCCI is designed as an evaluation dataset for both text-to-image (T2I) and image-to-text (I2T) generation. Please see our paper for more details. ### Source Data #### Initial Data Collection All images were taken by one of the authors and their family. ### Annotations #### Annotation process All text descriptions were written by human annotators. We do not rely on any automated process in our data annotation pipeline. Please see Appendix A of [our paper](https://arxiv.org/pdf/2404.19753) for details about image curation. ### Personal and Sensitive Information We manually reviewed all images for personally identifiable information (PII), removing some images and blurring detected faces, phone numbers, and URLs to protect privacy. For text descriptions, we instructed annotators to exclude any PII, such as people's names, phone numbers, and URLs. After the annotation phase, we employed automatic tools to scan for PII, ensuring the descriptions remained free of such information. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ### Licensing Information CC BY 4.0 ### Citation Information ``` @inproceedings{OnoeDocci2024, author = {Yasumasa Onoe and Sunayana Rane and Zachary Berger and Yonatan Bitton and Jaemin Cho and Roopal Garg and Alexander Ku and Zarana Parekh and Jordi Pont-Tuset and Garrett Tanzer and Su Wang and Jason Baldridge}, title = {{DOCCI: Descriptions of Connected and Contrasting Images}}, booktitle = {ECCV}, year = {2024} } ```

mlfoundations/dclm-baseline-1.0

mlfoundations

2024-07-22T15:27:52Z

1,151,865

218

[ "license:cc-by-4.0", "arxiv:2406.11794", "region:us" ]

[]

2024-06-17T18:57:13Z

null

--- license: cc-by-4.0 dataset_info: features: - name: bff_contained_ngram_count_before_dedupe dtype: int64 - name: language_id_whole_page_fasttext struct: - name: en dtype: float64 - name: metadata struct: - name: Content-Length dtype: string - name: Content-Type dtype: string - name: WARC-Block-Digest dtype: string - name: WARC-Concurrent-To dtype: string - name: WARC-Date dtype: timestamp[s] - name: WARC-IP-Address dtype: string - name: WARC-Identified-Payload-Type dtype: string - name: WARC-Payload-Digest dtype: string - name: WARC-Record-ID dtype: string - name: WARC-Target-URI dtype: string - name: WARC-Type dtype: string - name: WARC-Warcinfo-ID dtype: string - name: WARC-Truncated dtype: string - name: previous_word_count dtype: int64 - name: text dtype: string - name: url dtype: string - name: warcinfo dtype: string - name: fasttext_openhermes_reddit_eli5_vs_rw_v2_bigram_200k_train_prob dtype: float64 --- ## DCLM-baseline DCLM-baseline is a 4T token / 3B document pretraining dataset that achieves strong performance on language model benchmarks. Below are comparisions of model trained on DCLM-baseline with other models in the 7B regime. | Model | Params | Tokens | Open dataset? | CORE | MMLU | EXTENDED | |---------------|--------|--------|---------------|----------|----------|----------| | **Open weights, closed datasets** | | | | | | | | Llama2 | 7B | 2T | ✗ | 49.2 | 45.8 | 34.1 | | DeepSeek | 7B | 2T | ✗ | 50.7 | 48.5 | 35.3 | | Mistral-0.3 | 7B | ? | ✗ | 57.0 | 62.7 | 45.1 | | QWEN-2 | 7B | ? | ✗ | 57.5 | **71.9** | 50.5 | | Llama3 | 8B | 15T | ✗ | 57.6 | 66.2 | 46.3 | | Gemma | 8B | 6T | ✗ | 57.8 | 64.3 | 44.6 | | Phi-3 | 7B | ? | ✗ | **61.0** | 69.9 | **57.9** | | **Open weights, open datasets** | | | | | | | | Falcon | 7B | 1T | ✓ | 44.1 | 27.4 | 25.1 | | Amber | 7B | 1.2T | ✓ | 39.8 | 27.9 | 22.3 | | Crystal | 7B | 1.2T | ✓ | 48.0 | 48.2 | 33.2 | | OLMo-1.7 | 7B | 2.1T | ✓ | 47.0 | 54.0 | 34.2 | | MAP-Neo | 7B | 4.5T | ✓ | **50.2** | **57.1** | **40.4** | | **Models we trained** | | | | | | | | FineWeb edu | 7B | 0.14T | ✓ | 38.7 | 26.3 | 22.1 | | FineWeb edu | 7B | 0.28T | ✓ | 41.9 | 37.3 | 24.5 | | **DCLM-BASELINE** | 7B | 0.14T | ✓ | 44.1 | 38.3 | 25.0 | | **DCLM-BASELINE** | 7B | 0.28T | ✓ | 48.9 | 50.8 | 31.8 | | **DCLM-BASELINE** | 7B | 2.6T | ✓ | **57.1** | **63.7** | **45.4** | ## Dataset Details ### Dataset Description - **Curated by:** The DCLM Team - **Language(s) (NLP):** English - **License:** CC-by-4.0 ### Dataset Sources - **Repository:** https://datacomp.ai/dclm - **Paper:**: https://arxiv.org/abs/2406.11794 - **Construction Code**: https://github.com/mlfoundations/dclm ## Uses ### Direct Use DCLM-Baseline is intended to be used as a research baseline for the DCLM benchmark. It demonstrates the importance of data curation in training performant language models. ### Out-of-Scope Use DCLM-Baseline is not intended for training production-ready models or for specific domains such as code and math. It may not perform as well as domain-specific datasets for these tasks. Due to these limitations, the dataset is intended for research use only. DCLM-Baseline is a subset of the DCLM-Pool, which is a corpus of 240 trillion tokens derived from Common Crawl. The dataset is in plain text format. ## Dataset Creation ### Curation Rationale DCLM-Baseline was created to demonstrate the effectiveness of the DCLM testbed in developing high-quality training sets for language models. It serves as a proof of concept for the data curation strategies enabled by DCLM and is designed to be a research baseline for the benchmark. ### Source Data #### Data Collection and Processing DCLM-Baseline was created by applying a series of cleaning, filtering, and deduplication steps to the raw Common Crawl data (DCLM-Pool). The key steps include: 1. Heuristic cleaning and filtering (reproduction of RefinedWeb) 2. Deduplication using a Bloom filter 3. Model-based filtering using a fastText classifier trained on instruction-formatted data (OpenHermes 2.5 and r/ExplainLikeImFive) #### Who are the source data producers? The source data is from Common Crawl, which is a repository of web crawl data. ### Personal and Sensitive Information [More Information Needed] ## Bias, Risks, and Limitations The dataset may contain biases present in the Common Crawl data. The dataset's performance on code and math tasks is limited compared to its performance on language understanding tasks. DCLM-Baseline is designed for research purposes only. ### Recommendations Users should be aware of the potential biases and limitations of the dataset, especially when using it for specific domains like code and math. The dataset should only be used for research purposes in the context of the DCLM benchmark. ## Citation ```bibtex @misc{li2024datacomplm, title={DataComp-LM: In search of the next generation of training sets for language models}, author={Jeffrey Li and Alex Fang and Georgios Smyrnis and Maor Ivgi and Matt Jordan and Samir Gadre and Hritik Bansal and Etash Guha and Sedrick Keh and Kushal Arora and Saurabh Garg and Rui Xin and Niklas Muennighoff and Reinhard Heckel and Jean Mercat and Mayee Chen and Suchin Gururangan and Mitchell Wortsman and Alon Albalak and Yonatan Bitton and Marianna Nezhurina and Amro Abbas and Cheng-Yu Hsieh and Dhruba Ghosh and Josh Gardner and Maciej Kilian and Hanlin Zhang and Rulin Shao and Sarah Pratt and Sunny Sanyal and Gabriel Ilharco and Giannis Daras and Kalyani Marathe and Aaron Gokaslan and Jieyu Zhang and Khyathi Chandu and Thao Nguyen and Igor Vasiljevic and Sham Kakade and Shuran Song and Sujay Sanghavi and Fartash Faghri and Sewoong Oh and Luke Zettlemoyer and Kyle Lo and Alaaeldin El-Nouby and Hadi Pouransari and Alexander Toshev and Stephanie Wang and Dirk Groeneveld and Luca Soldaini and Pang Wei Koh and Jenia Jitsev and Thomas Kollar and Alexandros G. Dimakis and Yair Carmon and Achal Dave and Ludwig Schmidt and Vaishaal Shankar}, year={2024}, eprint={2406.11794}, archivePrefix={arXiv}, primaryClass={id='cs.LG' full_name='Machine Learning' is_active=True alt_name=None in_archive='cs' is_general=False description='Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.'} ```

rethinklab/Bench2Drive-Full

rethinklab

2024-07-22T06:46:56Z

15,646

2

[ "license:apache-2.0", "region:us" ]

[]

2024-05-13T05:56:17Z

null

--- license: apache-2.0 ---

mlfoundations/dclm-baseline-1.0-parquet

mlfoundations

2024-07-19T17:35:58Z

24,886

27

[ "language:en", "license:cc-by-4.0", "size_categories:1B<n<10B", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2406.11794", "region:us" ]

[]

2024-06-30T20:31:14Z

null

--- language: - en license: cc-by-4.0 --- ## DCLM-baseline ***Note: this is an identical copy of https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0, where all the files have been mapped to a parquet format.*** DCLM-baseline is a 4T token / 3B document pretraining dataset that achieves strong performance on language model benchmarks. Below are comparisions of model trained on DCLM-baseline with other models in the 7B regime. | Model | Params | Tokens | Open dataset? | CORE | MMLU | EXTENDED | |---------------|--------|--------|---------------|----------|----------|----------| | **Open weights, closed datasets** | | | | | | | | Llama2 | 7B | 2T | ✗ | 49.2 | 45.8 | 34.1 | | DeepSeek | 7B | 2T | ✗ | 50.7 | 48.5 | 35.3 | | Mistral-0.3 | 7B | ? | ✗ | 57.0 | 62.7 | 45.1 | | QWEN-2 | 7B | ? | ✗ | 57.5 | **71.9** | 50.5 | | Llama3 | 8B | 15T | ✗ | 57.6 | 66.2 | 46.3 | | Gemma | 8B | 6T | ✗ | 57.8 | 64.3 | 44.6 | | Phi-3 | 7B | ? | ✗ | **61.0** | 69.9 | **57.9** | | **Open weights, open datasets** | | | | | | | | Falcon | 7B | 1T | ✓ | 44.1 | 27.4 | 25.1 | | Amber | 7B | 1.2T | ✓ | 39.8 | 27.9 | 22.3 | | Crystal | 7B | 1.2T | ✓ | 48.0 | 48.2 | 33.2 | | OLMo-1.7 | 7B | 2.1T | ✓ | 47.0 | 54.0 | 34.2 | | MAP-Neo | 7B | 4.5T | ✓ | **50.2** | **57.1** | **40.4** | | **Models we trained** | | | | | | | | FineWeb edu | 7B | 0.14T | ✓ | 38.7 | 26.3 | 22.1 | | FineWeb edu | 7B | 0.28T | ✓ | 41.9 | 37.3 | 24.5 | | **DCLM-BASELINE** | 7B | 0.14T | ✓ | 44.1 | 38.3 | 25.0 | | **DCLM-BASELINE** | 7B | 0.28T | ✓ | 48.9 | 50.8 | 31.8 | | **DCLM-BASELINE** | 7B | 2.6T | ✓ | **57.1** | **63.7** | **45.4** | ## Dataset Details ### Dataset Description - **Curated by:** The DCLM Team - **Language(s) (NLP):** English - **License:** CC-by-4.0 ### Dataset Sources - **Repository:** https://datacomp.ai/dclm - **Paper:**: https://arxiv.org/abs/2406.11794 - **Construction Code**: https://github.com/mlfoundations/dclm ## Uses ### Direct Use DCLM-Baseline is intended to be used as a research baseline for the DCLM benchmark. It demonstrates the importance of data curation in training performant language models. ### Out-of-Scope Use DCLM-Baseline is not intended for training production-ready models or for specific domains such as code and math. It may not perform as well as domain-specific datasets for these tasks. Due to these limitations, the dataset is intended for research use only. DCLM-Baseline is a subset of the DCLM-Pool, which is a corpus of 240 trillion tokens derived from Common Crawl. The dataset is in plain text format. ## Dataset Creation ### Curation Rationale DCLM-Baseline was created to demonstrate the effectiveness of the DCLM testbed in developing high-quality training sets for language models. It serves as a proof of concept for the data curation strategies enabled by DCLM and is designed to be a research baseline for the benchmark. ### Source Data #### Data Collection and Processing DCLM-Baseline was created by applying a series of cleaning, filtering, and deduplication steps to the raw Common Crawl data (DCLM-Pool). The key steps include: 1. Heuristic cleaning and filtering (reproduction of RefinedWeb) 2. Deduplication using a Bloom filter 3. Model-based filtering using a fastText classifier trained on instruction-formatted data (OpenHermes 2.5 and r/ExplainLikeImFive) #### Who are the source data producers? The source data is from Common Crawl, which is a repository of web crawl data. ### Personal and Sensitive Information [More Information Needed] ## Bias, Risks, and Limitations The dataset may contain biases present in the Common Crawl data. The dataset's performance on code and math tasks is limited compared to its performance on language understanding tasks. DCLM-Baseline is designed for research purposes only. ### Recommendations Users should be aware of the potential biases and limitations of the dataset, especially when using it for specific domains like code and math. The dataset should only be used for research purposes in the context of the DCLM benchmark. ## Citation ```bibtex @misc{li2024datacomplm, title={DataComp-LM: In search of the next generation of training sets for language models}, author={Jeffrey Li and Alex Fang and Georgios Smyrnis and Maor Ivgi and Matt Jordan and Samir Gadre and Hritik Bansal and Etash Guha and Sedrick Keh and Kushal Arora and Saurabh Garg and Rui Xin and Niklas Muennighoff and Reinhard Heckel and Jean Mercat and Mayee Chen and Suchin Gururangan and Mitchell Wortsman and Alon Albalak and Yonatan Bitton and Marianna Nezhurina and Amro Abbas and Cheng-Yu Hsieh and Dhruba Ghosh and Josh Gardner and Maciej Kilian and Hanlin Zhang and Rulin Shao and Sarah Pratt and Sunny Sanyal and Gabriel Ilharco and Giannis Daras and Kalyani Marathe and Aaron Gokaslan and Jieyu Zhang and Khyathi Chandu and Thao Nguyen and Igor Vasiljevic and Sham Kakade and Shuran Song and Sujay Sanghavi and Fartash Faghri and Sewoong Oh and Luke Zettlemoyer and Kyle Lo and Alaaeldin El-Nouby and Hadi Pouransari and Alexander Toshev and Stephanie Wang and Dirk Groeneveld and Luca Soldaini and Pang Wei Koh and Jenia Jitsev and Thomas Kollar and Alexandros G. Dimakis and Yair Carmon and Achal Dave and Ludwig Schmidt and Vaishaal Shankar}, year={2024}, eprint={2406.11794}, archivePrefix={arXiv}, primaryClass={id='cs.LG' full_name='Machine Learning' is_active=True alt_name=None in_archive='cs' is_general=False description='Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.'} ```

TheBritishLibrary/EThOS-PhD-metadata

TheBritishLibrary

2024-07-19T16:28:25Z

21

2

[ "task_categories:text-classification", "task_categories:fill-mask", "task_ids:multi-label-classification", "task_ids:masked-language-modeling", "multilinguality:monolingual", "language:en", "region:us" ]

[ "text-classification", "fill-mask" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: [] language: - en language_creators: [] license: [] multilinguality: - monolingual pretty_name: EThOS PhD metadata size_categories: [] source_datasets: [] tags: [] task_categories: - text-classification - fill-mask task_ids: - multi-label-classification - masked-language-modeling --- # Dataset Card for EThOS PhD metadata ## Table of Contents - [Dataset Card for blbooksgenre](#dataset-card-for-EThOS PhD metadata) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Supervised tasks](#supervised-tasks) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:**: https://bl.iro.bl.uk/concern/datasets/10cc13f9-797d-41f2-a7e2-d29f4306133e?locale=en - **Repository:** https://doi.org/10.23636/rcm4-zk44 - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The data in this collection comprises the bibliographic metadata for all UK doctoral theses listed in EThOS, the UK's national thesis service. We estimate the data covers around 98% of all PhDs ever awarded by UK Higher Education institutions, dating back to 1787. Thesis metadata from every PhD-awarding university in the UK is included. You can investigate and re-use this unique collection of UK universities' PhD thesis data to analyse trends in postgraduate research, make connections between researchers, apply large data analysis, improve citation of theses and many more applications. [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] #### Supervised tasks [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure [More Information Needed] ### Data Instances An example data instance: ```python {'Abstract': ' ', 'Author': 'Loizou, Panos A.', 'Author ISNI': 'https://isni.org/isni/0000000136122593', 'DOI': ' ', 'Date': datetime.datetime(1989, 1, 1, 0, 0), 'EThOS URL': 'https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.232781', 'Funder(s)': ' ', 'IR URL': ' ', 'Institution': 'University of Manchester', 'Institution ISNI': 'https://isni.org/isni/0000000121662407', 'ORCID': ' ', 'Qualification': 'Thesis (Ph.D.)', 'Subject Discipline': 0, 'Supervisor(s)': ' ', 'Title': 'Computation and measurement of turbulent flow through idealized turbine blade passages'} ``` ### Data Fields [More Information Needed] ### Data Splits This dataset contains a single split `train`. ## Dataset Creation [More Information Needed] ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data [More Information Needed] ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The books are licensed under the [CC BY 4.0 Attribution](https://creativecommons.org/licenses/by/4.0/) license. ### Citation Information

nunorc/squad_v1_pt

nunorc

2024-07-17T06:57:39Z

127

12

[ "task_categories:question-answering", "task_ids:extractive-qa", "task_ids:open-domain-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:pt", "license:mit", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:1606.05250", "region:us" ]

[ "question-answering" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - pt license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa - open-domain-qa pretty_name: SquadV1Pt dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: train num_bytes: 85322985 num_examples: 87599 - name: validation num_bytes: 11265418 num_examples: 10570 download_size: 17430106 dataset_size: 96588403 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* --- # Dataset Card for "squad_v1_pt" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/nunorc/squad-v1.1-pt](https://github.com/nunorc/squad-v1.1-pt) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 39.53 MB - **Size of the generated dataset:** 96.72 MB - **Total amount of disk used:** 136.25 MB ### Dataset Summary Portuguese translation of the SQuAD dataset. The translation was performed automatically using the Google Cloud API. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 39.53 MB - **Size of the generated dataset:** 96.72 MB - **Total amount of disk used:** 136.25 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [0], "text": ["Saint Bernadette Soubirous"] }, "context": "\"Arquitetonicamente, a escola tem um caráter católico. No topo da cúpula de ouro do edifício principal é uma estátua de ouro da ...", "id": "5733be284776f41900661182", "question": "A quem a Virgem Maria supostamente apareceu em 1858 em Lourdes, na França?", "title": "University_of_Notre_Dame" } ``` ### Data Fields The data fields are the same among all splits. #### default - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. ### Data Splits | name | train | validation | | ------- | ----: | ---------: | | default | 87599 | 10570 | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{2016arXiv160605250R, author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev}, Konstantin and {Liang}, Percy}, title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}", journal = {arXiv e-prints}, year = 2016, eid = {arXiv:1606.05250}, pages = {arXiv:1606.05250}, archivePrefix = {arXiv}, eprint = {1606.05250}, } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@albertvillanova](https://github.com/albertvillanova), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

argilla/dpo-mix-7k

argilla

2024-07-16T13:31:40Z

880

162

[ "language:en", "license:mit", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "library:distilabel", "library:argilla", "region:us", "distilabel", "synthetic", "dpo", "argilla" ]

[]

2024-01-30T05:49:25Z

null

--- language: - en license: mit size_categories: - 1K<n<10K configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: dataset dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: chosen_rating dtype: float64 - name: rejected_rating dtype: float64 splits: - name: train num_bytes: 41362946 num_examples: 6750 - name: test num_bytes: 4586808 num_examples: 750 download_size: 24232011 dataset_size: 45949754 tags: - distilabel - synthetic - dpo - argilla --- # Argilla DPO Mix 7K Dataset > A small cocktail combining DPO datasets built by Argilla with [distilabel](https://github.com/argilla-io/distilabel). The goal of this dataset is having a small, high-quality DPO dataset by filtering only highly rated chosen responses. <div> <img src="https://cdn-uploads.huggingface.co/production/uploads/60420dccc15e823a685f2b03/Csd2-zPji7iwIxyz6UFe1.webp"> </div> <p align="center"> <a href="https://github.com/argilla-io/distilabel"> <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> </a> </p> ## Datasets mixed As already mentioned, this dataset mixes the following datasets: * [`argilla/distilabel-capybara-dpo-7k-binarized`](https://huggingface.co/datasets/argilla/distilabel-capybara-dpo-7k-binarized): random sample of highly scored chosen responses (>=4). * [`argilla/distilabel-intel-orca-dpo-pairs`](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs): random sample of highly scored chosen responses (>=8). * [`argilla/ultrafeedback-binarized-preferences-cleaned`](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned): random sample of highly scored chosen responses (>=4). The samples have been randomly selected from the original datasets with a proportion of 0.33 each, as can be seen via the `dataset` column of the dataset. ## Next steps * Adding more samples * Use data selection techniques to improve the diversity, usefulness, and complexity of the dataset.

argilla/distilabel-capybara-dpo-7k-binarized

argilla

2024-07-16T13:30:29Z

1,509

180

[ "task_categories:question-answering", "task_categories:text-generation", "language:en", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "library:distilabel", "library:argilla", "region:us", "Physics", "Biology", "Math", "Chemistry", "Culture", "Logic", "Roleplay", "rlaif", "rlhf", "dpo", "distilabel", "synthetic", "argilla" ]

[ "question-answering", "text-generation" ]

2024-01-26T08:36:14Z

null

--- license: apache-2.0 task_categories: - question-answering - text-generation language: - en tags: - Physics - Biology - Math - Chemistry - Culture - Logic - Roleplay - rlaif - rlhf - dpo - distilabel - synthetic - argilla pretty_name: CapybaraDPO-7k size_categories: - 1K<n<10K dataset_info: features: - name: source dtype: string - name: conversation list: - name: input dtype: string - name: output dtype: string - name: original_response dtype: string - name: generation_prompt sequence: string - name: raw_generation_responses sequence: string - name: new_generations sequence: string - name: prompt dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: rating_chosen dtype: int64 - name: rating_rejected dtype: int64 - name: chosen_model dtype: string - name: rejected_model dtype: string splits: - name: train num_bytes: 348791651 num_examples: 7563 download_size: 155776049 dataset_size: 348791651 configs: - config_name: default data_files: - split: train path: data/train-* --- # Capybara-DPO 7K binarized > A DPO dataset built with [distilabel](https://github.com/argilla-io/distilabel) atop the awesome [LDJnr/Capybara](https://huggingface.co/datasets/LDJnr/Capybara) > This is a preview version to collect feedback from the community. v2 will include the full base dataset and responses from more powerful models. <div> <img src="https://cdn-uploads.huggingface.co/production/uploads/60420dccc15e823a685f2b03/Vmr0FtTvnny6Snm-UDM_n.png"> </div> <p align="center"> <a href="https://github.com/argilla-io/distilabel"> <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> </a> </p> ## Why? Multi-turn dialogue data is key to fine-tune capable chat models. Multi-turn preference data has been used by the most relevant RLHF works (Anthropic, Meta Llama2, etc.). Unfortunately, there are very few multi-turn open datasets for DPO/RLHF. This dataset is the first of a series of datasets to fill this gap for the Open Source AI community. Why Capybara? Because it's 🔥 ## Dataset structure Here's a video showing the dataset structure using Argilla UI. For preference tuning, chosen and rejected mean the best/worse response to the last turn. <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/60420dccc15e823a685f2b03/KoYK-Or0JNNVS9PNLF8jJ.mp4"></video> ## How to use this dataset This dataset is a multi-turn preference dataset to improve chat capabilities of open-source LLMs. Chosen and rejected pairs are formatted following OpenAI's conversation format with potentially several turns between a user and an assistant. To use this dataset for DPO use only the last assistant message as `chosen`/`rejected` and the rest as `prompt`. Let's see an example, step by step. First let's keep only highly-scored chosen responses (scale is 1-5) and let's filter out very long conversations: ```python capy = load_dataset("argilla/distilabel-capybara-dpo-7k-binarized", split="train") capy = capy.filter( lambda r: r["rating_chosen"]>=4 ) capy = capy.map(lambda r: {"messages": len(r["chosen"])}).filter(lambda r: r["messages"]<18) ``` Then let's prepare this in the chatml prompt and `trl` format: ```python def chatml_format(example): # get everything except the last message as input prompt = tokenizer.apply_chat_template(example["chosen"][:-1], tokenize=False, add_generation_prompt=True) # get the last assistant responses chosen = example["chosen"][-1]["content"] + "</s>" rejected = example["rejected"][-1]["content"] + "</s>" return { "prompt": system + prompt, "chosen": chosen, "rejected": rejected, } # Save columns original_columns = capy.column_names # Format dataset capy = capy.map( chatml_format, remove_columns=original_columns ) ``` The dataset is now ready to be used for DPO fine-tuning! In our benchmarks with 7B models, we've seen this is a challenging dataset to learn from, the best results can be achieved by mixing it with other datasets like this [dpo mix 7k](https://huggingface.co/datasets/argilla/dpo-mix-7k). We'd love to hear from the community how this works with larger models and other hyperparams. ## How we've built this dataset ### Generate responses from 3 different OSS models In the spirit of UltraFeedback, in this step we generate three responses to the last user message using OSS 7B models and distilabel's `LLMPool` and the vLLM engine. We use Notus7B, NeuralBeagle and OpenHermes-2.5. Additionally, the original capybara dataset already has a generated assistant response (the last assistant response) we keep it for the next step. ```python from distilabel.llm import LLM, LLMPool, ProcessLLM from distilabel.tasks import TextGenerationTask, Task from distilabel.tasks.prompt import Prompt from distilabel.dataset import DatasetCheckpoint from distilabel.pipeline import Pipeline from datasets import load_dataset from dataclasses import dataclass from pathlib import Path dataset = load_dataset("LDJnr/Capybara", split="train") here = Path(__file__).parent.resolve() def extract_conversation(r): all_but_last = r["conversation"][:-1] all_but_last.append({"input": r["conversation"][-1]["input"]}) last = r["conversation"][-1]["output"] return {"input": all_but_last, "original_response": last} dataset = dataset.map(extract_conversation) @dataclass class NotusChatTextGeneration(TextGenerationTask): # custom class to generate prompts in the chatml format # skipped for brevity @dataclass class ChatMLTextGeneration(TextGenerationTask): # custom class to generate prompts in the chatml format # skipped for brevity save_frequency = len(dataset) // 1000 checkpointing = DatasetCheckpoint(path=here / "checkpoint_generation", save_frequency=save_frequency) def load_notus(task: Task) -> LLM: import os from distilabel.llm import vLLM from vllm import LLM os.environ["CUDA_VISIBLE_DEVICES"] = "0" return vLLM( vllm=LLM( model="argilla/notus-7b-v1", trust_remote_code=True ), task=task, max_new_tokens=1024, temperature=1, ) def load_beagle(task: Task) -> LLM: import os from distilabel.llm import vLLM from vllm import LLM os.environ["CUDA_VISIBLE_DEVICES"] = "1" return vLLM( vllm=LLM( model="mlabonne/NeuralBeagle14-7B", trust_remote_code=True ), task=task, max_new_tokens=1024, temperature=1, ) def load_hermes(task: Task) -> LLM: import os from distilabel.llm import vLLM from vllm import LLM os.environ["CUDA_VISIBLE_DEVICES"] = "2" return vLLM( vllm=LLM( model="teknium/OpenHermes-2.5-Mistral-7B", trust_remote_code=True ), task=task, max_new_tokens=1024, temperature=1, ) llm_pool = LLMPool( [ ProcessLLM(task=NotusChatTextGeneration(), load_llm_fn=load_notus), ProcessLLM(task=ChatMLTextGeneration(), load_llm_fn=load_beagle), ProcessLLM(task=ChatMLTextGeneration(), load_llm_fn=load_hermes), ] ) pipe_generation_pool = Pipeline(generator=llm_pool) dataset = pipe_generation_pool.generate( dataset=dataset, num_generations=len(llm_pool.llms), batch_size=32, display_progress_bar=True, checkpoint_strategy=checkpointing, ) ``` ### Generate a preference dataset from 4 responses At this point, we have 4 responses to each multi-turn dialogue. We will now use distilabel's `UltraFeedback.for_overall_quality()` preference model to judge the quality of responses. We use gpt-4-turbo but could have use other models. ```python from distilabel.tasks import UltraFeedbackTask from distilabel.llm import OpenAILLM from distilabel.pipeline import Pipeline from datasets import load_dataset def format_conversation(r): mapping_role = {"input": "<|user|>\n", "output":"<|assistant|>\n"} all_but_last = r["conversation"][:-1] all_but_last.append({"input": r["conversation"][-1]["input"]}) input = "" for e in all_but_last: for k,v in e.items(): input += f"{mapping_role[k]}{v}</s>\n" return {"input": input} # this formats the conversation input # one could choose other format prepared_dataset = dataset.map(format_conversation) # the LLM Judge will evaluate each response to the # last user message taking into account the conversation history labeler = OpenAILLM( task=UltraFeedbackTask.for_overall_quality(), model="gpt-4-1106-preview", num_threads=8, max_new_tokens=512, ) distilabeler = Pipeline( labeller=labeler ) # this computes ratings and natural language critiques for each pair distiset = distilabeler.generate(dataset=prepared_dataset, num_generations=4, display_progress_bar=True) ``` This preference step is also useful to evaluate the performance of the four models (3+ the original response in Capybara): ![image/png](https://cdn-uploads.huggingface.co/production/uploads/60420dccc15e823a685f2b03/FShIr2Hsu-dk9IpAihV1A.png) ## Benchmark results We've tested this new dataset by preference tuning [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B). The resulting model is [CapybaraHermes](https://huggingface.co/argilla/CapybaraHermes-2.5-Mistral-7B). CapybaraHermes has been preference tuned with LoRA and TRL for 3 epochs using argilla's [dpo mix 7k](https://huggingface.co/datasets/argilla/dpo-mix-7k). To test the impact on multi-turn performance we have used MTBench. We also include the Nous Benchmark results and Mistral-7B-Instruct-v0.2 for reference as it's a strong 7B model on MTBench: | Model | AGIEval | GPT4All | TruthfulQA | Bigbench | MTBench First Turn | MTBench Second Turn | Nous avg. | MTBench avg. | |-----------------------------------|---------|---------|------------|----------|------------|-------------|-----------|--------------| | CapybaraHermes-2.5-Mistral-7B | **43.8** | **73.35** | 57.07 | **42.44** | 8.24375 | **7.5625** | 54.16 | **7.903125** | | teknium/OpenHermes-2.5-Mistral-7B | 42.75 | 72.99 | 52.99 | 40.94 | **8.25** | 7.2875 | 52.42 | 7.76875 | | Mistral-7B-Instruct-v0.2 | 38.5 | 71.64 | **66.82** | 42.29 | 7.8375 | 7.1 | **54.81** | 7.46875 | The most interesting aspect in the context of the capybara-dpo dataset is the increased performance in MTBench Second Turn scores. For the merge lovers, we also preference tuned Beagle14-7B with a mix of capybara-dpo and distilabel orca pairs using the same recipe as NeuralBeagle (see [ YALL - Yet Another LLM Leaderboard](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard) for reference): | Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average| |------------------------------------------------------------------------------------------------------------------------------------|------:|------:|---------:|-------:|------:| |[DistilabelBeagle14-7B](https://huggingface.co/dvilasuero/DistilabelBeagle14-7B)| 45.29| 76.92| 71.66| 48.78| 60.66|

Locutusque/function-calling-chatml

Locutusque

2024-07-16T11:50:45Z

101

163

[ "license:apache-2.0", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-02-02T21:51:35Z

null

--- dataset_info: features: - name: system_message dtype: string - name: function_description dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 311913135 num_examples: 112960 download_size: 107035875 dataset_size: 311913135 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 --- # Dataset Card for "function-calling-chatml" Converted glaiveai/Glaive-function-calling-v2 to chatml format. ## Example entry ``` [ { "from": "system", "value": "You are a helpful assistant with access to the following functions. Use them if required -{\n \"name\": \"create_contact\",\n \"description\": \"Create a new contact\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"name\": {\n \"type\": \"string\",\n \"description\": \"The name of the contact\"\n },\n \"email\": {\n \"type\": \"string\",\n \"description\": \"The email address of the contact\"\n }\n },\n \"required\": [\n \"name\",\n \"email\"\n ]\n }\n}" }, { "from": "human", "value": "I need to create a new contact for my friend John Doe. His email is [email protected]." }, { "from": "function-call", "value": "{\"name\": \"create_contact\", \"arguments\": '{\"name\": \"John Doe\", \"email\": \"[email protected]\"}'}" }, { "from": "function-response", "value": "{\"status\": \"success\", \"message\": \"Contact for John Doe with email [email protected] has been created successfully.\"}" }, { "from": "gpt", "value": "I have successfully created a new contact for your friend John Doe with the email [email protected]." } ] ```

CAiRE/ASCEND

CAiRE

2024-07-16T08:56:04Z

376

35

[ "task_categories:automatic-speech-recognition", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:multilingual", "source_datasets:original", "language:en", "language:zh", "license:cc-by-sa-4.0", "size_categories:10K<n<100K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2112.06223", "region:us", "speech-recognition", "code-switching" ]

[ "automatic-speech-recognition" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - en - zh license: - cc-by-sa-4.0 multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - automatic-speech-recognition task_ids: [] pretty_name: 'ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation' tags: - speech-recognition - code-switching dataset_info: config_name: main features: - name: id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - name: duration dtype: float32 - name: language dtype: string - name: original_speaker_id dtype: int64 - name: session_id dtype: int64 - name: topic dtype: string splits: - name: train num_bytes: 1014573740.14 num_examples: 9869 - name: test num_bytes: 106171230.135 num_examples: 1315 - name: validation num_bytes: 106772517.43 num_examples: 1130 download_size: 1223536062 dataset_size: 1227517487.7050002 configs: - config_name: main data_files: - split: train path: main/train-* - split: test path: main/test-* - split: validation path: main/validation-* default: true --- # Dataset Card for ASCEND ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Usage](#usage) - [Dataset Structure](#dataset-structure) - [Data Splits](#data-instances) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** [Needs More Information] - **Repository:** [Needs More Information] - **Paper:** https://arxiv.org/abs/2112.06223 - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary ASCEND (A Spontaneous Chinese-English Dataset) introduces a high-quality resource of spontaneous multi-turn conversational dialogue Chinese-English code-switching corpus collected in Hong Kong. ASCEND consists of 10.62 hours of spontaneous speech with a total of ~12.3K utterances. The corpus is split into 3 sets: training, validation, and test with a ratio of 8:1:1 while maintaining a balanced gender proportion on each set. ### Supported Tasks and Leaderboards Code-switching ### Languages Chinese and English ## Usage To obtain the full dataset (complete with train, validation, and test set), simply run this: ``` import datasets dataset = datasets.load_dataset("CAiRE/ASCEND") ``` ## Dataset Structure A typical data point comprises the path to the audio file, the loaded audio array, and its transcription. Additional fields include datapoint id, duration, language, speaker id, session id, and topic. ``` { 'id': '00644', 'path': '.cache/huggingface/datasets/downloads/extracted/f0b33b5266cd9452ee310eef3577cf7adb7f29aa54dbff74b9a8ee406a55d614/waves/ses2_spk3_L13101_189.900_5.490.wav', 'audio': { 'path': '.cache/huggingface/datasets/downloads/extracted/f0b33b5266cd9452ee310eef3577cf7adb7f29aa54dbff74b9a8ee406a55d614/waves/ses2_spk3_L13101_189.900_5.490.wav', 'array': array([-6.1035156e-05, -1.8310547e-04, 3.0517578e-05, ..., 0.0000000e+00, -3.0517578e-05, 0.0000000e+00 ], dtype = float32), 'sampling_rate': 16000 }, 'transcription': '因为你不可能邀你的female friends去说走我们去play basketball', 'duration': 5.489999771118164, 'language': 'mixed', 'original_speaker_id': 3, 'session_id': 2, 'topic': 'sports' } ``` ### Data Splits Number of utterances: 9,869 train, 1,130 validation, and 1,315 test. ## Additional Information For comprehensive explanations, please check [our paper](https://arxiv.org/pdf/2112.06223.pdf). ### Licensing Information Creative Common Attribution Share-Alike 4.0 International (CC-BY-SA 4.0) ### Citation Information If you use our dataset, please cite us: ``` @inproceedings{lovenia2022ascend, title={ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation}, author={Lovenia, Holy and Cahyawijaya, Samuel and Winata, Genta Indra and Xu, Peng and Yan, Xu and Liu, Zihan and Frieske, Rita and Yu, Tiezheng and Dai, Wenliang and Barezi, Elham J and others}, booktitle={Proceedings of the 13th Language Resources and Evaluation Conference (LREC)}, year={2022} ```

b3x0m/Chinese-H-Novels

b3x0m

2024-07-12T02:32:57Z

1,036

211

[ "task_categories:text-classification", "task_categories:summarization", "task_categories:token-classification", "task_categories:text2text-generation", "task_categories:question-answering", "task_categories:text-generation", "task_categories:fill-mask", "task_categories:sentence-similarity", "language:zh", "size_categories:100M<n<1B", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "art" ]

[ "text-classification", "summarization", "token-classification", "text2text-generation", "question-answering", "text-generation", "fill-mask", "sentence-similarity" ]

2023-11-27T17:19:10Z

null

--- language: - zh size_categories: - 1B<n<10B task_categories: - text-classification - summarization - token-classification - text2text-generation - question-answering - text-generation - fill-mask - sentence-similarity pretty_name: H-novel-corpus tags: - art dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 95784400372 num_examples: 934354429 download_size: 60873072258 dataset_size: 95784400372 configs: - config_name: default data_files: - split: train path: data/train-* --- Update 12/07/2024: convert to parquet to download easier. Chinese 18+ novels corpus, use at your own risk, you and only you are responsible for every choice you make. (͡ ° ͜ʖ ͡ °) tags: socks, garter belt, foot fetish, ntr, netori..... Thanks [Moleys/Numeron](https://huggingface.co/Numeronvr) for the dataset donation.

nyu-visionx/Cambrian-10M

nyu-visionx

2024-07-08T04:34:51Z

9,177

113

[ "task_categories:visual-question-answering", "task_categories:question-answering", "language:en", "license:apache-2.0", "size_categories:1M<n<10M", "arxiv:2406.16860", "region:us" ]

[ "visual-question-answering", "question-answering" ]

2024-05-30T03:27:31Z

null

--- task_categories: - visual-question-answering - question-answering language: - en size_categories: - 1M<n<10M license: apache-2.0 --- # Cambrian-10M Dataset **Please see paper & website for more information:** - https://cambrian-mllm.github.io/ - https://arxiv.org/abs/2406.16860 ## Overview Cambrian-10M is a comprehensive dataset designed for instruction tuning, particularly in multimodal settings involving visual interaction data. The dataset is crafted to address the scarcity of high-quality multimodal instruction-tuning data and to maintain the language abilities of multimodal large language models (LLMs). ## Data Collection ### Multimodal Data Sources Unlike language data, multimodal instruction-tuning data is much rarer and harder to collect. To address this, we leverage existing multimodal benchmarks and datasets involving visual interaction data, such as Visual Question Answering (VQA) and Optical Character Recognition (OCR) data. This approach helps mitigate the catastrophic forgetting commonly observed when fine-tuning multimodal LLMs. ### Language-Only Instruction-Following Data To ensure the preservation of language capabilities, we also collect a small volume of high-quality language-only instruction-following data from the community. ### Targeted Internet Data Collection Engine We introduce a data engine designed to create large-scale, reliable, high-quality knowledge-based multimodal instruction tuning data. The engine works as follows: 1. **Field and Subfield Selection**: The engine selects a target field and subfield, such as “Physics”. 2. **Topic Identification**: An LLM like GPT-4 identifies topics within the field (e.g., “Newton’s Laws”). 3. **Reliable Source Search**: The engine searches reliable sources like Wikipedia for each topic. 4. **Text-Image Association Extraction**: The parser extracts image-caption-text tuples from the sources. 5. **Q&A Pair Generation**: The caption-text is fed to an LLM, such as GPT-3.5, to generate instruction-type Q&A pairs about the image. These Q&A pairs, along with the images, form our VQA dataset. ### GPT Rewriting We also incorporate recent MLLMs such as GPT-4v and GPT-4o to generate extended responses and free-form instruction tuning data. To play with gpt generated data, use [gpt4v_77k](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/resolve/main/jsons/gpt4v_77k.jsonl), Curated [gpt4o_60k](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/resolve/main/jsons/gpt4o_60k.jsonl) - [gpt4v_77k](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/resolve/main/jsons/gpt4v_77k.jsonl) contains more extended responses from Cambrian-10M. - [gpt4o_60k](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/resolve/main/jsons/gpt4o_60k.jsonl) contains more creative data in visual interactions. ## Cambrian-10M Composition The Cambrian-10M dataset consists of approximately 9.784 million data points, offering a diverse range of data for various research applications. The composition of the dataset is visualized in Fig. 9. ## Cambrian-7M We make an initial effort to study data curation. In particular, we find the following data ratio to perform most optimally - **Language**: 21.00% - **General**: 34.52% - **OCR**: 27.22% - **Counting**: 8.71% - **Math**: 7.20% - **Code**: 0.87% - **Science**: 0.88% ![Cambrian-7M](cambrian7m.png) ## Getting Started with Cambrian Data Before you start, ensure you have sufficient storage space to download and process the data. Cambrian-10M contains a total of 10 million images collected from previous datasets, an internet data engine, and GPT-generated instruction tuning data. Follow these steps to get started: 1. **Download the Data Repository** Download the data repository. Note that due to Hugging Face policy constraints, the data folder is archived into tar files. We also split the `allava` and `data_engine` data into smaller tar files because they exceed the 50 GB size limit. 2. **Merge Tar Files** To explore the Cambrian-10M dataset, first merge the different parts of `allava` and `data_engine` together: ```bash python merge_tars.py ``` 3. **Extract Tar Files** Then, extract all the tar files into the current directory: ```bash python extract.py ``` 4. **Training with Cambrian** You can train with the raw [Cambrian10M](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/resolve/main/jsons/Cambrian10M.jsonl), Curated [Cambrian7M](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/resolve/main/jsons/Cambrian7M.jsonl). We recommend using the Curated [Cambrian7M with system prompt](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/blob/main/jsons/Cambrian7M_withsystemprompt.jsonl) that also alleviates 'answer machine' problem.

SetFit/bbc-news

SetFit

2024-07-04T13:00:48Z

886

16

[ "task_categories:text-classification", "language:en", "size_categories:1K<n<10K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[ "text-classification" ]

2022-03-02T23:29:22Z

1

--- task_categories: - text-classification language: - en pretty_name: BBC News Topic Dataset --- # BBC News Topic Dataset Dataset on [BBC News Topic Classification](https://www.kaggle.com/yufengdev/bbc-text-categorization/data) consisting of 2,225 articles published on the BBC News website corresponding during 2004-2005. Each article is labeled under one of 5 categories: business, entertainment, politics, sport or tech. Original source for this dataset: - Derek Greene, Pádraig Cunningham, “Practical Solutions to the Problem of Diagonal Dominance in Kernel Document Clustering,” in Proc. 23rd International Conference on Machine learning (ICML’06), 2006 [Raw dataset](http://mlg.ucd.ie/datasets/bbc.html) - [Paper PDF](http://derekgreene.com/papers/greene06icml.pdf) All rights, including copyright, in the content of the original articles are owned by the BBC.

lmms-lab/Video-MME

lmms-lab

2024-07-04T08:14:20Z

17,892

40

[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "modality:video", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-06-07T12:06:37Z

null

--- dataset_info: config_name: videomme features: - name: video_id dtype: string - name: duration dtype: string - name: domain dtype: string - name: sub_category dtype: string - name: url dtype: string - name: videoID dtype: string - name: question_id dtype: string - name: task_type dtype: string - name: question dtype: string - name: options sequence: string - name: answer dtype: string splits: - name: test num_bytes: 1003241.0 num_examples: 2700 download_size: 405167 dataset_size: 1003241.0 configs: - config_name: videomme data_files: - split: test path: videomme/test-* ---

agentsea/wave-ui-25k

agentsea

2024-07-03T04:13:40Z

1,118

32

[ "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-06-28T18:38:08Z

2

--- dataset_info: features: - name: image dtype: image - name: instruction dtype: string - name: bbox sequence: float64 - name: resolution sequence: int64 - name: source dtype: string - name: platform dtype: string - name: name dtype: string - name: description dtype: string - name: type dtype: string - name: OCR dtype: string - name: language dtype: string - name: purpose dtype: string - name: expectation dtype: string splits: - name: train num_bytes: 10884587492.75 num_examples: 24978 download_size: 10577935056 dataset_size: 10884587492.75 configs: - config_name: default data_files: - split: train path: data/train-* --- # WaveUI-25k This dataset contains 25k examples of labeled UI elements. It is a subset of a collection of ~80k preprocessed examples assembled from the following sources: - [WebUI](https://uimodeling.github.io/) - [RoboFlow](https://universe.roboflow.com/roboflow-gw7yv/website-screenshots) - [GroundUI-18K](https://huggingface.co/datasets/agent-studio/GroundUI-18K) These datasets were preprocessed to have matching schemas and to filter out unwanted examples, such as duplicated, overlapping and low-quality datapoints. We also filtered out many text elements which were not in the main scope of this work. The WaveUI-25k dataset includes the original fields from the source datasets, as well as the following additional fields, obtained from the annotation process: - `name`: A descriptive name of the element. - `description`: A long detailed description of the element - `type`: The type of the element. - `OCR`: OCR of the element. Set to `null` if no text is available. - `language`: The language of the OCR text, if available. Set to `null` if no text is available. - `purpose`: A general purpose of the element. - `expectation`: An expectation on what will happen when you click this element. The dataset can be visualized better in [this space](https://huggingface.co/spaces/agentsea/wave-ui-viz). ## Data sources - [WebUI](https://uimodeling.github.io/) ```bibtex @article{wu2023webui, title={WebUI: A Dataset for Enhancing Visual UI Understanding with Web Semantics}, author={Jason Wu and Siyan Wang and Siman Shen and Yi-Hao Peng and Jeffrey Nichols and Jeffrey Bigham}, journal={ACM Conference on Human Factors in Computing Systems (CHI)}, year={2023} } ``` - [RoboFlow](https://universe.roboflow.com/roboflow-gw7yv/website-screenshots) ```bibtex @misc{ website-screenshots_dataset, title = { Website Screenshots Dataset }, type = { Open Source Dataset }, author = { Brad Dwyer }, howpublished = { \url{ https://universe.roboflow.com/roboflow-gw7yv/website-screenshots } }, url = { https://universe.roboflow.com/roboflow-gw7yv/website-screenshots }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { aug }, note = { visited on 2024-07-02 }, } ``` - [GroundUI-18K](https://huggingface.co/datasets/agent-studio/GroundUI-18K) ```bibtex @article{zheng2024agentstudio, title={AgentStudio: A Toolkit for Building General Virtual Agents}, author={Longtao Zheng and Zhiyuan Huang and Zhenghai Xue and Xinrun Wang and Bo An and Shuicheng Yan}, journal={arXiv preprint arXiv:2403.17918}, year={2024} } ```

QuietImpostor/Claude-3-Opus-Claude-3.5-Sonnnet-9k

QuietImpostor

2024-06-30T20:43:11Z

90

22

[ "task_categories:text-generation", "size_categories:1K<n<10K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[ "text-generation" ]

2024-06-30T20:39:49Z

2

--- task_categories: - text-generation pretty_name: Claude 3 Opus + Claude 3.5 Sonnet Ultrachat ShareGPT size_categories: - 1K<n<10K --- # Overview This dataset is a combination of samples from Sao10k's original Claude 3 Opus dataset and a personally created Claude 3.5 Sonnet dataset. Due to budget constraints, approximately 700 samples are from Claude 3.5 Sonnet, with the remainder sourced from the Claude 3 Opus dataset.

nnenufar/speakerVerification_PTBR

nnenufar

2024-06-29T14:20:34Z

41,704

0

[ "task_categories:audio-classification", "language:pt", "license:apache-2.0", "size_categories:10K<n<100K", "modality:audio", "region:us" ]

[ "audio-classification" ]

2024-06-27T01:07:59Z

null

--- license: apache-2.0 task_categories: - audio-classification language: - pt size_categories: - 10K<n<100K --- # Dataset card  This dataset includes ~80k samples of speech audio in Brazilian Portuguese. Samples have variable length ranging from 1 to 4 seconds, with a sampling rate of 16kHz. The metadata file includes speaker tags and corresponding labels for each sample, making it appropriate for speaker identification and speaker verification tasks. ## Dataset Description Audio samples are taken from three bigger corpora: C-ORAL Brasil, NURC Recife and NURC SP. Please take into consideration the licenses details of the original corpora. All the recordings come from monologues, but there are brief moments of interruption by the researchers that conducted the recordings. The recordings comprise accents from three different states of Brazil: Belo Horizonte, São Paulo and Recife. The dataset is structured in a way that makes it possible to load with HF Audiofolder. In my experience, I noticed that it is easier to clone the dataset repo to your machine and then load it specifying the local data_dir than to load it directly from the hub using the dataset tag. ## Dataset Sources  - **C-ORAL BRASIL:** [https://www.c-oral-brasil.org/c-oral-brasil-i.php] - **NURC Recife:** [https://fale.ufal.br/projeto/nurcdigital/index.php?action=home] - **NURC SP:** [http://tarsila.icmc.usp.br:8080/nurc/home] ## Out-of-Scope Use  The models trained with this dataset should not be used to intentionally create hostile or alienating environments for people.

ncbi/MedCalc-Bench-v1.0

ncbi

2024-06-26T20:51:59Z

501

13

[ "license:cc-by-sa-4.0", "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-06-12T12:44:02Z

2

--- license: cc-by-sa-4.0 dataset_info: features: - name: Row Number dtype: int64 - name: Calculator ID dtype: int64 - name: Calculator Name dtype: string - name: Category dtype: string - name: Output Type dtype: string - name: Note ID dtype: string - name: Note Type dtype: string - name: Patient Note dtype: string - name: Question dtype: string - name: Relevant Entities dtype: string - name: Ground Truth Answer dtype: string - name: Lower Limit dtype: string - name: Upper Limit dtype: string - name: Ground Truth Explanation dtype: string splits: - name: train num_bytes: 41265322 num_examples: 10053 - name: test num_bytes: 4043748 num_examples: 1047 download_size: 19670625 dataset_size: 45309070 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- MedCalc-Bench is the first medical calculation dataset used to benchmark LLMs ability to serve as clinical calculators. Each instance in the dataset consists of a patient note, a question asking to compute a specific clinical value, an final answer value, and a step-by-step solution explaining how the final answer was obtained. Our dataset covers 55 different calculation tasks. We hope this dataset serves as a call to improve the verbal and computational reasoning skills of LLMs in medical settings. This dataset contains a training dataset of 10,053 instances and a testing dataset of 1,047 instances. ## Contents inside the Training and Testing CSV To download the CSV for the MedCalc-Bench evaluation dataset, please download the file, test_data.csv inside the dataset folder of this repository. You can also download the test set split from HuggingFace at https://huggingface.co/datasets/ncbi/MedCalc-Bench. In addition to the 1,047 evaluation instances, we also provide a training dataset of 10,053 instances which can be used for fine-tuning open-source LLMs (see Section C of the Appendix). Each Instance in the dataset contains the following information: - Row Number: Specifies the index of the instance. - Calculator ID: Specifies the integer ID of the calculator. - Calculator Name: Specifies the name of the clinical calculation task. - Category: Specifies the sub-category of the calculator. For equation-based calculators, the options are lab test, dosage, date, or physical and for rule-based calculators, the options are risk, severity, and diagnosis. - Output Type: Specifies the format type that the calculator will return. The options are decimal, integer, date (MM/DD/YY), or time in terms of weeks and days (i.e. (17 weeks, 4 days)). - Note ID: Specifies the ID of the patient note. The ID of the note will either be the ID given by Open-Patients or it will be an integer value if the patient note was handwritten by clinicians or synthesized by a template. - Note Type: Specifies whether the patient note was synthesized by a clinician (Handwritten), produced from a template (Template), or was extracted from PMC-Patients (extracted). - Patient Note: Specifies the patient note which provides the information needed to compute the final answer. - Question: Specifies the question that is asked to the model to compute a specific medical value based on a particular calculator. - Relevant Entities: Provides a dictionary of the parameters and their extracted values based on the patient note. - Ground Truth Answer: Specifies the ground truth value without any units for the medical value that needs to be calculated. - Lower Limit: For equation-based calculators whose output is a decimal, this value is 95% of the ground truth answer value. For all other cases, the lower limit is the same as the ground-truth value. - Upper Limit: For equation-based calculators whose output is a decimal, this value is 105% of the ground truth answer value. For all other cases, the upper limit is the same as the ground-truth value. - Ground Truth Explanation: The ground truth explanation for the data instance providing a step-by-step explanation for how the final answer was obtained. ## How to Use MedCalc-Bench The training dataset of MedCalc-Bench can be used for fine-tunining LLMs. We have provided both the fine-tuned models and code for fine-tuning at our repository link: https://github.com/ncbi-nlp/MedCalc-Bench. The test set of MedCalc-Bench is helpful for benchamrking LLMs under different settings. We provide instructions in the README of this repository for how to reproduce all of our results for all of the models using the different prompt settings. By experimenting with different LLMs and prompts, we hope our dataset demonstrates the potential and limitations of LLMs in clinical settings. ## License Both the training and testing dataset of MedCalc-Bench are released under the CC-BY-SA 4.0 license.

PleIAs/YouTube-Commons

PleIAs

2024-06-26T08:08:14Z

763

346

[ "task_categories:text-generation", "language:en", "language:fr", "language:es", "language:pt", "language:de", "language:ru", "license:cc-by-4.0", "region:us", "conversational" ]

[ "text-generation" ]

2024-04-15T22:12:27Z

null

--- language: - en - fr - es - pt - de - ru license: cc-by-4.0 task_categories: - text-generation pretty_name: Youtube Commons Corpus tags: - conversational dataset_info: features: - name: video_id dtype: string - name: video_link dtype: string - name: title dtype: string - name: text dtype: string - name: channel dtype: string - name: channel_id dtype: string - name: date dtype: string - name: license dtype: string - name: original_language dtype: string - name: source_language dtype: string - name: transcription_language dtype: string - name: word_count dtype: int64 - name: character_count dtype: int64 splits: - name: train num_bytes: 3284822536 num_examples: 250000 download_size: 1830819739 dataset_size: 3284822536 configs: - config_name: default data_files: - split: train path: data/train-* --- # 📺 YouTube-Commons 📺 **YouTube-Commons** is a collection of audio transcripts of 2,063,066 videos shared on YouTube under a CC-By license. ## Content The collection comprises 22,709,724 original and automatically translated transcripts from 3,156,703 videos (721,136 individual channels). In total, this represents nearly 45 billion words (44,811,518,375). All the videos where shared on YouTube with a CC-BY license: the dataset provide all the necessary provenance information including the title, link, channel name and upload date. The corpus is multilingual with a majority of English-speaking content (71%) for original languages. Automated translations are provided for nearly all the videos in English, French, Spanish, German, Russian, Italian and Dutch. ## Uses The collection aims to expand the availability of conversational data for research in AI, computational social science and digital humanities. Most of the available resources under free licenses are written texts such as public domain works or open science articles. The text can be used for training model and republished with for reproducibility purposes. ## License and ethics All the transcripts are part of a video shared under a CC-By license. In accordance with the provision of the license, every YouTube channels is fully credited. While content under a free license can be lawfully reproduced in any setting, there is currently a debate over the legitimacy and proper ethical use of free content for pre-training large language models. In accordance with the philosophy of Creative Commons, we recommend that this set be preferably used for open research. Furthermore, the license requires that contribution of each individual author is properly credited. In a research context, the best way to achieve this aim would be to fully release the data sources used for training or, at the very least, provide an extensive open documentation. ## Future developments The collection is far from covering the total amount of available YouTube videos under a Creative Commons license. We will continue to expand it significantly. Other additional release will also focus on transcripts from other video sources not available on YouTube (especially from public service/university websites). ## Acknowledgements The corpus was stored and processed with the generous support of Scaleway. It was built up with the support and concerted efforts of the state start-up LANGU:IA (start-up d’Etat), supported by the French Ministry of Culture and DINUM, as part of the prefiguration of the service offering of the Alliance for Language technologies EDIC (ALT-EDIC). Pleias corpus collection projects have been also facilitated thanks to the open science LLM community support, insights and cooperation (Occiglot, Eleuther AI, Allen AI). <div style="text-align: center;"> <img src="https://github.com/mch-dd/datasetlogo/blob/main/scaleway.jpeg?raw=true" style="width: 33%; margin: 0 auto; display: inline-block;"/> <img src="https://github.com/mch-dd/datasetlogo/blob/main/ministere.png?raw=true" style="width: 33%; margin: 0 auto; display: inline-block;"/> <img src="https://github.com/mch-dd/datasetlogo/blob/main/occiglot.jpg?raw=true" style="width: 33%; margin: 0 auto; display: inline-block;"/> </div>

community-datasets/urdu_fake_news

community-datasets

2024-06-26T07:46:11Z

114

2

[ "task_categories:text-classification", "task_ids:fact-checking", "task_ids:intent-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "source_datasets:original", "language:ur", "license:unknown", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[ "text-classification" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - ur license: - unknown multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - text-classification task_ids: - fact-checking - intent-classification pretty_name: Bend the Truth (Urdu Fake News) dataset_info: features: - name: news dtype: string - name: label dtype: class_label: names: '0': Fake '1': Real - name: category dtype: class_label: names: '0': bus '1': hlth '2': sp '3': tch '4': sbz splits: - name: train num_bytes: 1762901 num_examples: 638 - name: test num_bytes: 799583 num_examples: 262 download_size: 1187921 dataset_size: 2562484 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for Bend the Truth (Urdu Fake News) ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Github](https://github.com/MaazAmjad/Datasets-for-Urdu-news/) - **Repository:** [Github](https://github.com/MaazAmjad/Datasets-for-Urdu-news/) - **Paper:** - **Leaderboard:** - **Point of Contact:** [Maaz Amjad](https://github.com/MaazAmjad) ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - news: a string in urdu - label: the label indicating whethere the provided news is real or fake. - category: The intent of the news being presented. The available 5 classes are Sports, Health, Technology, Entertainment, and Business. ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@chaitnayabasava](https://github.com/chaitnayabasava) for adding this dataset.

community-datasets/tamilmixsentiment

community-datasets

2024-06-26T07:04:23Z

94

4

[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:multilingual", "source_datasets:original", "language:en", "language:ta", "license:unknown", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[ "text-classification" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - en - ta license: - unknown multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: Tamilmixsentiment dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': Positive '1': Negative '2': Mixed_feelings '3': unknown_state '4': not-Tamil splits: - name: train num_bytes: 790124 num_examples: 11335 - name: validation num_bytes: 89614 num_examples: 1260 - name: test num_bytes: 218760 num_examples: 3149 download_size: 708889 dataset_size: 1098498 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # Dataset Card for Tamilmixsentiment ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Tamilmixsentiment Homepage](https://dravidian-codemix.github.io/2020/index.html) - **Repository:** [Tamilmixsentiment repository](https://dravidian-codemix.github.io/2020/datasets.html) - **Paper:** [Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text](https://www.aclweb.org/anthology/2020.sltu-1.28/) - **Leaderboard:** [Rank list](https://drive.google.com/file/d/1Mf8-No-63koGRwdF13RrO01NAFBlNmI0/view?usp=sharing) - **Point of Contact:** [Bharathi Raja Chakravarthi](mailto:[email protected]) ### Dataset Summary The first gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. This makes the largest general domain sentiment dataset for this relatively low-resource language with code-mixing phenomenon. The comment/post may contain more than one sentence but the average sentence length of the corpora is 1. Each comment/post is annotated with sentiment polarity at the comment/post level. This dataset also has class imbalance problems depicting real-world scenarios. ### Supported Tasks and Leaderboards To identify sentiment polarity of the code-mixed dataset of comments/posts in Tamil-English collected from social media. ### Languages Tamil-English code-switched. The dataset contains all the three types of code-mixed sentences - Inter-Sentential switch, Intra-Sentential switch and Tag switching. Most comments were written in Roman script with either Tamil grammar with English lexicon or English grammar with Tamil lexicon. Some comments were written in Tamil script with English expressions in between. ## Dataset Structure ### Data Instances An example from the Tamilmixsentiment train set looks as follows: ``` text label Trailer late ah parthavanga like podunga Positive ``` ### Data Fields - `text`: Tamil-English code-mixed comment. - `label`: list of the possible sentiments "Positive", "Negative", "Mixed_feelings", "unknown_state", "not-Tamil" ### Data Splits The entire dataset of 15,744 sentences was randomly shuffled and split into three parts as follows: | | train | validation | test | |------------------------------|------:|-----------:|-----:| | Tamilmixsentiment | 11335 | 1260 | 3149 | ## Dataset Creation ### Curation Rationale Sentiment analysis has become important in social media research (Yang and Eisenstein, 2017). Until recently these applications were created for high-resourced languages which analysed monolingual utterances. But social media in multilingual communities contains more code-mixed text. Code-mixing is common among speakers in a bilingual speech community. As English is seen as the language of prestige and education, the influence of lexicon, connectives and phrases from English language is common in spoken Tamil. Tamil has little annotated data for code-mixed scenarios. An annotated corpus developed for monolingual data cannot deal with code-mixed usage and therefore it fails to yield good results due to mixture of languages at different levels of linguistic analysis. Therefore this dataset of code-mixed Tamil-English sentiment annotated corpus is created. ### Source Data #### Initial Data Collection and Normalization The data was scraped from Youtube. In total 184,573 sentences for Tamil from YouTube comments from the trailers of a movies released in 2019. Many of the them contained sentences that were either entirely written in English or code-mixed Tamil-English or fully written in Tamil. So we filtered out a non-code-mixed corpus based on language identification at comment level using the langdetect library. The comment is written fully in Tamil or English, we discarded that comment since monolingual resources are available for these languages. We also identified if the sentences were written in other languages such as Hindi, Malayalam, Urdu, Telugu, and Kannada. We preprocessed the comments by removing the emoticons and applying a sentence length filter. We want to create a code-mixed corpus of reasonable size with sentences that have fairly defined sentiments which will be useful for future research. Thus our filter removed sentences with less than five words and more than 15 words after cleaning the data. In the end we got 15,744 Tanglish sentences. #### Who are the source language producers? Youtube users ### Annotations #### Annotation process Three steps complete the annotation setup. First, each sentence was annotated by two people. In the second step, the data were collected if both of them agreed. In the case of conflict, a third person annotated the sentence. In the third step, if all the three of them did not agree, then two more annotators annotated the sentences. #### Who are the annotators? Eleven volunteers were involved in the process. All of them were native speakers of Tamil with diversity in gender, educational level and medium of instruction in their school education. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{chakravarthi-etal-2020-corpus, title = "Corpus Creation for Sentiment Analysis in Code-Mixed {T}amil-{E}nglish Text", author = "Chakravarthi, Bharathi Raja and Muralidaran, Vigneshwaran and Priyadharshini, Ruba and McCrae, John Philip", booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources association", url = "https://www.aclweb.org/anthology/2020.sltu-1.28", pages = "202--210", abstract = "Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark.", language = "English", ISBN = "979-10-95546-35-1", } ``` ### Contributions Thanks to [@jamespaultg](https://github.com/jamespaultg) for adding this dataset.

community-datasets/senti_ws

community-datasets

2024-06-26T06:37:47Z

105

2

[ "task_categories:token-classification", "task_categories:text-classification", "task_ids:text-scoring", "task_ids:sentiment-scoring", "task_ids:part-of-speech", "annotations_creators:expert-generated", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:de", "license:cc-by-sa-3.0", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[ "token-classification", "text-classification" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - expert-generated - machine-generated language_creators: - found language: - de license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification - text-classification task_ids: - text-scoring - sentiment-scoring - part-of-speech pretty_name: SentiWS dataset_info: - config_name: pos-tagging features: - name: word dtype: string - name: pos-tag dtype: class_label: names: '0': NN '1': VVINF '2': ADJX '3': ADV splits: - name: train num_bytes: 75526 num_examples: 3471 download_size: 37314 dataset_size: 75526 - config_name: sentiment-scoring features: - name: word dtype: string - name: sentiment-score dtype: float32 splits: - name: train num_bytes: 61642 num_examples: 3471 download_size: 45116 dataset_size: 61642 configs: - config_name: pos-tagging data_files: - split: train path: pos-tagging/train-* default: true - config_name: sentiment-scoring data_files: - split: train path: sentiment-scoring/train-* --- # Dataset Card for SentiWS ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://wortschatz.uni-leipzig.de/en/download - **Repository:** [Needs More Information] - **Paper:** http://www.lrec-conf.org/proceedings/lrec2010/pdf/490_Paper.pdf - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary SentimentWortschatz, or SentiWS for short, is a publicly available German-language resource for sentiment analysis, opinion mining etc. It lists positive and negative polarity bearing words weighted within the interval of [-1; 1] plus their part of speech tag, and if applicable, their inflections. The current version of SentiWS contains around 1,650 positive and 1,800 negative words, which sum up to around 16,000 positive and 18,000 negative word forms incl. their inflections, respectively. It not only contains adjectives and adverbs explicitly expressing a sentiment, but also nouns and verbs implicitly containing one. ### Supported Tasks and Leaderboards Sentiment-Scoring, Pos-Tagging ### Languages German ## Dataset Structure ### Data Instances For pos-tagging: ``` { "word":"Abbau" "pos_tag": 0 } ``` For sentiment-scoring: ``` { "word":"Abbau" "sentiment-score":-0.058 } ``` ### Data Fields SentiWS is UTF8-encoded text. For pos-tagging: - word: one word as a string, - pos_tag: the part-of-speech tag of the word as an integer, For sentiment-scoring: - word: one word as a string, - sentiment-score: the sentiment score of the word as a float between -1 and 1, The POS tags are ["NN", "VVINF", "ADJX", "ADV"] -> ["noun", "verb", "adjective", "adverb"], and positive and negative polarity bearing words are weighted within the interval of [-1, 1]. ### Data Splits train: 1,650 negative and 1,818 positive words ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License ### Citation Information @INPROCEEDINGS{remquahey2010, title = {SentiWS -- a Publicly Available German-language Resource for Sentiment Analysis}, booktitle = {Proceedings of the 7th International Language Resources and Evaluation (LREC'10)}, author = {Remus, R. and Quasthoff, U. and Heyer, G.}, year = {2010} } ### Contributions Thanks to [@harshalmittal4](https://github.com/harshalmittal4) for adding this dataset.

community-datasets/qanta

community-datasets

2024-06-26T06:06:55Z

50,006

4

[ "task_categories:question-answering", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:unknown", "size_categories:1M<n<10M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:1904.04792", "region:us", "quizbowl" ]

[ "question-answering" ]

2022-03-02T23:29:22Z

null

--- annotations_creators: - machine-generated language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - question-answering task_ids: [] paperswithcode_id: quizbowl pretty_name: Quizbowl tags: - quizbowl dataset_info: - config_name: mode=first,char_skip=25 features: - name: id dtype: string - name: qanta_id dtype: int32 - name: proto_id dtype: string - name: qdb_id dtype: int32 - name: dataset dtype: string - name: text dtype: string - name: full_question dtype: string - name: first_sentence dtype: string - name: char_idx dtype: int32 - name: sentence_idx dtype: int32 - name: tokenizations sequence: sequence: int32 length: 2 - name: answer dtype: string - name: page dtype: string - name: raw_answer dtype: string - name: fold dtype: string - name: gameplay dtype: bool - name: category dtype: string - name: subcategory dtype: string - name: tournament dtype: string - name: difficulty dtype: string - name: year dtype: int32 splits: - name: guesstrain num_bytes: 117599150 num_examples: 96221 - name: buzztrain num_bytes: 19699616 num_examples: 16706 - name: guessdev num_bytes: 1414822 num_examples: 1055 - name: buzzdev num_bytes: 1553576 num_examples: 1161 - name: guesstest num_bytes: 2997063 num_examples: 2151 - name: buzztest num_bytes: 2653365 num_examples: 1953 - name: adversarial num_bytes: 1258784 num_examples: 1145 download_size: 90840024 dataset_size: 147176376 - config_name: mode=full,char_skip=25 features: - name: id dtype: string - name: qanta_id dtype: int32 - name: proto_id dtype: string - name: qdb_id dtype: int32 - name: dataset dtype: string - name: text dtype: string - name: full_question dtype: string - name: first_sentence dtype: string - name: char_idx dtype: int32 - name: sentence_idx dtype: int32 - name: tokenizations sequence: sequence: int32 length: 2 - name: answer dtype: string - name: page dtype: string - name: raw_answer dtype: string - name: fold dtype: string - name: gameplay dtype: bool - name: category dtype: string - name: subcategory dtype: string - name: tournament dtype: string - name: difficulty dtype: string - name: year dtype: int32 splits: - name: guesstrain num_bytes: 168874612 num_examples: 96221 - name: buzztrain num_bytes: 27989445 num_examples: 16706 - name: guessdev num_bytes: 2098857 num_examples: 1055 - name: buzzdev num_bytes: 2301145 num_examples: 1161 - name: guesstest num_bytes: 4434626 num_examples: 2151 - name: buzztest num_bytes: 3930150 num_examples: 1953 - name: adversarial num_bytes: 1799969 num_examples: 1145 download_size: 133005755 dataset_size: 211428804 - config_name: mode=runs,char_skip=25 features: - name: id dtype: string - name: qanta_id dtype: int32 - name: proto_id dtype: string - name: qdb_id dtype: int32 - name: dataset dtype: string - name: text dtype: string - name: full_question dtype: string - name: first_sentence dtype: string - name: char_idx dtype: int32 - name: sentence_idx dtype: int32 - name: tokenizations sequence: sequence: int32 length: 2 - name: answer dtype: string - name: page dtype: string - name: raw_answer dtype: string - name: fold dtype: string - name: gameplay dtype: bool - name: category dtype: string - name: subcategory dtype: string - name: tournament dtype: string - name: difficulty dtype: string - name: year dtype: int32 splits: - name: guesstrain num_bytes: 3975570298 num_examples: 2641161 - name: buzztrain num_bytes: 622976884 num_examples: 433552 - name: guessdev num_bytes: 55281178 num_examples: 33602 - name: buzzdev num_bytes: 60226416 num_examples: 36803 - name: guesstest num_bytes: 120192213 num_examples: 70772 - name: buzztest num_bytes: 104422131 num_examples: 63050 - name: adversarial num_bytes: 37874827 num_examples: 27986 download_size: 306157359 dataset_size: 4976543947 - config_name: mode=sentences,char_skip=25 features: - name: id dtype: string - name: qanta_id dtype: int32 - name: proto_id dtype: string - name: qdb_id dtype: int32 - name: dataset dtype: string - name: text dtype: string - name: full_question dtype: string - name: first_sentence dtype: string - name: char_idx dtype: int32 - name: sentence_idx dtype: int32 - name: tokenizations sequence: sequence: int32 length: 2 - name: answer dtype: string - name: page dtype: string - name: raw_answer dtype: string - name: fold dtype: string - name: gameplay dtype: bool - name: category dtype: string - name: subcategory dtype: string - name: tournament dtype: string - name: difficulty dtype: string - name: year dtype: int32 splits: - name: guesstrain num_bytes: 629450237 num_examples: 505321 - name: buzztrain num_bytes: 98941633 num_examples: 82574 - name: guessdev num_bytes: 9112676 num_examples: 6818 - name: buzzdev num_bytes: 9924887 num_examples: 7451 - name: guesstest num_bytes: 19470155 num_examples: 14069 - name: buzztest num_bytes: 17011859 num_examples: 12610 - name: adversarial num_bytes: 6491504 num_examples: 5812 download_size: 150604036 dataset_size: 790402951 configs: - config_name: mode=first,char_skip=25 data_files: - split: guesstrain path: mode=first,char_skip=25/guesstrain-* - split: buzztrain path: mode=first,char_skip=25/buzztrain-* - split: guessdev path: mode=first,char_skip=25/guessdev-* - split: buzzdev path: mode=first,char_skip=25/buzzdev-* - split: guesstest path: mode=first,char_skip=25/guesstest-* - split: buzztest path: mode=first,char_skip=25/buzztest-* - split: adversarial path: mode=first,char_skip=25/adversarial-* - config_name: mode=full,char_skip=25 data_files: - split: guesstrain path: mode=full,char_skip=25/guesstrain-* - split: buzztrain path: mode=full,char_skip=25/buzztrain-* - split: guessdev path: mode=full,char_skip=25/guessdev-* - split: buzzdev path: mode=full,char_skip=25/buzzdev-* - split: guesstest path: mode=full,char_skip=25/guesstest-* - split: buzztest path: mode=full,char_skip=25/buzztest-* - split: adversarial path: mode=full,char_skip=25/adversarial-* - config_name: mode=runs,char_skip=25 data_files: - split: guesstrain path: mode=runs,char_skip=25/guesstrain-* - split: buzztrain path: mode=runs,char_skip=25/buzztrain-* - split: guessdev path: mode=runs,char_skip=25/guessdev-* - split: buzzdev path: mode=runs,char_skip=25/buzzdev-* - split: guesstest path: mode=runs,char_skip=25/guesstest-* - split: buzztest path: mode=runs,char_skip=25/buzztest-* - split: adversarial path: mode=runs,char_skip=25/adversarial-* - config_name: mode=sentences,char_skip=25 data_files: - split: guesstrain path: mode=sentences,char_skip=25/guesstrain-* - split: buzztrain path: mode=sentences,char_skip=25/buzztrain-* - split: guessdev path: mode=sentences,char_skip=25/guessdev-* - split: buzzdev path: mode=sentences,char_skip=25/buzzdev-* - split: guesstest path: mode=sentences,char_skip=25/guesstest-* - split: buzztest path: mode=sentences,char_skip=25/buzztest-* - split: adversarial path: mode=sentences,char_skip=25/adversarial-* --- # Dataset Card for "qanta" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [http://www.qanta.org/](http://www.qanta.org/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [Quizbowl: The Case for Incremental Question Answering](https://arxiv.org/abs/1904.04792) - **Point of Contact:** [Jordan Boyd-Graber](mailto:[email protected]) - **Size of downloaded dataset files:** 170.75 MB - **Size of the generated dataset:** 147.18 MB - **Total amount of disk used:** 317.93 MB ### Dataset Summary The Qanta dataset is a question answering dataset based on the academic trivia game Quizbowl. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### mode=first,char_skip=25 - **Size of downloaded dataset files:** 170.75 MB - **Size of the generated dataset:** 147.18 MB - **Total amount of disk used:** 317.93 MB An example of 'guessdev' looks as follows. ``` This example was too long and was cropped: { "answer": "Apollo_program", "category": "History", "char_idx": -1, "dataset": "quizdb.org", "difficulty": "easy_college", "first_sentence": "As part of this program, William Anders took a photo that Galen Rowell called \"the most influential environmental photograph ever taken.\"", "fold": "guessdev", "full_question": "\"As part of this program, William Anders took a photo that Galen Rowell called \\\"the most influential environmental photograph e...", "gameplay": false, "id": "127028-first", "page": "Apollo_program", "proto_id": "", "qanta_id": 127028, "qdb_id": 126689, "raw_answer": "Apollo program [or Project Apollo; accept Apollo 8; accept Apollo 1; accept Apollo 11; prompt on landing on the moon]", "sentence_idx": -1, "subcategory": "American", "text": "As part of this program, William Anders took a photo that Galen Rowell called \"the most influential environmental photograph ever taken.\"", "tokenizations": [[0, 137], [138, 281], [282, 412], [413, 592], [593, 675]], "tournament": "ACF Fall", "year": 2016 } ``` ### Data Fields The data fields are the same among all splits. #### mode=first,char_skip=25 - `id`: a `string` feature. - `qanta_id`: a `int32` feature. - `proto_id`: a `string` feature. - `qdb_id`: a `int32` feature. - `dataset`: a `string` feature. - `text`: a `string` feature. - `full_question`: a `string` feature. - `first_sentence`: a `string` feature. - `char_idx`: a `int32` feature. - `sentence_idx`: a `int32` feature. - `tokenizations`: a dictionary feature containing: - `feature`: a `int32` feature. - `answer`: a `string` feature. - `page`: a `string` feature. - `raw_answer`: a `string` feature. - `fold`: a `string` feature. - `gameplay`: a `bool` feature. - `category`: a `string` feature. - `subcategory`: a `string` feature. - `tournament`: a `string` feature. - `difficulty`: a `string` feature. - `year`: a `int32` feature. ### Data Splits | name |adversarial|buzzdev|buzztrain|guessdev|guesstrain|buzztest|guesstest| |-----------------------|----------:|------:|--------:|-------:|---------:|-------:|--------:| |mode=first,char_skip=25| 1145| 1161| 16706| 1055| 96221| 1953| 2151| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{Rodriguez2019QuizbowlTC, title={Quizbowl: The Case for Incremental Question Answering}, author={Pedro Rodriguez and Shi Feng and Mohit Iyyer and He He and Jordan L. Boyd-Graber}, journal={ArXiv}, year={2019}, volume={abs/1904.04792} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.

community-datasets/qa4mre

community-datasets

2024-06-26T05:31:09Z

1,204

4

[ "task_categories:multiple-choice", "task_ids:multiple-choice-qa", "annotations_creators:other", "language_creators:found", "multilinguality:multilingual", "source_datasets:original", "language:ar", "language:bg", "language:de", "language:en", "language:es", "language:it", "language:ro", "license:unknown", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[ "multiple-choice" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - other language_creators: - found language: - ar - bg - de - en - es - it - ro license: - unknown multilinguality: - multilingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - multiple-choice task_ids: - multiple-choice-qa pretty_name: 'QA4MRE: Question Answering for Machine Reading Evaluation' dataset_info: - config_name: 2011.main.DE features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1747066 num_examples: 120 download_size: 128323 dataset_size: 1747066 - config_name: 2011.main.EN features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1569624 num_examples: 120 download_size: 115284 dataset_size: 1569624 - config_name: 2011.main.ES features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1694408 num_examples: 120 download_size: 125016 dataset_size: 1694408 - config_name: 2011.main.IT features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1667136 num_examples: 120 download_size: 125939 dataset_size: 1667136 - config_name: 2011.main.RO features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1740367 num_examples: 120 download_size: 129859 dataset_size: 1740367 - config_name: 2012.alzheimers.EN features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1637936 num_examples: 40 download_size: 96156 dataset_size: 1637936 - config_name: 2012.main.AR features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2710604 num_examples: 160 download_size: 168781 dataset_size: 2710604 - config_name: 2012.main.BG features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 3454163 num_examples: 160 download_size: 203734 dataset_size: 3454163 - config_name: 2012.main.DE features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2087414 num_examples: 160 download_size: 158289 dataset_size: 2087414 - config_name: 2012.main.EN features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1757534 num_examples: 160 download_size: 137169 dataset_size: 1757534 - config_name: 2012.main.ES features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2057350 num_examples: 160 download_size: 152867 dataset_size: 2057350 - config_name: 2012.main.IT features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2071658 num_examples: 160 download_size: 156976 dataset_size: 2071658 - config_name: 2012.main.RO features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2074878 num_examples: 160 download_size: 157140 dataset_size: 2074878 - config_name: 2013.alzheimers.EN features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2614760 num_examples: 40 download_size: 134481 dataset_size: 2614760 - config_name: 2013.entrance_exam.EN features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 180775 num_examples: 46 download_size: 57454 dataset_size: 180775 - config_name: 2013.main.AR features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 4180927 num_examples: 284 download_size: 155246 dataset_size: 4180927 - config_name: 2013.main.BG features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 5403194 num_examples: 284 download_size: 191476 dataset_size: 5403194 - config_name: 2013.main.EN features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2887814 num_examples: 284 download_size: 134883 dataset_size: 2887814 - config_name: 2013.main.ES features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 3449641 num_examples: 284 download_size: 151478 dataset_size: 3449641 - config_name: 2013.main.RO features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 3362997 num_examples: 284 download_size: 154236 dataset_size: 3362997 configs: - config_name: 2011.main.DE data_files: - split: train path: 2011.main.DE/train-* - config_name: 2011.main.EN data_files: - split: train path: 2011.main.EN/train-* - config_name: 2011.main.ES data_files: - split: train path: 2011.main.ES/train-* - config_name: 2011.main.IT data_files: - split: train path: 2011.main.IT/train-* - config_name: 2011.main.RO data_files: - split: train path: 2011.main.RO/train-* - config_name: 2012.alzheimers.EN data_files: - split: train path: 2012.alzheimers.EN/train-* - config_name: 2012.main.AR data_files: - split: train path: 2012.main.AR/train-* - config_name: 2012.main.BG data_files: - split: train path: 2012.main.BG/train-* - config_name: 2012.main.DE data_files: - split: train path: 2012.main.DE/train-* - config_name: 2012.main.EN data_files: - split: train path: 2012.main.EN/train-* - config_name: 2012.main.ES data_files: - split: train path: 2012.main.ES/train-* - config_name: 2012.main.IT data_files: - split: train path: 2012.main.IT/train-* - config_name: 2012.main.RO data_files: - split: train path: 2012.main.RO/train-* - config_name: 2013.alzheimers.EN data_files: - split: train path: 2013.alzheimers.EN/train-* - config_name: 2013.entrance_exam.EN data_files: - split: train path: 2013.entrance_exam.EN/train-* - config_name: 2013.main.AR data_files: - split: train path: 2013.main.AR/train-* - config_name: 2013.main.BG data_files: - split: train path: 2013.main.BG/train-* - config_name: 2013.main.EN data_files: - split: train path: 2013.main.EN/train-* - config_name: 2013.main.ES data_files: - split: train path: 2013.main.ES/train-* - config_name: 2013.main.RO data_files: - split: train path: 2013.main.RO/train-* --- # Dataset Card for "qa4mre" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://nlp.uned.es/clef-qa/repository/qa4mre.php - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [QA4MRE 2011-2013: Overview of Question Answering for Machine Reading Evaluation](https://link.springer.com/chapter/10.1007/978-3-642-40802-1_29) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 5.49 MB - **Size of the generated dataset:** 48.35 MB - **Total amount of disk used:** 53.84 MB ### Dataset Summary QA4MRE dataset was created for the CLEF 2011/2012/2013 shared tasks to promote research in question answering and reading comprehension. The dataset contains a supporting passage and a set of questions corresponding to the passage. Multiple options for answers are provided for each question, of which only one is correct. The training and test datasets are available for the main track. Additional gold standard documents are available for two pilot studies: one on alzheimers data, and the other on entrance exams data. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### 2011.main.DE - **Size of downloaded dataset files:** 0.22 MB - **Size of the generated dataset:** 1.75 MB - **Total amount of disk used:** 1.97 MB An example of 'train' looks as follows. ``` ``` #### 2011.main.EN - **Size of downloaded dataset files:** 0.20 MB - **Size of the generated dataset:** 1.57 MB - **Total amount of disk used:** 1.77 MB An example of 'train' looks as follows. ``` ``` #### 2011.main.ES - **Size of downloaded dataset files:** 0.22 MB - **Size of the generated dataset:** 1.70 MB - **Total amount of disk used:** 1.91 MB An example of 'train' looks as follows. ``` ``` #### 2011.main.IT - **Size of downloaded dataset files:** 0.21 MB - **Size of the generated dataset:** 1.67 MB - **Total amount of disk used:** 1.88 MB An example of 'train' looks as follows. ``` ``` #### 2011.main.RO - **Size of downloaded dataset files:** 0.22 MB - **Size of the generated dataset:** 1.74 MB - **Total amount of disk used:** 1.96 MB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### 2011.main.DE - `topic_id`: a `string` feature. - `topic_name`: a `string` feature. - `test_id`: a `string` feature. - `document_id`: a `string` feature. - `document_str`: a `string` feature. - `question_id`: a `string` feature. - `question_str`: a `string` feature. - `answer_options`: a dictionary feature containing: - `answer_id`: a `string` feature. - `answer_str`: a `string` feature. - `correct_answer_id`: a `string` feature. - `correct_answer_str`: a `string` feature. #### 2011.main.EN - `topic_id`: a `string` feature. - `topic_name`: a `string` feature. - `test_id`: a `string` feature. - `document_id`: a `string` feature. - `document_str`: a `string` feature. - `question_id`: a `string` feature. - `question_str`: a `string` feature. - `answer_options`: a dictionary feature containing: - `answer_id`: a `string` feature. - `answer_str`: a `string` feature. - `correct_answer_id`: a `string` feature. - `correct_answer_str`: a `string` feature. #### 2011.main.ES - `topic_id`: a `string` feature. - `topic_name`: a `string` feature. - `test_id`: a `string` feature. - `document_id`: a `string` feature. - `document_str`: a `string` feature. - `question_id`: a `string` feature. - `question_str`: a `string` feature. - `answer_options`: a dictionary feature containing: - `answer_id`: a `string` feature. - `answer_str`: a `string` feature. - `correct_answer_id`: a `string` feature. - `correct_answer_str`: a `string` feature. #### 2011.main.IT - `topic_id`: a `string` feature. - `topic_name`: a `string` feature. - `test_id`: a `string` feature. - `document_id`: a `string` feature. - `document_str`: a `string` feature. - `question_id`: a `string` feature. - `question_str`: a `string` feature. - `answer_options`: a dictionary feature containing: - `answer_id`: a `string` feature. - `answer_str`: a `string` feature. - `correct_answer_id`: a `string` feature. - `correct_answer_str`: a `string` feature. #### 2011.main.RO - `topic_id`: a `string` feature. - `topic_name`: a `string` feature. - `test_id`: a `string` feature. - `document_id`: a `string` feature. - `document_str`: a `string` feature. - `question_id`: a `string` feature. - `question_str`: a `string` feature. - `answer_options`: a dictionary feature containing: - `answer_id`: a `string` feature. - `answer_str`: a `string` feature. - `correct_answer_id`: a `string` feature. - `correct_answer_str`: a `string` feature. ### Data Splits | name |train| |------------|----:| |2011.main.DE| 120| |2011.main.EN| 120| |2011.main.ES| 120| |2011.main.IT| 120| |2011.main.RO| 120| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{10.1007/978-3-642-40802-1_29, author="Pe{\~{n}}as, Anselmo and Hovy, Eduard and Forner, Pamela and Rodrigo, {\'A}lvaro and Sutcliffe, Richard and Morante, Roser", editor="Forner, Pamela and M{\"u}ller, Henning and Paredes, Roberto and Rosso, Paolo and Stein, Benno", title="QA4MRE 2011-2013: Overview of Question Answering for Machine Reading Evaluation", booktitle="Information Access Evaluation. Multilinguality, Multimodality, and Visualization", year="2013", publisher="Springer Berlin Heidelberg", address="Berlin, Heidelberg", pages="303--320", isbn="978-3-642-40802-1" } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@albertvillanova](https://github.com/albertvillanova), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

community-datasets/ohsumed

community-datasets

2024-06-26T05:23:19Z

158

5

[ "task_categories:text-classification", "task_ids:multi-label-classification", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-nc-4.0", "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[ "text-classification" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - en license: - cc-by-nc-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - multi-label-classification pretty_name: Ohsumed dataset_info: config_name: ohsumed features: - name: seq_id dtype: int64 - name: medline_ui dtype: int64 - name: mesh_terms dtype: string - name: title dtype: string - name: publication_type dtype: string - name: abstract dtype: string - name: author dtype: string - name: source dtype: string splits: - name: train num_bytes: 76530191 num_examples: 54709 - name: test num_bytes: 435681911 num_examples: 293855 download_size: 213196532 dataset_size: 512212102 configs: - config_name: ohsumed data_files: - split: train path: ohsumed/train-* - split: test path: ohsumed/test-* default: true --- # Dataset Card for ohsumed ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://davis.wpi.edu/xmdv/datasets/ohsumed.html - **Repository:** https://trec.nist.gov/data/filtering/t9.filtering.tar.gz - **Paper:** https://link.springer.com/chapter/10.1007/978-1-4471-2099-5_20 - **Leaderboard:** - **Point of Contact:** [William Hersh](mailto:[email protected]) [Aakash Gupta](mailto:[email protected]) ### Dataset Summary The OHSUMED test collection is a set of 348,566 references from MEDLINE, the on-line medical information database, consisting of titles and/or abstracts from 270 medical journals over a five-year period (1987-1991). The available fields are title, abstract, MeSH indexing terms, author, source, and publication type. The National Library of Medicine has agreed to make the MEDLINE references in the test database available for experimentation, restricted to the following conditions: 1. The data will not be used in any non-experimental clinical, library, or other setting. 2. Any human users of the data will explicitly be told that the data is incomplete and out-of-date. Please check this [readme](https://trec.nist.gov/data/filtering/README.t9.filtering) for more details ### Supported Tasks and Leaderboards [Text Classification](https://paperswithcode.com/sota/text-classification-on-ohsumed) ### Languages The text is primarily in English. The BCP 47 code is `en` ## Dataset Structure ### Data Instances ``` {'seq_id': 7770, 'medline_ui': 87120420, 'mesh_terms': 'Adult; Aged; Aneurysm/CO; Arteriovenous Fistula/*TH; Carotid Arteries; Case Report; Female; Human; Jugular Veins; Male; Methods; Middle Age; Neck/*BS; Vertebral Artery.', 'title': 'Arteriovenous fistulas of the large vessels of the neck: nonsurgical percutaneous occlusion.', 'publication_type': 'JOURNAL ARTICLE.', 'abstract': 'We describe the nonsurgical treatment of arteriovenous fistulas of the large vessels in the neck using three different means of endovascular occlusion of these large lesions, which are surgically difficult to approach and treat.', 'author': 'Vitek JJ; Keller FS.', 'source': 'South Med J 8705; 80(2):196-200'} ``` ### Data Fields Here are the field definitions: - seg_id: sequential identifier (important note: documents should be processed in this order) - medline_ui: MEDLINE identifier (UI) (<DOCNO> used for relevance judgements) - mesh_terms: Human-assigned MeSH terms (MH) - title: Title (TI) - publication_type : Publication type (PT) - abstract: Abstract (AB) - author: Author (AU) - source: Source (SO) Note: some abstracts are truncated at 250 words and some references have no abstracts at all (titles only). We do not have access to the full text of the documents. ### Data Splits The files are Train/ Test. Where the training has files from 1987 while the test files has abstracts from 1988-91 Total number of files: Train: 54710 Test: 348567 ## Dataset Creation ### Curation Rationale The OHSUMED document collection was obtained by William Hersh ([email protected]) and colleagues for the experiments described in the papers below. [Check citation](#citation-information) ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? The test collection was built as part of a study assessing the use of MEDLINE by physicians in a clinical setting (Hersh and Hickam, above). Novice physicians using MEDLINE generated 106 queries. Only a subset of these queries were used in the TREC-9 Filtering Track. Before they searched, they were asked to provide a statement of information about their patient as well as their information need. The data was collected by William Hersh & colleagues ### Annotations #### Annotation process The existing OHSUMED topics describe actual information needs, but the relevance judgements probably do not have the same coverage provided by the TREC pooling process. The MeSH terms do not directly represent information needs, rather they are controlled indexing terms. However, the assessment should be more or less complete and there are a lot of them, so this provides an unusual opportunity to work with a very large topic sample. The topic statements are provided in the standard TREC format #### Who are the annotators? Each query was replicated by four searchers, two physicians experienced in searching and two medical librarians. The results were assessed for relevance by a different group of physicians, using a three point scale: definitely, possibly, or not relevant. The list of documents explicitly judged to be not relevant is not provided here. Over 10% of the query-document pairs were judged in duplicate to assess inter-observer reliability. For evaluation, all documents judged here as either possibly or definitely relevant were considered relevant. TREC-9 systems were allowed to distinguish between these two categories during the learning process if desired. ### Personal and Sensitive Information No PII data is present in the train, test or query files. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators [Aakash Gupta](mailto:[email protected]) *Th!nkEvolve Consulting* and Researcher at CoronaWhy ### Licensing Information CC BY-NC 4.0 ### Citation Information Hersh WR, Buckley C, Leone TJ, Hickam DH, OHSUMED: An interactive retrieval evaluation and new large test collection for research, Proceedings of the 17th Annual ACM SIGIR Conference, 1994, 192-201. Hersh WR, Hickam DH, Use of a multi-application computer workstation in a clinical setting, Bulletin of the Medical Library Association, 1994, 82: 382-389. ### Contributions Thanks to [@skyprince999](https://github.com/skyprince999) for adding this dataset.

community-datasets/yahoo_answers_topics

community-datasets

2024-06-24T04:59:52Z

1,669

54

[ "task_categories:text-classification", "task_ids:topic-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "source_datasets:extended|other-yahoo-answers-corpus", "language:en", "license:unknown", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[ "text-classification" ]

2022-03-02T23:29:22Z

1

--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - extended|other-yahoo-answers-corpus task_categories: - text-classification task_ids: - topic-classification pretty_name: YahooAnswersTopics dataset_info: config_name: yahoo_answers_topics features: - name: id dtype: int32 - name: topic dtype: class_label: names: '0': Society & Culture '1': Science & Mathematics '2': Health '3': Education & Reference '4': Computers & Internet '5': Sports '6': Business & Finance '7': Entertainment & Music '8': Family & Relationships '9': Politics & Government - name: question_title dtype: string - name: question_content dtype: string - name: best_answer dtype: string splits: - name: train num_bytes: 760285695 num_examples: 1400000 - name: test num_bytes: 32653862 num_examples: 60000 download_size: 533429663 dataset_size: 792939557 configs: - config_name: yahoo_answers_topics data_files: - split: train path: yahoo_answers_topics/train-* - split: test path: yahoo_answers_topics/test-* default: true train-eval-index: - config: yahoo_answers_topics task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: question_content: text topic: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for "Yahoo Answers Topics" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Add homepage URL here if available (unless it's a GitHub repository)]() - **Repository:** https://github.com/LC-John/Yahoo-Answers-Topic-Classification-Dataset - **Paper:** [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]() - **Leaderboard:** [If the dataset supports an active leaderboard, add link here]() - **Point of Contact:** [If known, name and email of at least one person the reader can contact for questions about the dataset.]() ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.

hf-internal-testing/librispeech_asr_dummy

hf-internal-testing

2024-06-19T14:41:44Z

70,751

4

[ "size_categories:n<1K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2022-03-02T23:29:22Z

null

--- dataset_info: config_name: clean features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string splits: - name: validation num_bytes: 9677021.0 num_examples: 73 download_size: 9192059 dataset_size: 9677021.0 configs: - config_name: clean data_files: - split: validation path: clean/validation-* ---

BAAI/TACO

BAAI

2024-06-19T09:16:49Z

2,808

109

[ "task_categories:text-generation", "task_ids:language-modeling", "language_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "language:code", "license:apache-2.0", "size_categories:10K<n<100K", "arxiv:2312.14852", "region:us", "code" ]

[ "text-generation" ]

2023-12-20T11:27:47Z

null

--- annotations_creators: [] language_creators: - crowdsourced - expert-generated language: - code license: apache-2.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: [] task_categories: - text-generation task_ids: - language-modeling paperswithcode_id: taco-topics-in-algorithmic-code-generation pretty_name: TACO tags: - code dataset_info: config_name: ALL features: - name: question dtype: string - name: solutions dtype: string - name: starter_code dtype: string - name: input_output dtype: string - name: difficulty dtype: string - name: raw_tags dtype: string - name: name dtype: string - name: source dtype: string - name: tags dtype: string - name: skill_types dtype: string - name: url dtype: string - name: Expected Auxiliary Space dtype: string - name: time_limit dtype: string - name: date dtype: string - name: picture_num dtype: string - name: memory_limit dtype: string - name: Expected Time Complexity dtype: string splits: - name: train num_bytes: 4239311973 num_examples: 25443 - name: test num_bytes: 481480755 num_examples: 1000 download_size: 2419844942 dataset_size: 4720792728 configs: - config_name: ALL data_files: - split: train path: ALL/train-* - split: test path: ALL/test-* --- # TACO Dataset <img src="https://cdn-uploads.huggingface.co/production/uploads/6335113375bed9932474315e/rMxdXcC56S3FEh37oRa2s.png" width="200" height="200"> [TACO](https://github.com/FlagOpen/TACO) is a benchmark for code generation with 26443 problems. It can be used to evaluate the ability of language models to generate code from natural language specifications. ## Key Update: We remove and modified some test cases in test set. Please update to use the newest version. ## Dataset Description - **Repository:** https://github.com/FlagOpen/TACO/ - **Paper:** [TACO: Topics in Algorithmic COde generation dataset](https://arxiv.org/abs/2312.14852) - **Leaderboard:** [Code Generation on CodeContests](https://paperswithcode.com/sota/code-generation-on-taco-code) - **Point of Contact:** [Bo-Wen Zhang](mailto:[email protected]) ## Languages The dataset contains questions in English and code solutions in Python. ## Dataset Structure ```python from datasets import load_dataset load_dataset("BAAI/TACO") DatasetDict({ train: Dataset({ features: ['question', 'solutions', 'starter_code', 'input_output', 'difficulty', 'raw_tags', 'name', 'source', 'tags', 'skill_types', 'url', 'Expected Auxiliary Space', 'time_limit', 'date', 'picture_num', 'memory_limit', 'Expected Time Complexity'], num_rows: 25443 }) test: Dataset({ features: ['question', 'solutions', 'starter_code', 'input_output', 'difficulty', 'raw_tags', 'name', 'source', 'tags', 'skill_types', 'url', 'Expected Auxiliary Space', 'time_limit', 'date', 'picture_num', 'memory_limit', 'Expected Time Complexity'], num_rows: 1000 }) }) ``` ### How to use it You can load and iterate through the dataset with the following two lines of code for the train split: ```python from datasets import load_dataset import json ds = load_dataset("BAAI/TACO", split="train") sample = next(iter(ds)) # non-empty solutions and input_output features can be parsed from text format this way: sample["solutions"] = json.loads(sample["solutions"]) sample["input_output"] = json.loads(sample["input_output"]) sample["raw_tags"] = eval(sample["raw_tags"]) sample["tags"] = eval(sample["tags"]) sample["skill_types"] = eval(sample["skill_types"]) print(sample) #OUTPUT: { "question": "You have a deck of $n$ cards, and you'd like to reorder it to a new one.\n\nEach card has a value between $1$ and $n$ equal to $p_i$. ...", "solutions": [ "import heapq\nfrom math import sqrt\nimport operator\nimport sys\ninf_var = 0\nif inf_var == 1:\n\tinf = open('input.txt', 'r')\nelse:\n\tinf = sys.stdin\n ...", "t = int(input())\nfor _ in range(t):\n\tn = int(input())\n\tp = list(map(int, input().split()))\n\tans = []\n\tp1 = [-1] * (n + 1)\n\tfor i in range(n):\n\t\tp1[p[i]] = i\n\ti = n\n\twhile i:\n\t\twhile i > 0 and p1[i] == -1:\n\t\t\ti -= 1\n\t\telse:\n\t\t\tif i:\n\t\t\t\tk = 0\n\t\t\t\tfor j in range(p1[i], n):\n\t\t\t\t\tans.append(p[j])\n\t\t\t\t\tp1[p[j]] = -1\n\t\t\t\t\tk += 1\n\t\t\t\tn -= k\n\t\t\t\ti -= 1\n\t\t\telse:\n\t\t\t\tbreak\n\tprint(*ans)\n", "import sys\n\ndef get_ints():\n\treturn map(int, sys.stdin.readline().strip().split())\n\ndef get_list():\n\treturn list(map(int, sys.stdin.readline().strip().split()))\n\ndef get_list_string():\n\treturn list(map(str, sys.stdin.readline().strip().split()))\n\ndef get_string():\n\treturn sys.stdin.readline().strip()\n\ndef get_int():\n\treturn int(sys.stdin.readline().strip())\n\ndef get_print_int(x):\n\tsys.stdout.write(str(x) + '\\n')\n\ndef get_print(x):\n\tsys.stdout.write(x + '\\n')\n\ndef get_print_int_same(x):\n\tsys.stdout.write(str(x) + ' ')\n\ndef get_print_same(x):\n\tsys.stdout.write(x + ' ')\nfrom sys import maxsize\n\ndef solve():\n\tfor _ in range(get_int()):\n\t\tn = get_int()\n\t\tarr = get_list()\n\t\ti = n - 1\n\t\tj = n - 1\n\t\ttemp = sorted(arr)\n\t\tvis = [False] * n\n\t\tans = []\n\t\twhile j >= 0:\n\t\t\tt = j\n\t\t\ttt = []\n\t\t\twhile t >= 0 and arr[t] != temp[i]:\n\t\t\t\tvis[arr[t] - 1] = True\n\t\t\t\ttt.append(arr[t])\n\t\t\t\tt -= 1\n\t\t\tvis[arr[t] - 1] = True\n\t\t\ttt.append(arr[t])\n\t\t\ttt = tt[::-1]\n\t\t\tfor k in tt:\n\t\t\t\tans.append(k)\n\t\t\tj = t - 1\n\t\t\twhile i >= 0 and vis[i]:\n\t\t\t\ti -= 1\n\t\tget_print(' '.join(map(str, ans)))\nsolve()\n", ... ], "starter_code": "", "input_output": { "inputs": [ "4\n4\n1 2 3 4\n5\n1 5 2 4 3\n6\n4 2 5 3 6 1\n1\n1\n", "4\n4\n2 1 3 4\n5\n1 5 2 4 3\n6\n4 2 5 3 6 1\n1\n1\n", "4\n4\n2 1 3 4\n5\n1 5 2 4 3\n6\n2 4 5 3 6 1\n1\n1\n", "4\n4\n1 2 3 4\n5\n1 5 2 4 3\n6\n4 2 5 3 6 1\n1\n1\n" ], "outputs": [ "4 3 2 1\n5 2 4 3 1\n6 1 5 3 4 2\n1\n", "4 3 2 1\n5 2 4 3 1\n6 1 5 3 4 2\n1\n", "4 3 2 1\n5 2 4 3 1\n6 1 5 3 4 2\n1\n", "\n4 3 2 1\n5 2 4 3 1\n6 1 5 3 4 2\n1\n" ] }, "difficulty": "EASY", "raw_tags": [ "data structures", "greedy", "math" ], "name": null, "source": "codeforces", "tags": [ "Data structures", "Mathematics", "Greedy algorithms" ], "skill_types": [ "Data structures", "Greedy algorithms" ], "url": "https://codeforces.com/problemset/problem/1492/B", "Expected Auxiliary Space": null, "time_limit": "1 second", "date": "2021-02-23", "picture_num": "0", "memory_limit": "512 megabytes", "Expected Time Complexity": null } ``` Each sample consists of a programming problem formulation in English, some ground truth Python solutions, test cases that are defined by their inputs and outputs and function name if provided, as well as some metadata regarding the difficulty level (difficulty), topics of task (raw tags), algorithms (tags) as well as required programming skill types (skill_types) of the problem and its source. If a sample has non empty `input_output` feature, you can read it as a dictionary with keys `inputs` and `outputs` and `fn_name` if it exists, and similarily you can parse the solutions into a list of solutions as shown in the code above. You can also filter the dataset for the difficulty level: EASY, MEDIUM, MEDIUM_HARD, HARD and VERY_HARD, or filter the programming skill types: Amortized analysis, Bit manipulation, Complete search, Data structures, Dynamic programming, Greedy algorithms, Range queries, Sorting. Just pass the list of difficulties or skills as a list. E.g. if you want the most challenging problems, you need to select the VERY_HARD level: ```python ds = load_dataset("BAAI/TACO", split="train", difficulties=["VERY_HARD"]) print(next(iter(ds))["question"]) ``` ``` #OUTPUT: """Let S(n) denote the number that represents the digits of n in sorted order. For example, S(1) = 1, S(5) = 5, S(50394) = 3459, S(353535) = 333555. Given a number X, compute <image> modulo 109 + 7. Input The first line of input will contain the integer X (1 ≤ X ≤ 10700). Output Print a single integer, the answer to the question. Examples Input 21 Output 195 Input 345342 Output 390548434 Note The first few values of S are 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 11, 12, 13, 14, 15, 16, 17, 18, 19, 2, 12. The sum of these values is 195. ``` Or if you want the problems invovled with Range queries and Sorting, you need to select the skills Range queries and Sorting: ```python ds = load_dataset("BAAI/TACO", split="train", skills=["Range queries", "Sorting"]) ``` ### Data Fields |Field|Type|Description| |---|---|---| |question|string|problem description| |solutions|string|some python solutions| |input_output|string|Json string with "inputs" and "outputs" of the test cases, might also include "fn_name" the name of the function| |difficulty|string|difficulty level of the problem| |picture_num|string|the number of pictures in the problem| |source|string|the source of the problem| |url|string|url of the source of the problem| |date|string|the date of the problem| |starter_code|string|starter code to include in prompts| |time_limit|string|the time consumption limit to solve the problem| |memory_limit|string|the memory consumption limit to solve the problem| |Expected Auxiliary Space|string|the extra auxiliary space expected to solve the problem| |Expected Time Complexity|string|the time complexity expected to solve the problem| |raw_tags|string|the topics of the programming task| |tags|string|the manually annoatated algorithms needed to solve the problem| |skill_types|string|the mapped programming skill types to solve the problem| ### Data Splits The dataset contains a train with 25443 samples and test splits with 1000 samples. ### Dataset Statistics * 26443 coding problems * 1.55M verified solutions * for tests split, the average number of test cases is 202.3 * all files have ground-truth solutions in the test split ## Dataset Creation To create the TACO dataset, the authors manually curated problems from open-access sites where programmers share problems with each other, including Aizu AtCoder, CodeChef, Codeforces, CodeWars, GeeksforGeeks, HackerEarth, HackerRank, Katti and LeetCode. For more details please refer to the original paper. ## License The TACO dataset that is authored by BAAI, Shandong Normal University and Peking University is released under an [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0). However, the data also includes content licensed under other permissive licenses such as MIT License, or web-crawled data which is used under the terms of the CC BY 4.0 license ([Creative Commons Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/legalcode)). We gratefully acknowledge the contributions of the following: * some AtCoder, Codeforces, CodeWars, Kattis, LeetCode material curated from APPS dataset (https://github.com/hendrycks/apps) * some Aizu, AtCoder, CodeChef, Codeforces material curated from CodeContest dataset (https://github.com/google-deepmind/code_contests) * Codeforces materials are sourced from http://codeforces.com. * CodeChef materials are sourced from https://www.codechef.com. * GeekforGeeks materials are sourced from https://www.geeksforgeeks.org * HackerEarth materials are curated from: [Description2Code Dataset](https://github.com/ethancaballero/description2code), licensed under the [MIT open source license](https://opensource.org/licenses/MIT), copyright not specified. * HackerRank materials are sourced from https://www.hackerrank.com. We don't know what the legal rights or data licenses of HackerRank. Please contact us if there is data license. ## Citation Information If you find our data, or code helpful, please cite [the original paper](https://arxiv.org/abs/2312.14852): ``` @article{li2023taco, title={TACO: Topics in Algorithmic COde generation dataset}, author={Rongao Li and Jie Fu and Bo-Wen Zhang and Tao Huang and Zhihong Sun and Chen Lyu and Guang Liu and Zhi Jin and Ge Li}, journal={arXiv preprint arXiv:2312.14852}, year={2023} } ```

toxigen/toxigen-data

toxigen

2024-06-17T16:35:29Z

4,574

55

[ "task_categories:text-classification", "task_ids:hate-speech-detection", "annotations_creators:expert-generated", "language_creators:machine-generated", "multilinguality:monolingual", "source_datasets:original", "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2203.09509", "region:us" ]

[ "text-classification" ]

2022-05-01T15:49:02Z

1

--- annotations_creators: - expert-generated language_creators: - machine-generated multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - hate-speech-detection pretty_name: ToxiGen dataset_info: - config_name: annotated features: - name: text dtype: string - name: target_group dtype: string - name: factual? dtype: string - name: ingroup_effect dtype: string - name: lewd dtype: string - name: framing dtype: string - name: predicted_group dtype: string - name: stereotyping dtype: string - name: intent dtype: float64 - name: toxicity_ai dtype: float64 - name: toxicity_human dtype: float64 - name: predicted_author dtype: string - name: actual_method dtype: string splits: - name: test num_bytes: 364518 num_examples: 940 - name: train num_bytes: 3238381 num_examples: 8960 download_size: 768996 dataset_size: 3602899 - config_name: annotations features: - name: Input.prompt dtype: string - name: Input.text dtype: string - name: Input.time dtype: string - name: Input.generation_method dtype: string - name: Input.prompt_label dtype: string - name: Input.target_group dtype: string - name: Input.binary_prompt_label dtype: int64 - name: Answer.annotatorAge dtype: string - name: Answer.annotatorGender dtype: string - name: Answer.annotatorMinority dtype: string - name: Answer.annotatorPolitics.1 dtype: bool - name: Answer.annotatorPolitics.2 dtype: bool - name: Answer.annotatorPolitics.3 dtype: bool - name: Answer.annotatorPolitics.4 dtype: bool - name: Answer.annotatorPolitics.5 dtype: bool - name: Answer.annotatorRace dtype: string - name: Answer.factSelect dtype: string - name: Answer.framingQ dtype: string - name: Answer.inGroup.on dtype: bool - name: Answer.ingroup.1 dtype: bool - name: Answer.ingroup.2 dtype: bool - name: Answer.ingroup.3 dtype: bool - name: Answer.intent.1 dtype: bool - name: Answer.intent.2 dtype: bool - name: Answer.intent.3 dtype: bool - name: Answer.intent.4 dtype: bool - name: Answer.intent.5 dtype: bool - name: Answer.lewd.1 dtype: bool - name: Answer.lewd.2 dtype: bool - name: Answer.lewd.3 dtype: bool - name: Answer.refTarget dtype: string - name: Answer.stateFrame dtype: string - name: Answer.stateGroup dtype: string - name: Answer.stereo.1 dtype: bool - name: Answer.stereo.2 dtype: bool - name: Answer.stereo.3 dtype: bool - name: Answer.toAI.1 dtype: bool - name: Answer.toAI.2 dtype: bool - name: Answer.toAI.3 dtype: bool - name: Answer.toAI.4 dtype: bool - name: Answer.toAI.5 dtype: bool - name: Answer.toPER.1 dtype: bool - name: Answer.toPER.2 dtype: bool - name: Answer.toPER.3 dtype: bool - name: Answer.toPER.4 dtype: bool - name: Answer.toPER.5 dtype: bool - name: Answer.writer.1 dtype: bool - name: Answer.writer.2 dtype: bool - name: HashedWorkerId dtype: int64 splits: - name: train num_bytes: 21933185 num_examples: 27450 download_size: 3350653 dataset_size: 21933185 - config_name: prompts features: - name: text dtype: string splits: - name: hate_trans_1k num_bytes: 585554 num_examples: 1000 - name: neutral_black_1k num_bytes: 857769 num_examples: 1000 - name: hate_native_american_1k num_bytes: 480000 num_examples: 1000 - name: neutral_immigrant_1k num_bytes: 342243 num_examples: 1000 - name: hate_middle_east_1k num_bytes: 426551 num_examples: 1000 - name: neutral_lgbtq_1k num_bytes: 914319 num_examples: 1000 - name: neutral_women_1k num_bytes: 394963 num_examples: 1000 - name: neutral_chinese_1k num_bytes: 412062 num_examples: 1000 - name: hate_latino_1k num_bytes: 708000 num_examples: 1000 - name: hate_bisexual_1k num_bytes: 447794 num_examples: 1000 - name: hate_mexican_1k num_bytes: 675444 num_examples: 1000 - name: hate_asian_1k num_bytes: 503093 num_examples: 1000 - name: neutral_mental_disability_1k num_bytes: 556905 num_examples: 1000 - name: neutral_mexican_1k num_bytes: 483603 num_examples: 1000 - name: hate_mental_disability_1k num_bytes: 480620 num_examples: 1000 - name: neutral_bisexual_1k num_bytes: 915612 num_examples: 1000 - name: neutral_latino_1k num_bytes: 470000 num_examples: 1000 - name: hate_chinese_1k num_bytes: 384934 num_examples: 1000 - name: neutral_jewish_1k num_bytes: 649674 num_examples: 1000 - name: hate_muslim_1k num_bytes: 425760 num_examples: 1000 - name: neutral_asian_1k num_bytes: 615895 num_examples: 1000 - name: hate_physical_disability_1k num_bytes: 413643 num_examples: 1000 - name: hate_jewish_1k num_bytes: 573538 num_examples: 1000 - name: neutral_muslim_1k num_bytes: 491659 num_examples: 1000 - name: hate_immigrant_1k num_bytes: 285309 num_examples: 1000 - name: hate_black_1k num_bytes: 745295 num_examples: 1000 - name: hate_lgbtq_1k num_bytes: 577075 num_examples: 1000 - name: hate_women_1k num_bytes: 389583 num_examples: 1000 - name: neutral_middle_east_1k num_bytes: 415319 num_examples: 1000 - name: neutral_native_american_1k num_bytes: 586993 num_examples: 1000 - name: neutral_physical_disability_1k num_bytes: 458497 num_examples: 1000 download_size: 1698170 dataset_size: 16667706 - config_name: train features: - name: prompt dtype: string - name: generation dtype: string - name: generation_method dtype: string - name: group dtype: string - name: prompt_label dtype: int64 - name: roberta_prediction dtype: float64 splits: - name: train num_bytes: 169400442 num_examples: 250951 download_size: 18784380 dataset_size: 169400442 configs: - config_name: annotated default: true data_files: - split: test path: annotated/test-* - split: train path: annotated/train-* - config_name: annotations data_files: - split: train path: annotations/train-* - config_name: prompts data_files: - split: hate_trans_1k path: prompts/hate_trans_1k-* - split: neutral_black_1k path: prompts/neutral_black_1k-* - split: hate_native_american_1k path: prompts/hate_native_american_1k-* - split: neutral_immigrant_1k path: prompts/neutral_immigrant_1k-* - split: hate_middle_east_1k path: prompts/hate_middle_east_1k-* - split: neutral_lgbtq_1k path: prompts/neutral_lgbtq_1k-* - split: neutral_women_1k path: prompts/neutral_women_1k-* - split: neutral_chinese_1k path: prompts/neutral_chinese_1k-* - split: hate_latino_1k path: prompts/hate_latino_1k-* - split: hate_bisexual_1k path: prompts/hate_bisexual_1k-* - split: hate_mexican_1k path: prompts/hate_mexican_1k-* - split: hate_asian_1k path: prompts/hate_asian_1k-* - split: neutral_mental_disability_1k path: prompts/neutral_mental_disability_1k-* - split: neutral_mexican_1k path: prompts/neutral_mexican_1k-* - split: hate_mental_disability_1k path: prompts/hate_mental_disability_1k-* - split: neutral_bisexual_1k path: prompts/neutral_bisexual_1k-* - split: neutral_latino_1k path: prompts/neutral_latino_1k-* - split: hate_chinese_1k path: prompts/hate_chinese_1k-* - split: neutral_jewish_1k path: prompts/neutral_jewish_1k-* - split: hate_muslim_1k path: prompts/hate_muslim_1k-* - split: neutral_asian_1k path: prompts/neutral_asian_1k-* - split: hate_physical_disability_1k path: prompts/hate_physical_disability_1k-* - split: hate_jewish_1k path: prompts/hate_jewish_1k-* - split: neutral_muslim_1k path: prompts/neutral_muslim_1k-* - split: hate_immigrant_1k path: prompts/hate_immigrant_1k-* - split: hate_black_1k path: prompts/hate_black_1k-* - split: hate_lgbtq_1k path: prompts/hate_lgbtq_1k-* - split: hate_women_1k path: prompts/hate_women_1k-* - split: neutral_middle_east_1k path: prompts/neutral_middle_east_1k-* - split: neutral_native_american_1k path: prompts/neutral_native_american_1k-* - split: neutral_physical_disability_1k path: prompts/neutral_physical_disability_1k-* - config_name: train data_files: - split: train path: train/train-* --- # Dataset Card for ToxiGen ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-instances) - [Additional Information](#additional-information) - [Citation Information](#citation-information) ## Sign up for Data Access To access ToxiGen, first fill out [this form](https://forms.office.com/r/r6VXX8f8vh). ## Dataset Description - **Repository:** https://github.com/microsoft/toxigen - **Paper:** https://arxiv.org/abs/2203.09509 - **Point of Contact #1:** [Tom Hartvigsen]([email protected]) - **Point of Contact #2:** [Saadia Gabriel]([email protected]) ### Dataset Summary This dataset is for implicit hate speech detection. All instances were generated using GPT-3 and the methods described in [our paper](https://arxiv.org/abs/2203.09509). ### Languages All text is written in English. ## Dataset Structure ### Data Fields We release TOXIGEN as a dataframe with the following fields: - **prompt** is the prompt used for **generation**. - **generation** is the TOXIGEN generated text. - **generation_method** denotes whether or not ALICE was used to generate the corresponding generation. If this value is ALICE, then ALICE was used, if it is TopK, then ALICE was not used. - **prompt_label** is the binary value indicating whether or not the prompt is toxic (1 is toxic, 0 is benign). - **group** indicates the target group of the prompt. - **roberta_prediction** is the probability predicted by our corresponding RoBERTa model for each instance. ### Citation Information ```bibtex @inproceedings{hartvigsen2022toxigen, title={ToxiGen: A Large-Scale Machine-Generated Dataset for Implicit and Adversarial Hate Speech Detection}, author={Hartvigsen, Thomas and Gabriel, Saadia and Palangi, Hamid and Sap, Maarten and Ray, Dipankar and Kamar, Ece}, booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}, year={2022} } ```

togethercomputer/RedPajama-Data-1T

togethercomputer

2024-06-17T11:36:03Z

1,926

1,084

[ "task_categories:text-generation", "language:en", "size_categories:1M<n<10M", "modality:text", "library:datasets", "library:mlcroissant", "region:us" ]

[ "text-generation" ]

2023-04-17T06:28:35Z

null

--- task_categories: - text-generation language: - en pretty_name: Red Pajama 1T --- ### Getting Started The dataset consists of 2084 jsonl files. You can download the dataset using HuggingFace: ```python from datasets import load_dataset ds = load_dataset("togethercomputer/RedPajama-Data-1T") ``` Or you can directly download the files using the following command: ``` wget 'https://data.together.xyz/redpajama-data-1T/v1.0.0/urls.txt' while read line; do dload_loc=${line#https://data.together.xyz/redpajama-data-1T/v1.0.0/} mkdir -p $(dirname $dload_loc) wget "$line" -O "$dload_loc" done < urls.txt ``` After downloading the files, you can load the dataset from disk by setting the `RED_PAJAMA_DATA_DIR` environment variable to the directory containing the files: ```python import os from datasets import load_dataset os.environ["RED_PAJAMA_DATA_DIR"] = "/path/to/download" ds = load_dataset("togethercomputer/RedPajama-Data-1T") ``` A smaller 1B-token sample of the dataset can be found [here](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T-Sample). A full set of scripts to recreate the dataset from scratch can be found [here](https://github.com/togethercomputer/RedPajama-Data). ### Dataset Summary RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset. | Dataset | Token Count | |---------------|-------------| | Commoncrawl | 878 Billion | | C4 | 175 Billion | | GitHub | 59 Billion | | ArXiv | 28 Billion | | Wikipedia | 24 Billion | | StackExchange | 20 Billion | | Total | 1.2 Trillion | ### Languages Primarily English, though the Wikipedia slice contains multiple languages. ## Dataset Structure The dataset structure is as follows: ```json { "text": ..., "meta": {"url": "...", "timestamp": "...", "source": "...", "language": "...", ...}, "red_pajama_subset": "common_crawl" | "c4" | "github" | "arxiv" | "wikipedia" | "stackexchange" } ``` ## Dataset Creation This dataset was created to follow the LLaMa paper as closely as possible to try to reproduce its recipe. ### Source Data #### Commoncrawl We download five dumps from Commoncrawl, and run the dumps through the official `cc_net` pipeline. We then deduplicate on the paragraph level, and filter out low quality text using a linear classifier trained to classify paragraphs as Wikipedia references or random Commoncrawl samples. #### C4 C4 is downloaded from Huggingface. The only preprocessing step is to bring the data into our own format. #### GitHub The raw GitHub data is downloaded from Google BigQuery. We deduplicate on the file level and filter out low quality files and only keep projects that are distributed under the MIT, BSD, or Apache license. #### Wikipedia We use the Wikipedia dataset available on Huggingface, which is based on the Wikipedia dump from 2023-03-20 and contains text in 20 different languages. The dataset comes in preprocessed format, so that hyperlinks, comments and other formatting boilerplate has been removed. #### Gutenberg and Books3 <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Defunct:</b> The 'book' config is defunct and no longer accessible due to reported copyright infringement for the Book3 dataset contained in this config.</p> </div> #### ArXiv ArXiv data is downloaded from Amazon S3 in the `arxiv` requester pays bucket. We only keep latex source files and remove preambles, comments, macros and bibliographies. #### Stackexchange The Stack Exchange split of the dataset is download from the [Internet Archive](https://archive.org/download/stackexchange). Here we only keep the posts from the 28 largest sites, remove html tags, group the posts into question-answer pairs, and order answers by their score. ### SHA256 Checksums SHA256 checksums for the dataset files for each data source are available here: ``` https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/arxiv_SHA256SUMS.txt https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/c4_SHA256SUMS.txt https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/common_crawl_SHA256SUMS.txt https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/github_SHA256SUMS.txt https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/stackexchange_SHA256SUMS.txt https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/wikipedia_SHA256SUMS.txt ``` To cite RedPajama, please use: ``` @software{together2023redpajama, author = {Together Computer}, title = {RedPajama: An Open Source Recipe to Reproduce LLaMA training dataset}, month = April, year = 2023, url = {https://github.com/togethercomputer/RedPajama-Data} } ``` ### License Please refer to the licenses of the data subsets you use. * [Common Crawl Foundation Terms of Use](https://commoncrawl.org/terms-of-use/full/) * [C4 license](https://huggingface.co/datasets/allenai/c4#license) * GitHub was limited to MIT, BSD, or Apache licenses only * [ArXiv Terms of Use](https://info.arxiv.org/help/api/tou.html) * [Wikipedia License](https://huggingface.co/datasets/wikipedia#licensing-information) * [StackExchange license on the Internet Archive](https://archive.org/details/stackexchange)

google-research-datasets/conceptual_captions

google-research-datasets

2024-06-17T10:51:29Z

7,082

94

[ "task_categories:image-to-text", "task_ids:image-captioning", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:other", "size_categories:1M<n<10M", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[ "image-to-text" ]

2022-04-14T13:08:21Z

null

--- annotations_creators: - found language_creators: - found language: - en license: - other multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - image-to-text task_ids: - image-captioning paperswithcode_id: conceptual-captions pretty_name: Conceptual Captions dataset_info: - config_name: default features: - name: id dtype: string - name: caption dtype: string - name: url dtype: string splits: - name: train num_bytes: 623230370 num_examples: 3318333 - name: validation num_bytes: 2846024 num_examples: 15840 download_size: 0 dataset_size: 626076394 - config_name: labeled features: - name: image_url dtype: string - name: caption dtype: string - name: labels sequence: string - name: MIDs sequence: string - name: confidence_scores sequence: float64 splits: - name: train num_bytes: 1199325228 num_examples: 2007090 download_size: 532762865 dataset_size: 1199325228 - config_name: unlabeled features: - name: image_url dtype: string - name: caption dtype: string splits: - name: train num_bytes: 584517500 num_examples: 3318333 - name: validation num_bytes: 2698710 num_examples: 15840 download_size: 375258708 dataset_size: 587216210 configs: - config_name: labeled data_files: - split: train path: labeled/train-* - config_name: unlabeled data_files: - split: train path: unlabeled/train-* - split: validation path: unlabeled/validation-* default: true --- # Dataset Card for Conceptual Captions ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Dataset Preprocessing](#dataset-preprocessing) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** [Conceptual Captions homepage](https://ai.google.com/research/ConceptualCaptions/) - **Repository:** [Conceptual Captions repository](https://github.com/google-research-datasets/conceptual-captions) - **Paper:** [Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning](https://www.aclweb.org/anthology/P18-1238/) - **Leaderboard:** [Conceptual Captions leaderboard](https://ai.google.com/research/ConceptualCaptions/competition?active_tab=leaderboard)https://ai.google.com/research/ConceptualCaptions/leaderboard?active_tab=leaderboard - **Point of Contact:** [Conceptual Captions e-mail](mailto:[email protected]) ### Dataset Summary Conceptual Captions is a dataset consisting of ~3.3M images annotated with captions. In contrast with the curated style of other image caption annotations, Conceptual Caption images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles. More precisely, the raw descriptions are harvested from the Alt-text HTML attribute associated with web images. To arrive at the current version of the captions, we have developed an automatic pipeline that extracts, filters, and transforms candidate image/caption pairs, with the goal of achieving a balance of cleanliness, informativeness, fluency, and learnability of the resulting captions. ### Dataset Preprocessing This dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code: ```python from concurrent.futures import ThreadPoolExecutor from functools import partial import io import urllib import PIL.Image from datasets import load_dataset from datasets.utils.file_utils import get_datasets_user_agent USER_AGENT = get_datasets_user_agent() def fetch_single_image(image_url, timeout=None, retries=0): for _ in range(retries + 1): try: request = urllib.request.Request( image_url, data=None, headers={"user-agent": USER_AGENT}, ) with urllib.request.urlopen(request, timeout=timeout) as req: image = PIL.Image.open(io.BytesIO(req.read())) break except Exception: image = None return image def fetch_images(batch, num_threads, timeout=None, retries=0): fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries) with ThreadPoolExecutor(max_workers=num_threads) as executor: batch["image"] = list(executor.map(fetch_single_image_with_args, batch["image_url"])) return batch num_threads = 20 dset = load_dataset("google-research-datasets/conceptual_captions") dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads}) ``` ### Supported Tasks and Leaderboards - `image-captioning`: This dataset can be used to train model for the Image Captioning task. The leaderboard for this task is available [here](https://ai.google.com/research/ConceptualCaptions/competition?active_tab=leaderboard). Official submission output captions are scored against the reference captions from the hidden test set using [this](https://github.com/tylin/coco-caption) implementation of the CIDEr (primary), ROUGE-L and SPICE metrics. ### Languages All captions are in English. ## Dataset Structure ### Data Instances #### `unlabeled` Each instance in this configuration represents a single image with a caption: ``` { 'image_url': 'http://lh6.ggpht.com/-IvRtNLNcG8o/TpFyrudaT6I/AAAAAAAAM6o/_11MuAAKalQ/IMG_3422.JPG?imgmax=800', 'caption': 'a very typical bus station' } ``` #### `labeled` Each instance in this configuration represents a single image with a caption with addtional machine-generated image labels and confidence scores: ``` { 'image_url': 'https://thumb1.shutterstock.com/display_pic_with_logo/261388/223876810/stock-vector-christmas-tree-on-a-black-background-vector-223876810.jpg', 'caption': 'christmas tree on a black background .', 'labels': ['christmas tree', 'christmas decoration', 'font', 'text', 'graphic design', 'illustration','interior design', 'tree', 'christmas eve', 'ornament', 'fir', 'plant', 'pine', 'pine family', 'graphics'], 'MIDs': ['/m/025nd', '/m/05fc9mj', '/m/03gq5hm', '/m/07s6nbt', '/m/03c31', '/m/01kr8f', '/m/0h8nzzj', '/m/07j7r', '/m/014r1s', '/m/05ykl4', '/m/016x4z', '/m/05s2s', '/m/09t57', '/m/01tfm0', '/m/021sdg'], 'confidence_scores': [0.9818305373191833, 0.952756941318512, 0.9227379560470581, 0.8524878621101379, 0.7597672343254089, 0.7493422031402588, 0.7332468628883362, 0.6869218349456787, 0.6552258133888245, 0.6357356309890747, 0.5992692708969116, 0.585474967956543, 0.5222904086112976, 0.5113164782524109, 0.5036579966545105] } ``` ### Data Fields #### `unlabeled` - `image_url`: Static URL for downloading the image associated with the post. - `caption`: Textual description of the image. #### `labeled` - `image_url`: Static URL for downloading the image associated with the post. - `caption`: Textual description of the image. - `labels`: A sequence of machine-generated labels obtained using the [Google Cloud Vision API](https://cloud.google.com/vision). - `MIDs`: A sequence of machine-generated identifiers (MID) corresponding to the label's Google Knowledge Graph entry. - `confidence_scores`: A sequence of confidence scores denoting how likely the corresponing labels are present on the image. ### Data Splits #### `unlabeled` The basic version of the dataset split into Training and Validation splits. The Training split consists of 3,318,333 image-URL/caption pairs and the Validation split consists of 15,840 image-URL/caption pairs. #### `labeled` The labeled version of the dataset with a single. The entire data is contained in Training split, which is a subset of 2,007,090 image-URL/caption pairs from the Training set of the `unlabeled` config. ## Dataset Creation ### Curation Rationale From the paper: > In this paper, we make contributions to both the data and modeling categories. First, we present a new dataset of caption annotations Conceptual Captions (Fig. 1), which has an order of magnitude more images than the COCO dataset. Conceptual Captions consists of about 3.3M himage, descriptioni pairs. In contrast with the curated style of the COCO images, Conceptual Captions images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles. ### Source Data #### Initial Data Collection and Normalization From the homepage: >For Conceptual Captions, we developed a fully automatic pipeline that extracts, filters, and transforms candidate image/caption pairs, with the goal of achieving a balance of cleanliness, informativeness, fluency, and learnability of the resulting captions. Because no human annotators are involved, the Conceptual Captions dataset generation process is highly scalable. > >To generate this dataset, we started with a Flume pipeline that processes billions of Internet webpages, extracting, filtering, and processing candidate image and caption pairs, and keeping those that pass through several filters. > >We first screen for certain properties like size, aspect ratio, adult content scores. These filters discard more than 65% of the candidates. Next, we use Alt-Texts for text-based filtering, removing captions with non-descriptive text (such as SEO tags or hashtags); we also discard texts with high sentiment polarity or adult content scores, resulting in just 3% of the incoming candidates passing through. > >In the next step, we filter out candidates for which none of the text tokens can be mapped to the visual content of the image. We use image classifiers (e.g., Google Cloud Vision APIs) to assign class labels to images and match these labels against the candidate text (allowing morphological transformations), discarding >around 60% of the candidates that reach this stage. > >The candidates passing the above filters tend to be good Alt-text image descriptions. However, a large majority of these use proper names (for people, venues, locations, etc.), brands, dates, quotes, etc. This creates two distinct problems. First, some of these cannot be inferred based on the image pixels alone. This is problematic because unless the image has the necessary visual information it is not useful for training. Second, even if the proper names could be inferred from the image it is extremely difficult for a model to learn to perform both fine-grained classification and natural-language descriptions simultaneously. We posit that if automatic determination of names, locations, brands, etc. is needed, it should be done as a separate task that may leverage image meta-information (e.g. GPS info), or complementary techniques such as OCR. > >We address the above problems with the insight that proper names should be replaced by words that represent the same general notion, i.e., by their concept. For example, we remove locations (“Crowd at a concert in Los Angeles“ becomes “Crowd at a concert”), names (e.g., “Former Miss World Priyanka Chopra on the red carpet” becomes “actor on the red carpet”), proper noun modifiers (e.g., “Italian cuisine” becomes just “cuisine”) and noun phrases (e.g., “actor and actor” becomes “actors”). Around 20% of the samples are discarded during this transformation because it can leave sentences too short, or otherwise inconsistent. > >Finally, we perform another round of filtering to identify concepts with low-count. We cluster all resolved entities (e.g., “actor”, “dog”, “neighborhood”, etc.) and keep only the candidate types which have a count of over 100 mentions. This retains around 16K entity concepts such as: “person”, “actor”, “artist”, “player” and “illustration”. The less frequent ones that we dropped include “baguette”, “bridle”, “deadline”, “ministry” and “funnel”. #### Who are the source language producers? Not specified. ### Annotations #### Annotation process Annotations are extracted jointly with the images using the automatic pipeline. #### Who are the annotators? Not specified. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Piyush Sharma, Nan Ding, Sebastian Goodman and Radu Soricut. ### Licensing Information The dataset may be freely used for any purpose, although acknowledgement of Google LLC ("Google") as the data source would be appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset. ### Citation Information ```bibtex @inproceedings{sharma2018conceptual, title = {Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning}, author = {Sharma, Piyush and Ding, Nan and Goodman, Sebastian and Soricut, Radu}, booktitle = {Proceedings of ACL}, year = {2018}, } ``` ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) and [@mariosasko](https://github.com/mariosasko) for adding this dataset.

NousResearch/CharacterCodex

NousResearch

2024-06-17T01:27:49Z

641

220

[ "language:en", "license:apache-2.0", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "language model" ]

[]

2024-06-05T00:30:38Z

null

--- language: - en license: apache-2.0 size_categories: - 10K<n<100K pretty_name: CharacterCodex dataset_info: features: - name: media_type dtype: string - name: genre dtype: string - name: character_name dtype: string - name: media_source dtype: string - name: description dtype: string - name: scenario dtype: string tags: - language model --- # Dataset Card for Character Codex ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/2qPIzxcnzXrEg66VZDjnv.png) ## Dataset Summary The Character Codex is a comprehensive dataset featuring popular characters from a wide array of media types and genres. Each entry includes detailed information about the character, the media source, and a unique scenario involving the character. This dataset is valuable for synthetic data, RAG for generative AI, writers, game developers, and fans who want to explore and utilize rich character descriptions for various creative projects. ## Dataset Structure ### Data Fields - **media_type**: The type of media the character originates from (e.g., Webcomics, Novels, Movies, TV Shows). - **genre**: The specific genre of the media type (e.g., Fantasy Webcomics, Martial Arts Fiction). - **character_name**: The name of the character. - **media_source**: The title of the media source where the character is from. - **description**: A detailed description of the character, including their role and significance in the story. - **scenario**: A creative scenario involving the character that can be used for interactive storytelling or role-playing purposes. ### Example Data ```json [ { "media_type": "Webcomics", "genre": "Fantasy Webcomics", "character_name": "Alana", "media_source": "Saga", "description": "Alana is one of the main characters from the webcomic \"Saga.\" She is a strong-willed and fiercely protective mother who is on the run with her family in a war-torn galaxy. The story blends elements of fantasy and science fiction, creating a rich and complex narrative.", "scenario": "You are a fellow traveler in the galaxy needing help, and Alana offers her assistance while sharing stories of her family's struggles and triumphs." }, { "media_type": "Novels", "genre": "Martial Arts Fiction", "character_name": "Yilin", "media_source": "The Smiling, Proud Wanderer", "description": "Yilin is a young nun from the Hengshan Sect in Jin Yong's novel \"The Smiling, Proud Wanderer.\" Known for her innocence and kindness, she becomes friends with the protagonist Linghu Chong. Her gentle nature often puts her at odds with the violent world of martial arts.", "scenario": "You are a fellow disciple of the Hengshan Sect seeking Yilin's comfort and advice after a particularly brutal conflict. Her gentle demeanor and compassionate words provide solace in a harsh world." } ] ``` # Usage ## Accessing the Data To load the dataset in your project, you can use the following code snippet: ```python from datasets import load_dataset dataset = load_dataset("NousResearch/CharacterCodex") ``` ## Use Cases - Seed Data: Useful for generating synthetic data or use in interactive experiences with generative AI. - Creative Writing: Use the detailed character descriptions and scenarios to inspire creative writing projects. - Educational: Study character development and storytelling techniques from various genres and media types. # Dataset Creation ## Data Collection The characters in this dataset were meticulously selected from a diverse range of media, ensuring a rich and varied collection. The descriptions and scenarios were crafted to provide insightful and engaging context for each character. ## Annotations Each character entry includes: - The media type (i.e. Novel, Magazine, Anime), the genre (i.e. action, historical), and the specific source/title of the media they are from (i.e. Pokemon) - A detailed description highlighting the character's role, traits, and significance. - A scenario designed to stimulate interactive and immersive experiences. # Citation ```bibtex @dataset{character_codex_2024, title={Character Codex}, author={"Teknium"}, year={2024}, note={https://huggingface.co/datasets/NousResearch/CharacterCodex} } ```

ikala/tmmluplus

ikala

2024-06-12T07:06:00Z

3,202

115

[ "task_categories:question-answering", "language:zh", "license:mit", "size_categories:10K<n<100K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "traditional chinese", "finance", "medical", "taiwan", "benchmark", "zh-tw", "zh-hant" ]

[ "question-answering" ]

2023-12-22T19:12:13Z

null

--- license: mit license_name: mit task_categories: - question-answering language: - zh tags: - traditional chinese - finance - medical - taiwan - benchmark - zh-tw - zh-hant pretty_name: tmmlu++ size_categories: - 100K<n<1M configs: - config_name: engineering_math data_files: - split: train path: "data/engineering_math_dev.csv" - split: validation path: "data/engineering_math_val.csv" - split: test path: "data/engineering_math_test.csv" - config_name: dentistry data_files: - split: train path: "data/dentistry_dev.csv" - split: validation path: "data/dentistry_val.csv" - split: test path: "data/dentistry_test.csv" - config_name: traditional_chinese_medicine_clinical_medicine data_files: - split: train path: "data/traditional_chinese_medicine_clinical_medicine_dev.csv" - split: validation path: "data/traditional_chinese_medicine_clinical_medicine_val.csv" - split: test path: "data/traditional_chinese_medicine_clinical_medicine_test.csv" - config_name: clinical_psychology data_files: - split: train path: "data/clinical_psychology_dev.csv" - split: validation path: "data/clinical_psychology_val.csv" - split: test path: "data/clinical_psychology_test.csv" - config_name: technical data_files: - split: train path: "data/technical_dev.csv" - split: validation path: "data/technical_val.csv" - split: test path: "data/technical_test.csv" - config_name: culinary_skills data_files: - split: train path: "data/culinary_skills_dev.csv" - split: validation path: "data/culinary_skills_val.csv" - split: test path: "data/culinary_skills_test.csv" - config_name: mechanical data_files: - split: train path: "data/mechanical_dev.csv" - split: validation path: "data/mechanical_val.csv" - split: test path: "data/mechanical_test.csv" - config_name: logic_reasoning data_files: - split: train path: "data/logic_reasoning_dev.csv" - split: validation path: "data/logic_reasoning_val.csv" - split: test path: "data/logic_reasoning_test.csv" - config_name: real_estate data_files: - split: train path: "data/real_estate_dev.csv" - split: validation path: "data/real_estate_val.csv" - split: test path: "data/real_estate_test.csv" - config_name: general_principles_of_law data_files: - split: train path: "data/general_principles_of_law_dev.csv" - split: validation path: "data/general_principles_of_law_val.csv" - split: test path: "data/general_principles_of_law_test.csv" - config_name: finance_banking data_files: - split: train path: "data/finance_banking_dev.csv" - split: validation path: "data/finance_banking_val.csv" - split: test path: "data/finance_banking_test.csv" - config_name: anti_money_laundering data_files: - split: train path: "data/anti_money_laundering_dev.csv" - split: validation path: "data/anti_money_laundering_val.csv" - split: test path: "data/anti_money_laundering_test.csv" - config_name: ttqav2 data_files: - split: train path: "data/ttqav2_dev.csv" - split: validation path: "data/ttqav2_val.csv" - split: test path: "data/ttqav2_test.csv" - config_name: marketing_management data_files: - split: train path: "data/marketing_management_dev.csv" - split: validation path: "data/marketing_management_val.csv" - split: test path: "data/marketing_management_test.csv" - config_name: business_management data_files: - split: train path: "data/business_management_dev.csv" - split: validation path: "data/business_management_val.csv" - split: test path: "data/business_management_test.csv" - config_name: organic_chemistry data_files: - split: train path: "data/organic_chemistry_dev.csv" - split: validation path: "data/organic_chemistry_val.csv" - split: test path: "data/organic_chemistry_test.csv" - config_name: advance_chemistry data_files: - split: train path: "data/advance_chemistry_dev.csv" - split: validation path: "data/advance_chemistry_val.csv" - split: test path: "data/advance_chemistry_test.csv" - config_name: physics data_files: - split: train path: "data/physics_dev.csv" - split: validation path: "data/physics_val.csv" - split: test path: "data/physics_test.csv" - config_name: secondary_physics data_files: - split: train path: "data/secondary_physics_dev.csv" - split: validation path: "data/secondary_physics_val.csv" - split: test path: "data/secondary_physics_test.csv" - config_name: human_behavior data_files: - split: train path: "data/human_behavior_dev.csv" - split: validation path: "data/human_behavior_val.csv" - split: test path: "data/human_behavior_test.csv" - config_name: national_protection data_files: - split: train path: "data/national_protection_dev.csv" - split: validation path: "data/national_protection_val.csv" - split: test path: "data/national_protection_test.csv" - config_name: jce_humanities data_files: - split: train path: "data/jce_humanities_dev.csv" - split: validation path: "data/jce_humanities_val.csv" - split: test path: "data/jce_humanities_test.csv" - config_name: politic_science data_files: - split: train path: "data/politic_science_dev.csv" - split: validation path: "data/politic_science_val.csv" - split: test path: "data/politic_science_test.csv" - config_name: agriculture data_files: - split: train path: "data/agriculture_dev.csv" - split: validation path: "data/agriculture_val.csv" - split: test path: "data/agriculture_test.csv" - config_name: official_document_management data_files: - split: train path: "data/official_document_management_dev.csv" - split: validation path: "data/official_document_management_val.csv" - split: test path: "data/official_document_management_test.csv" - config_name: financial_analysis data_files: - split: train path: "data/financial_analysis_dev.csv" - split: validation path: "data/financial_analysis_val.csv" - split: test path: "data/financial_analysis_test.csv" - config_name: pharmacy data_files: - split: train path: "data/pharmacy_dev.csv" - split: validation path: "data/pharmacy_val.csv" - split: test path: "data/pharmacy_test.csv" - config_name: educational_psychology data_files: - split: train path: "data/educational_psychology_dev.csv" - split: validation path: "data/educational_psychology_val.csv" - split: test path: "data/educational_psychology_test.csv" - config_name: statistics_and_machine_learning data_files: - split: train path: "data/statistics_and_machine_learning_dev.csv" - split: validation path: "data/statistics_and_machine_learning_val.csv" - split: test path: "data/statistics_and_machine_learning_test.csv" - config_name: management_accounting data_files: - split: train path: "data/management_accounting_dev.csv" - split: validation path: "data/management_accounting_val.csv" - split: test path: "data/management_accounting_test.csv" - config_name: introduction_to_law data_files: - split: train path: "data/introduction_to_law_dev.csv" - split: validation path: "data/introduction_to_law_val.csv" - split: test path: "data/introduction_to_law_test.csv" - config_name: computer_science data_files: - split: train path: "data/computer_science_dev.csv" - split: validation path: "data/computer_science_val.csv" - split: test path: "data/computer_science_test.csv" - config_name: veterinary_pathology data_files: - split: train path: "data/veterinary_pathology_dev.csv" - split: validation path: "data/veterinary_pathology_val.csv" - split: test path: "data/veterinary_pathology_test.csv" - config_name: accounting data_files: - split: train path: "data/accounting_dev.csv" - split: validation path: "data/accounting_val.csv" - split: test path: "data/accounting_test.csv" - config_name: fire_science data_files: - split: train path: "data/fire_science_dev.csv" - split: validation path: "data/fire_science_val.csv" - split: test path: "data/fire_science_test.csv" - config_name: optometry data_files: - split: train path: "data/optometry_dev.csv" - split: validation path: "data/optometry_val.csv" - split: test path: "data/optometry_test.csv" - config_name: insurance_studies data_files: - split: train path: "data/insurance_studies_dev.csv" - split: validation path: "data/insurance_studies_val.csv" - split: test path: "data/insurance_studies_test.csv" - config_name: pharmacology data_files: - split: train path: "data/pharmacology_dev.csv" - split: validation path: "data/pharmacology_val.csv" - split: test path: "data/pharmacology_test.csv" - config_name: taxation data_files: - split: train path: "data/taxation_dev.csv" - split: validation path: "data/taxation_val.csv" - split: test path: "data/taxation_test.csv" - config_name: trust_practice data_files: - split: train path: "data/trust_practice_dev.csv" - split: validation path: "data/trust_practice_val.csv" - split: test path: "data/trust_practice_test.csv" - config_name: geography_of_taiwan data_files: - split: train path: "data/geography_of_taiwan_dev.csv" - split: validation path: "data/geography_of_taiwan_val.csv" - split: test path: "data/geography_of_taiwan_test.csv" - config_name: physical_education data_files: - split: train path: "data/physical_education_dev.csv" - split: validation path: "data/physical_education_val.csv" - split: test path: "data/physical_education_test.csv" - config_name: auditing data_files: - split: train path: "data/auditing_dev.csv" - split: validation path: "data/auditing_val.csv" - split: test path: "data/auditing_test.csv" - config_name: administrative_law data_files: - split: train path: "data/administrative_law_dev.csv" - split: validation path: "data/administrative_law_val.csv" - split: test path: "data/administrative_law_test.csv" - config_name: education_(profession_level) data_files: - split: train path: "data/education_(profession_level)_dev.csv" - split: validation path: "data/education_(profession_level)_val.csv" - split: test path: "data/education_(profession_level)_test.csv" - config_name: economics data_files: - split: train path: "data/economics_dev.csv" - split: validation path: "data/economics_val.csv" - split: test path: "data/economics_test.csv" - config_name: veterinary_pharmacology data_files: - split: train path: "data/veterinary_pharmacology_dev.csv" - split: validation path: "data/veterinary_pharmacology_val.csv" - split: test path: "data/veterinary_pharmacology_test.csv" - config_name: nautical_science data_files: - split: train path: "data/nautical_science_dev.csv" - split: validation path: "data/nautical_science_val.csv" - split: test path: "data/nautical_science_test.csv" - config_name: occupational_therapy_for_psychological_disorders data_files: - split: train path: "data/occupational_therapy_for_psychological_disorders_dev.csv" - split: validation path: "data/occupational_therapy_for_psychological_disorders_val.csv" - split: test path: "data/occupational_therapy_for_psychological_disorders_test.csv" - config_name: basic_medical_science data_files: - split: train path: "data/basic_medical_science_dev.csv" - split: validation path: "data/basic_medical_science_val.csv" - split: test path: "data/basic_medical_science_test.csv" - config_name: macroeconomics data_files: - split: train path: "data/macroeconomics_dev.csv" - split: validation path: "data/macroeconomics_val.csv" - split: test path: "data/macroeconomics_test.csv" - config_name: trade data_files: - split: train path: "data/trade_dev.csv" - split: validation path: "data/trade_val.csv" - split: test path: "data/trade_test.csv" - config_name: chinese_language_and_literature data_files: - split: train path: "data/chinese_language_and_literature_dev.csv" - split: validation path: "data/chinese_language_and_literature_val.csv" - split: test path: "data/chinese_language_and_literature_test.csv" - config_name: tve_design data_files: - split: train path: "data/tve_design_dev.csv" - split: validation path: "data/tve_design_val.csv" - split: test path: "data/tve_design_test.csv" - config_name: junior_science_exam data_files: - split: train path: "data/junior_science_exam_dev.csv" - split: validation path: "data/junior_science_exam_val.csv" - split: test path: "data/junior_science_exam_test.csv" - config_name: junior_math_exam data_files: - split: train path: "data/junior_math_exam_dev.csv" - split: validation path: "data/junior_math_exam_val.csv" - split: test path: "data/junior_math_exam_test.csv" - config_name: junior_chinese_exam data_files: - split: train path: "data/junior_chinese_exam_dev.csv" - split: validation path: "data/junior_chinese_exam_val.csv" - split: test path: "data/junior_chinese_exam_test.csv" - config_name: junior_social_studies data_files: - split: train path: "data/junior_social_studies_dev.csv" - split: validation path: "data/junior_social_studies_val.csv" - split: test path: "data/junior_social_studies_test.csv" - config_name: tve_mathematics data_files: - split: train path: "data/tve_mathematics_dev.csv" - split: validation path: "data/tve_mathematics_val.csv" - split: test path: "data/tve_mathematics_test.csv" - config_name: tve_chinese_language data_files: - split: train path: "data/tve_chinese_language_dev.csv" - split: validation path: "data/tve_chinese_language_val.csv" - split: test path: "data/tve_chinese_language_test.csv" - config_name: tve_natural_sciences data_files: - split: train path: "data/tve_natural_sciences_dev.csv" - split: validation path: "data/tve_natural_sciences_val.csv" - split: test path: "data/tve_natural_sciences_test.csv" - config_name: junior_chemistry data_files: - split: train path: "data/junior_chemistry_dev.csv" - split: validation path: "data/junior_chemistry_val.csv" - split: test path: "data/junior_chemistry_test.csv" - config_name: music data_files: - split: train path: "data/music_dev.csv" - split: validation path: "data/music_val.csv" - split: test path: "data/music_test.csv" - config_name: education data_files: - split: train path: "data/education_dev.csv" - split: validation path: "data/education_val.csv" - split: test path: "data/education_test.csv" - config_name: three_principles_of_people data_files: - split: train path: "data/three_principles_of_people_dev.csv" - split: validation path: "data/three_principles_of_people_val.csv" - split: test path: "data/three_principles_of_people_test.csv" - config_name: taiwanese_hokkien data_files: - split: train path: "data/taiwanese_hokkien_dev.csv" - split: validation path: "data/taiwanese_hokkien_val.csv" - split: test path: "data/taiwanese_hokkien_test.csv" --- # TMMLU+ : Large scale traditional chinese massive multitask language understanding <p align="center"> <img src="https://huggingface.co/datasets/ikala/tmmluplus/resolve/main/cover.png" alt="A close-up image of a neat paper note with a white background. The text 'TMMLU+' is written horizontally across the center of the note in bold, black. Join us to work in multimodal LLM : https://ikala.ai/recruit/" style="max-width: 400" width=400 /> </p> We present TMMLU+, a traditional Chinese massive multitask language understanding dataset. TMMLU+ is a multiple-choice question-answering dataset featuring 66 subjects, ranging from elementary to professional level. The TMMLU+ dataset is six times larger and contains more balanced subjects compared to its predecessor, [TMMLU](https://github.com/mtkresearch/MR-Models/tree/main/TC-Eval/data/TMMLU). We have included benchmark results in TMMLU+ from closed-source models and 20 open-weight Chinese large language models, with parameters ranging from 1.8B to 72B. The benchmark results show that Traditional Chinese variants still lag behind those trained on major Simplified Chinese models. ```python from datasets import load_dataset task_list = [ 'engineering_math', 'dentistry', 'traditional_chinese_medicine_clinical_medicine', 'clinical_psychology', 'technical', 'culinary_skills', 'mechanical', 'logic_reasoning', 'real_estate', 'general_principles_of_law', 'finance_banking', 'anti_money_laundering', 'ttqav2', 'marketing_management', 'business_management', 'organic_chemistry', 'advance_chemistry', 'physics', 'secondary_physics', 'human_behavior', 'national_protection', 'jce_humanities', 'politic_science', 'agriculture', 'official_document_management', 'financial_analysis', 'pharmacy', 'educational_psychology', 'statistics_and_machine_learning', 'management_accounting', 'introduction_to_law', 'computer_science', 'veterinary_pathology', 'accounting', 'fire_science', 'optometry', 'insurance_studies', 'pharmacology', 'taxation', 'trust_practice', 'geography_of_taiwan', 'physical_education', 'auditing', 'administrative_law', 'education_(profession_level)', 'economics', 'veterinary_pharmacology', 'nautical_science', 'occupational_therapy_for_psychological_disorders', 'basic_medical_science', 'macroeconomics', 'trade', 'chinese_language_and_literature', 'tve_design', 'junior_science_exam', 'junior_math_exam', 'junior_chinese_exam', 'junior_social_studies', 'tve_mathematics', 'tve_chinese_language', 'tve_natural_sciences', 'junior_chemistry', 'music', 'education', 'three_principles_of_people', 'taiwanese_hokkien' ] for task in task_list: val = load_dataset('ikala/tmmluplus', task)['validation'] dev = load_dataset('ikala/tmmluplus', task)['train'] test = load_dataset('ikala/tmmluplus', task)['test'] ``` For each dataset split ```python for row in test: print(row) break >> Dataset({ features: ['question', 'A', 'B', 'C', 'D', 'answer'], num_rows: 11 }) ``` Statistic on all four categories : STEM, Social Science, Humanities, Other | Category | Test | Dev | Validation | |----------------------------------|-------|------|------------| | STEM | 3458 | 70 | 385 | | Social Sciences | 5958 | 90 | 665 | | Humanities | 1763 | 35 | 197 | | Other (Business, Health, Misc.) | 8939 | 135 | 995 | | **Total** | 20118 | 330 | 2242 | ## Benchmark on direct prompting | model | STEM | Social Science | Humanities | Other | Average | |------------|------------|------------|------------|------------|------------| |Gemini-1.5-pro | 66.18|70.29|61.84|60.30|64.65| | [Qwen/Qwen-72B](https://huggingface.co/Qwen/Qwen-72B) | 61.12 | 71.65 | 63.00 | 61.31 |64.27| | gpt-4-0613 | 60.36 | 67.36 | 56.03 | 57.62 |60.34| | Qwen-max | 59.92 | 66.95 | 57.43 | 56.48 |60.20| | [Qwen/Qwen-72B-Chat](https://huggingface.co/Qwen/Qwen-72B-Chat) | 55.15 | 66.20 | 55.65 | 57.19 |58.55| | [Qwen/Qwen-14B](https://huggingface.co/Qwen/Qwen-14B) | 46.94 | 56.69 | 49.43 | 48.81 |50.47| | Gemini-pro | 45.38 | 57.29 | 48.80 | 48.21 |49.92| | [01-ai/Yi-34B-Chat](https://huggingface.co/01-ai/Yi-34B-Chat) | 40.24 | 56.77 | 53.99 | 47.58 |49.64| | Gemini-1.5-flash |53.47|53.42|42.99|46.56|49.11| | [Reka Flash](https://www.reka.ai/)|45.26|52.91|46.31|43.76|47.06| | [Qwen/Qwen-14B-Chat](https://huggingface.co/Qwen/Qwen-14B-Chat) | 43.86 | 53.29 | 44.78 | 45.13 |46.77| | [Qwen/Qwen1.5-14B-Chat](https://huggingface.co/Qwen/Qwen1.5-14B-Chat)|39.65|52.76|43.90|44.95|45.31| | [01-ai/Yi-6B-Chat](https://huggingface.co/01-ai/Yi-6B-Chat) | 39.62 | 50.24 | 44.44 | 44.26 |44.64| | Claude-1.3 | 42.65 | 49.33 | 42.16 | 44.14 |44.57| | [MediaTek-Research/Breeze-7B-Instruct-v0_1](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-v0_1)| 36.46 | 48.38 |45.11 |40.75 | 42.67 | | gpt-3.5-turbo-0613 | 41.56 | 46.72 | 36.73 | 42.03 |41.76| | [CausalLM/14B](https://huggingface.co/CausalLM/14B) | 39.83 | 44.50 | 39.61 | 41.97 |41.48| | [Skywork/Skywork-13B-base](https://huggingface.co/Skywork/Skywork-13B-base) | 36.93 | 47.27 | 41.04 | 40.10 |41.33| | Claude-3-opus |42.95|45.49|35.79|40.24|41.12| | [Qwen/Qwen-7B](https://huggingface.co/Qwen/Qwen-7B) | 37.53 | 45.48 | 38.09 | 38.96 |40.01| | [meta-llama/Llama-3-70b-chat-hf](https://docs.together.ai/docs/inference-models) | 34.44 | 47.02 | 37.50 |39.51 | 39.62 | | [Qwen/Qwen-7B-Chat](https://huggingface.co/Qwen/Qwen-7B-Chat) | 33.32 | 44.64 | 40.27 | 39.89 |39.53| | [vivo-ai/BlueLM-7B-Base](https://huggingface.co/vivo-ai/BlueLM-7B-Base) | 33.94 | 41.52 | 37.38 | 38.74 |37.90| | [baichuan-inc/Baichuan2-13B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat) | 29.64 | 43.73 | 37.36 | 39.88 |37.65| | [Qwen/Qwen-1_8B](https://huggingface.co/Qwen/Qwen-1_8B) | 32.65 | 38.95 | 38.34 | 35.27 |36.30| | Claude-2 | 39.65 | 39.09 | 28.59 | 37.47 |36.20| | [THUDM/chatglm3-6b](https://huggingface.co/THUDM/chatglm3-6b) | 31.05 | 39.31 | 35.64 | 35.60 |35.40| | [deepseek-ai/deepseek-llm-7b-chat](https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat) | 29.82 | 42.29 | 34.24 | 34.31 |35.17| | [CausalLM/7B](https://huggingface.co/CausalLM/7B) | 31.03 | 38.17 | 35.87 | 35.39 |35.11| | [Azure99/blossom-v3_1-mistral-7b](https://huggingface.co/Azure99/blossom-v3_1-mistral-7b) | 32.80 | 36.91 | 32.36 | 34.53 |34.15| | [google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it) | 31.89 | 35.70 | 34.00 | 33.79 | 33.84 | | [Reka Edge](https://www.reka.ai/)|30.02|39.40|31.84|32.36|33.41| | [microsoft/Orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b) | 24.69 | 39.18 | 33.60 | 31.99 |32.37| | [Qwen/Qwen-1_8B-Chat](https://huggingface.co/Qwen/Qwen-1_8B-Chat) | 26.60 | 36.36 | 31.81 | 31.96 |31.68| | [meta-llama/Llama-3-8b-chat-hf](https://docs.together.ai/docs/inference-models) | 31.52 | 34.19 | 28.91 | 31.79 | 31.60 | | [TigerResearch/tigerbot-13b-chat-v3](https://huggingface.co/TigerResearch/tigerbot-13b-chat-v3) | 24.73 | 29.63 | 25.72 | 27.22 |26.82| | [hongyin/mistral-7b-80k](https://huggingface.co/hongyin/mistral-7b-80k) | 24.26 | 23.76 | 22.56 | 24.57 |23.79| | [deepseek-ai/deepseek-llm-67b-chat](https://huggingface.co/deepseek-ai/deepseek-llm-67b-chat) | 19.10 | 26.06 | 21.51 | 21.77 |22.11| | [yentinglin/Taiwan-LLM-13B-v2.0-chat](https://huggingface.co/yentinglin/Taiwan-LLM-13B-v2.0-chat) | 18.53 | 27.65 | 17.77 | 21.49 |21.36| | [GeneZC/MiniChat-3B](https://huggingface.co/GeneZC/MiniChat-3B) | 17.66 | 23.35 | 22.71 | 20.34 |21.02| | [LinkSoul/Chinese-Llama-2-7b](https://huggingface.co/LinkSoul/Chinese-Llama-2-7b) | 16.55 | 18.39 | 12.97 | 16.13 |16.01| | [yentinglin/Taiwan-LLM-7B-v2.1-chat](https://huggingface.co/yentinglin/Taiwan-LLM-7B-v2.1-chat) | 14.99 | 16.23 | 15.00 | 16.22 |15.61| | Claude-instant-1 | 12.52 | 17.13 | 15.10 | 13.57 |14.58| | [FlagAlpha/Atom-7B](https://huggingface.co/FlagAlpha/Atom-7B) | 5.60 | 13.57 | 7.71 | 11.84 |9.68| Results via [ievals](https://github.com/iKala/ievals) ( settings : 0-shot direct answering ) # Citation ``` @article{ikala2024improved, title={An Improved Traditional Chinese Evaluation Suite for Foundation Model}, author={Tam, Zhi-Rui and Pai, Ya-Ting and Lee, Yen-Wei and Cheng, Sega and Shuai, Hong-Han}, journal={arXiv preprint arXiv:2403.01858}, year={2024} } ```

espnet/yodas

espnet

2024-06-10T02:11:54Z

176,619

110

[ "license:cc-by-3.0", "arxiv:2406.00899", "region:us" ]

[]

2024-02-10T21:00:10Z

null

--- license: cc-by-3.0 --- Updates - 2024/07/09: we also uploaded a new version of YODAS as [YODAS2](https://huggingface.co/datasets/espnet/yodas2), it provides unsegmented audios and higher sampling rate (24k) ## README This is the YODAS manual/automatic subset from our YODAS dataset, it has 369,510 hours of speech. This dataset contains audio utterances and corresponding captions (manual or automatic) from YouTube. Note that manual caption only indicates that it is uploaded by users, but not necessarily transcribed by a human For more details about YODAS dataset, please refer to [our paper](https://arxiv.org/abs/2406.00899) ## Usage: Considering the extremely large size of the entire dataset, we support two modes of dataset loadings: **standard mode**: each subset will be downloaded to the local dish before first iterating. ```python from datasets import load_dataset # Note this will take very long time to download and preprocess # you can try small subset for testing purpose ds = load_dataset('espnet/yodas', 'en000') print(next(iter(ds['train']))) ``` **streaming mode** most of the files will be streamed instead of downloaded to your local deivce. It can be used to inspect this dataset quickly. ```python from datasets import load_dataset # this streaming loading will finish quickly ds = load_dataset('espnet/yodas', 'en000', streaming=True) #{'id': '9774', 'utt_id': 'YoRjzEnRcqu-00000-00000716-00000819', 'audio': {'path': None, 'array': array([-0.009552 , -0.01086426, -0.012146 , ..., -0.01992798, # -0.01885986, -0.01074219]), 'sampling_rate': 16000}, 'text': 'There is a saying'} print(next(iter(ds['train']))) ``` ## Subsets/Shards There are 149 languages in this dataset, each language is sharded into at least 1 shard to make it easy for our processing and uploading purposes. The raw data of each shard contains 500G at most. Statistics of each shard can be found in the last section. We distinguish manual caption subset and automatic caption subset by the first digit in each shard's name. The first digit is 0 if it contains manual captions, 1 if it contains automatic captions. For example, `en000` to `en005` are the English shards containing manual subsets, and `en100` to `en127` contains the automatic subsets. ## Reference ``` @inproceedings{li2023yodas, title={Yodas: Youtube-Oriented Dataset for Audio and Speech}, author={Li, Xinjian and Takamichi, Shinnosuke and Saeki, Takaaki and Chen, William and Shiota, Sayaka and Watanabe, Shinji}, booktitle={2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)}, pages={1--8}, year={2023}, organization={IEEE} } ``` ## Contact If you have any questions, feel free to contact us at the following email address. We made sure that our dataset only consisted of videos with CC licenses during our downloading. But in case you find your video unintentionally included in our dataset and would like to delete it, you can send a delete request to the following email. Remove the parenthesis `()` from the following email address `(lixinjian)(1217)@gmail.com` ## Statistics Note that there are no overlappings across different subsets, each audio can be included in the dataset at most once. | Subset name | Hours | |------|--------| |aa000|0.171472| |ab000|0.358342| |af000|0.880497| |ak000|0.250858| |am000|0.924708| |ar000|289.707| |as000|0.548239| |ay000|0.0342722| |az000|3.8537| |ba000|0.0210556| |be000|48.1537| |bg000|46.8375| |bh000|0.0127111| |bi000|0.0125556| |bm000|0.00214722| |bn000|27.064| |bo000|0.746211| |br000|0.729914| |bs000|9.36959| |ca000|74.1909| |co000|0.0418639| |cr000|0.00584167| |cs000|167.604| |cy000|5.20017| |da000|27.4345| |de000|3063.81| |de100|4998.11| |de101|4995.08| |de102|955.389| |dz000|0.06365| |ee000|0.0411722| |el000|126.75| |en000|4999.73| |en001|5032.69| |en002|5039.9| |en003|5001.4| |en004|5054.66| |en005|4027.02| |en100|5147.07| |en101|5123.05| |en102|5117.68| |en103|5127.3| |en104|5126.33| |en105|5097.65| |en106|5131.47| |en107|5135.6| |en108|5136.84| |en109|5112.94| |en110|5109| |en111|5118.69| |en112|5122.57| |en113|5122.31| |en114|5112.36| |en115|5112.27| |en116|5123.77| |en117|5117.31| |en118|5117.94| |en119|5133.05| |en120|5127.79| |en121|5129.08| |en122|5130.22| |en123|5097.56| |en124|5116.59| |en125|5109.76| |en126|5136.21| |en127|2404.89| |eo000|12.6874| |es000|3737.86| |es100|5125.25| |es101|5130.44| |es102|5145.66| |es103|5138.26| |es104|5139.57| |es105|5138.95| |es106|2605.26| |et000|14.4129| |eu000|19.6356| |fa000|42.6734| |ff000|0.0394972| |fi000|212.899| |fj000|0.0167806| |fo000|0.183244| |fr000|2423.7| |fr100|5074.93| |fr101|5057.79| |fr102|5094.14| |fr103|3222.95| |fy000|0.0651667| |ga000|1.49252| |gd000|0.01885| |gl000|9.52575| |gn000|0.181356| |gu000|1.99355| |ha000|0.102931| |hi000|480.79| |hi100|2.74865| |ho000|0.0562194| |hr000|25.9171| |ht000|1.07494| |hu000|181.763| |hy000|1.64412| |ia000|0.0856056| |id000|1420.09| |id100|4902.79| |id101|3560.82| |ie000|0.134603| |ig000|0.086875| |ik000|0.00436667| |is000|5.07075| |it000|1454.98| |it100|4989.62| |it101|4242.87| |iu000|0.0584278| |iw000|161.373| |ja000|1094.18| |ja100|2929.94| |jv000|1.08701| |ka000|26.9727| |ki000|0.000555556| |kk000|3.72081| |kl000|0.00575556| |km000|3.98273| |kn000|2.36041| |ko000|2774.28| |ko100|5018.29| |ko101|5048.49| |ko102|5018.27| |ko103|2587.85| |ks000|0.0150444| |ku000|1.93419| |ky000|14.3917| |la000|7.26088| |lb000|0.1115| |lg000|0.00386111| |ln000|0.188739| |lo000|0.230986| |lt000|17.6507| |lv000|2.47671| |mg000|0.169653| |mi000|1.10089| |mk000|5.54236| |ml000|13.2386| |mn000|2.0232| |mr000|7.11602| |ms000|28.0219| |my000|2.35663| |na000|0.0397056| |nd000|0.00111111| |ne000|2.34936| |nl000|413.044| |nl100|2490.13| |no000|129.183| |nv000|0.00319444| |oc000|0.166108| |om000|0.148478| |or000|0.421436| |pa000|1.58188| |pl000|757.986| |ps000|0.9871| |pt000|1631.44| |pt100|5044.57| |pt101|5038.33| |pt102|5041.59| |pt103|3553.28| |qu000|0.748772| |rm000|0.192933| |rn000|0.00401111| |ro000|99.9175| |ru000|4968.37| |ru001|627.679| |ru100|5098.3| |ru101|5098| |ru102|5119.43| |ru103|5107.29| |ru104|5121.73| |ru105|5088.05| |ru106|3393.44| |rw000|0.640825| |sa000|0.354139| |sc000|0.00801111| |sd000|0.0768722| |sg000|0.000472222| |sh000|0.250914| |si000|4.2634| |sk000|30.0155| |sl000|22.9366| |sm000|0.102333| |sn000|0.0134722| |so000|3.36819| |sq000|3.48276| |sr000|15.2849| |st000|0.00324167| |su000|0.0404639| |sv000|127.411| |sw000|1.93409| |ta000|59.4805| |te000|5.66794| |tg000|0.272386| |th000|497.14| |th100|1.87429| |ti000|0.343897| |tk000|0.0651806| |tn000|0.112181| |to000|0.000555556| |tr000|588.698| |tr100|4067.68| |ts000|0.00111111| |tt000|0.0441194| |ug000|0.0905| |uk000|396.598| |uk100|450.411| |ur000|22.4373| |uz000|5.29325| |ve000|0.00355278| |vi000|779.854| |vi100|4963.77| |vi101|4239.37| |vo000|0.209436| |wo000|0.0801528| |xh000|0.126628| |yi000|0.0810111| |yo000|0.322206| |zh000|299.368| |zu000|0.139931|

LDJnr/Capybara

LDJnr

2024-06-07T20:15:36Z

529

237

[ "task_categories:question-answering", "task_categories:text-generation", "language:en", "license:apache-2.0", "size_categories:10K<n<100K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "Physics", "Biology", "Math", "Chemistry", "Culture", "Logic", "Roleplay" ]

[ "conversational", "question-answering", "text-generation" ]

2023-12-16T05:45:37Z

null

--- license: apache-2.0 task_categories: - conversational - question-answering - text-generation language: - en tags: - Physics - Biology - Math - Chemistry - Culture - Logic - Roleplay pretty_name: LessWrong-Amplify-Instruct size_categories: - 10K<n<100K --- ## This is the Official Capybara dataset. Over 10,000 multi-turn examples. Capybara is the culmination of insights derived from synthesis techniques like Evol-instruct (used for WizardLM), Alpaca, Orca, Vicuna, Lamini, FLASK and others. The single-turn seeds used to initiate the Amplify-Instruct synthesis of conversations are mostly based on datasets that i've personally vetted extensively, and are often highly regarded for their diversity and demonstration of logical robustness and prose, such as Airoboros, Know logic, EverythingLM, GPTeacher and even entirely new seed instructions derived from different sources, including certain in-house multi-turn datasets like Dove and Verified-Camel(A successor to Puffin). The multi-turn synthetic conversation generation method is what i'm calling Amplify-Instruct, and the first resulting dataset using this method is called Capybara. This dataset has a strong focus on information diversity across a wide range of domains, and multi-turn conversations that strongly emphasize reasoning, logic and extrapolation about a wide range of subjects, also many great examples of conversations delving into obscure sub-topics and rabbit holes across pop-culture and STEM, while also maintaining natural prose. While performing great in it's current state, the current dataset used for fine-tuning is entirely contained within 20K training examples, this is 10 times smaller than many similar performing datasets, this is signficant when it comes to scaling implications once I decide to scale the use of Amplify-Instruct to significantly more examples. - Most tokens contained in this dataset are newly synthesized and did not exist prior online. - This leverages the Amplify-Instruct method(paper coming soon) to grow thousands of high-quality single-turn seeds into advanced and in-depth multi-turn conversations. - Average context length per conversation is over 1,000 tokens and 3 turns or more per example (most instruction/chat datasets on HF for fine-tuning are only 1 turn) - Each conversation is optimized to amplify the natural raw knowledge capabilities of the model, as well as delving deep into obscure and advanced topics. - Aggresively filtered to remove any and all possible examples of overt moralizing/alignment, and common undesirable behaviours such as "as an AI language model" and "September 2021" and "I don't have personal beliefs" ## Benchmarks. - Resulting benchmarks are available on HF Leaderboard, and other benchmarks done as well such as AGIEval, Bigbench and GPT4All. - (The only Capybara model available on all of these benchmarks including HF leaderboard is Capybara V1, trained on Llama-2) - The below benchmarks are compared against fine-tunes also done on Llama-2. ![Capybara](https://i.imgur.com/OpajtNJ.jpeg) ![Capybara](https://i.imgur.com/daIZn6n.jpeg) ## Quality filtering and cleaning. - Extensive measures were done to filter out any conversations that contained even a single instance of overt AI moralizing/alignment, such as "As an AI language model" and common undesirable behaviours such as conversations that include "September 2021" and "I don't have personal beliefs" and other phrases I've found to be highly correlated with undesirable responses and conversation paths. ## Thank you to those of you that have indirectly contributed! While most of the tokens within Capybara are newly synthsized and part of datasets like Puffin/Dove, we would like to credit the single-turn datasets we leveraged as seeds, which were used to generate the multi-turn data. The datasets shown in green below are datasets that we sampled from to curate seeds that are used during Amplify-Instruct synthesis for this project, however, most of the tokens in capybara within those given sections are novel tokens not present in any of the seed datasets. Datasets in Blue are in-house curations that previously existed prior to Capybara, and were now used as seeds for Capybara. ![Capybara](https://i.imgur.com/yB58OoD.jpeg) ## Dataset contamination. We have checked the capybara dataset for contamination for several of the most popular benchmarks and can confirm that there is no contaminaton found besides MT-bench which is now cleaned out. We leveraged minhash to check for 100%, 99%, 98% and 97% similarity matches between our data and the questions and answers in benchmarks, we found no exact matches, nor did we find any matches down to the 97% similarity level. The following are benchmarks we checked for contamination against our dataset: - HumanEval - AGIEval - TruthfulQA - MMLU - GPT4All *Newly cleaned out as of 12/15/2023 - MT-bench ## Credits: During the curation process, there can be some relatively arduos steps when it comes to actually executing on the best experimentation or concepts for how to filter examples out. Luckily there is folks over at Nous Research that helped with expediting these processes, big thank you to J-Supha specifically for making these types of significant contributions. ## Example Outputs from the Llama-2 7B model trained on this dataset: ![Capybara](https://img001.prntscr.com/file/img001/T9yYxR1xQSaK_UGdy3t2Cw.png) ![Capybara](https://img001.prntscr.com/file/img001/DQXqmKbsQQOIcgny1eoGNA.png) ![Capybara](https://img001.prntscr.com/file/img001/85X3L9ZxTsOKo3fUQ7GRVA.png) ## Future Plans & How you can help This is a relatively early build amongst the grand plans for the future of what I plan to work on! In the near future we plan on leveraging the help of domain specific expert volunteers to eliminate any mathematically/verifiably incorrect answers from training curations of different types of datasets. If you have at-least a bachelors in mathematics, physics, biology or chemistry and would like to volunteer even just 30 minutes of your expertise time, please contact LDJ on discord! Citation: ``` @article{daniele2023amplify-instruct, title={Amplify-Instruct: Synthetically Generated Diverse Multi-turn Conversations for efficient LLM Training.}, author={Daniele, Luigi and Suphavadeeprasit}, journal={arXiv preprint arXiv:(coming soon)}, url={https://huggingface.co/datasets/LDJnr/Capybara}, year={2023} } ```

Lin-Chen/ShareGPT4V

Lin-Chen

2024-06-06T13:52:04Z

1,098

285

[ "task_categories:visual-question-answering", "task_categories:question-answering", "language:en", "license:cc-by-nc-4.0", "size_categories:1M<n<10M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2311.12793", "region:us" ]

[ "visual-question-answering", "question-answering" ]

2023-11-20T09:29:12Z

null

--- license: cc-by-nc-4.0 task_categories: - visual-question-answering - question-answering language: - en pretty_name: ShareGPT4V Captions 1.2M Dataset Card size_categories: - 1M<n configs: - config_name: ShareGPT4V data_files: sharegpt4v_instruct_gpt4-vision_cap100k.json - config_name: ShareGPT4V-PT data_files: share-captioner_coco_lcs_sam_1246k_1107.json --- # News **[2024/5/8]** We released **[ShareGPT4Video](https://sharegpt4video.github.io/)**, a large-scale video-caption dataset, with **40K** captions annotated by GPT4V and **4.8M** captions annotated by our ShareCaptioner-Video. The total videos last with **300** hours and **3000** hours separately! # ShareGPT4V 1.2M Dataset Card ## Dataset details **Dataset type:** ShareGPT4V Captions 1.2M is a set of GPT4-Vision-powered multi-modal captions data. It is constructed to enhance modality alignment and fine-grained visual concept perception in Large Multi-Modal Models (LMMs) during both the pre-training and supervised fine-tuning stages. This advancement aims to bring LMMs towards GPT4-Vision capabilities. * sharegpt4v_instruct_gpt4-vision_cap100k.json is generated by GPT4-Vision (ShareGPT4V). * share-captioner_coco_lcs_sam_1246k_1107.json is generated by our Share-Captioner trained on GPT4-Vision-generated data (ShareGPT4V-PT). * sharegpt4v_mix665k_cap23k_coco-ap9k_lcs3k_sam9k_div2k.json is curated from sharegpt4v_instruct_gpt4-vision_cap100k.json for the supervised fine-tuning stage. **Dataset date:** ShareGPT4V Captions 1.2M was collected in 11.07 2023. **Paper or resources for more information:** [[Project](https://ShareGPT4V.github.io/)] [[Paper](https://huggingface.co/papers/2311.12793)] [[Code](https://github.com/ShareGPT4Omni/ShareGPT4V)] **License:** Attribution-NonCommercial 4.0 International It should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use ## Intended use **Primary intended uses:** The primary use of ShareGPT4V Captions 1.2M is research on large multimodal models and chatbots. **Primary intended users:** The primary intended users of this dataset are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.

osunlp/Multimodal-Mind2Web

osunlp

2024-06-05T05:12:21Z

3,318

68

[ "language:en", "license:openrail", "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2401.01614", "region:us", "web agent", "multimodal" ]

[]

2024-03-18T03:32:11Z

2

--- license: openrail language: - en tags: - web agent - multimodal dataset_info: features: - name: action_uid dtype: string - name: raw_html dtype: string - name: cleaned_html dtype: string - name: operation dtype: string - name: pos_candidates sequence: string - name: neg_candidates sequence: string - name: website dtype: string - name: domain dtype: string - name: subdomain dtype: string - name: annotation_id dtype: string - name: confirmed_task dtype: string - name: screenshot dtype: image - name: action_reprs sequence: string - name: target_action_index dtype: string - name: target_action_reprs dtype: string splits: - name: test_website num_bytes: 1589513606.713 num_examples: 1019 - name: test_task num_bytes: 2004628575.972 num_examples: 1339 - name: test_domain num_bytes: 5128899015.440001 num_examples: 4060 - name: train num_bytes: 13439470200.25 num_examples: 7775 download_size: 4014045168 dataset_size: 22162511398.375 --- ## Dataset Description - **Homepage:** https://osu-nlp-group.github.io/SeeAct/ - **Repository:** https://github.com/OSU-NLP-Group/SeeAct - **Paper:** https://arxiv.org/abs/2401.01614 - **Point of Contact:** [Boyuan Zheng](mailto:[email protected]) ### Dataset Summary Multimodal-Mind2Web is the multimodal version of [Mind2Web](https://osu-nlp-group.github.io/Mind2Web/), a dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. In this dataset, we align each HTML document in the dataset with its corresponding webpage screenshot image from the Mind2Web raw dump. This multimodal version addresses the inconvenience of loading images from the ~300GB Mind2Web Raw Dump. ## Dataset Structure ### Data Splits - train: 7775 actions from 1009 tasks. - test_task: 1339 actions from 177 tasks. Tasks from the same website are seen during training. - test_website: 1019 actions from 142 tasks. Websites are not seen during training. - test_domain: 4060 actions from 694 tasks. Entire domains are not seen during training. The **_train_** set may include some screenshot images not properly rendered caused by rendering issues during Mind2Web annotation. The three **_test splits (test_task, test_website, test_domain)_** have undergone human verification to confirm element visibility and correct rendering for action prediction. ### Data Fields Each line in the dataset is an action consisting of screenshot image, HTML text and other fields required for action prediction, for the convenience of inference. - "annotation_id" (str): unique id for each task - "website" (str): website name - "domain" (str): website domain - "subdomain" (str): website subdomain - "confirmed_task" (str): task description - **"screenshot" (str): path to the webpage screenshot image corresponding to the HTML.** - "action_uid" (str): unique id for each action (step) - "raw_html" (str): raw html of the page before the action is performed - "cleaned_html" (str): cleaned html of the page before the action is performed - "operation" (dict): operation to perform - "op" (str): operation type, one of CLICK, TYPE, SELECT - "original_op" (str): original operation type, contain additional HOVER and ENTER that are mapped to CLICK, not used - "value" (str): optional value for the operation, e.g., text to type, option to select - "pos_candidates" (list[dict]): ground truth elements. Here we only include positive elements that exist in "cleaned_html" after our preprocessing, so "pos_candidates" might be empty. The original labeled element can always be found in the "raw_html". - "tag" (str): tag of the element - "is_original_target" (bool): whether the element is the original target labeled by the annotator - "is_top_level_target" (bool): whether the element is a top level target find by our algorithm. please see the paper for more details. - "backend_node_id" (str): unique id for the element - "attributes" (str): serialized attributes of the element, use `json.loads` to convert back to dict - "neg_candidates" (list[dict]): other candidate elements in the page after preprocessing, has similar structure as "pos_candidates" - "action_reprs" (list[str]): human readable string representation of the action sequence - "target_action_index" (str): the index of the target action in the action sequence - "target_action_reprs" (str): human readable string representation of the target action ### Disclaimer This dataset was collected and released solely for research purposes, with the goal of making the web more accessible via language technologies. The authors are strongly against any potential harmful use of the data or technology to any party. ### Citation Information ``` @article{zheng2024seeact, title={GPT-4V(ision) is a Generalist Web Agent, if Grounded}, author={Boyuan Zheng and Boyu Gou and Jihyung Kil and Huan Sun and Yu Su}, booktitle={Forty-first International Conference on Machine Learning}, year={2024}, url={https://openreview.net/forum?id=piecKJ2DlB}, } @inproceedings{deng2023mindweb, title={Mind2Web: Towards a Generalist Agent for the Web}, author={Xiang Deng and Yu Gu and Boyuan Zheng and Shijie Chen and Samuel Stevens and Boshi Wang and Huan Sun and Yu Su}, booktitle={Thirty-seventh Conference on Neural Information Processing Systems}, year={2023}, url={https://openreview.net/forum?id=kiYqbO3wqw} } ```