Datasets:

cfahlgren1

/

hub-stats

like 13

671c2cc275a642a7c16a7c21

microsoft/orca-agentinstruct-1M-v1

microsoft

{"language": ["en"], "license": "cdla-permissive-2.0", "size_categories": ["1M<n<10M"], "task_categories": ["question-answering"], "dataset_info": {"features": [{"name": "messages", "dtype": "string"}], "splits": [{"name": "creative_content", "num_bytes": 288747542, "num_examples": 50000}, {"name": "text_modification", "num_bytes": 346421282, "num_examples": 50000}, {"name": "struct2text_flow", "num_bytes": 251920604, "num_examples": 50000}, {"name": "rc", "num_bytes": 282448904, "num_examples": 50000}, {"name": "rag", "num_bytes": 421188673, "num_examples": 50000}, {"name": "text_extraction", "num_bytes": 312246895, "num_examples": 50000}, {"name": "mcq", "num_bytes": 230459938, "num_examples": 99986}, {"name": "follow_up", "num_bytes": 881311205, "num_examples": 99054}, {"name": "analytical_reasoning", "num_bytes": 100724491, "num_examples": 25000}, {"name": "fermi", "num_bytes": 78109959, "num_examples": 25000}, {"name": "fs_cot_flow", "num_bytes": 109007740, "num_examples": 25000}, {"name": "code_", "num_bytes": 617418962, "num_examples": 100000}, {"name": "brain_teaser", "num_bytes": 124523402, "num_examples": 50000}, {"name": "text_classification", "num_bytes": 151217275, "num_examples": 50000}, {"name": "open_domain_qa", "num_bytes": 616935002, "num_examples": 272370}], "download_size": 2210440144, "dataset_size": 4812681874}, "configs": [{"config_name": "default", "data_files": [{"split": "creative_content", "path": "data/creative_content-*"}, {"split": "text_modification", "path": "data/text_modification-*"}, {"split": "struct2text_flow", "path": "data/struct2text_flow-*"}, {"split": "rc", "path": "data/rc-*"}, {"split": "rag", "path": "data/rag-*"}, {"split": "text_extraction", "path": "data/text_extraction-*"}, {"split": "mcq", "path": "data/mcq-*"}, {"split": "follow_up", "path": "data/follow_up-*"}, {"split": "analytical_reasoning", "path": "data/analytical_reasoning-*"}, {"split": "fermi", "path": "data/fermi-*"}, {"split": "fs_cot_flow", "path": "data/fs_cot_flow-*"}, {"split": "code_", "path": "data/code_-*"}, {"split": "brain_teaser", "path": "data/brain_teaser-*"}, {"split": "text_classification", "path": "data/text_classification-*"}, {"split": "open_domain_qa", "path": "data/open_domain_qa-*"}]}]}

False

2024-11-01T00:14:29.000Z

86d609183249ff8037eae33d76ebca3af9390ea8

Dataset Card This dataset is a fully synthetic set of instruction pairs where both the prompts and the responses have been synthetically generated, using the AgentInstruct framework. AgentInstruct is an extensible agentic framework for synthetic data generation. This dataset contains ~1 million instruction pairs generated by the AgentInstruct, using only raw text content publicly avialble on the Web as seeds. The data covers different capabilities, such as text editing… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/orca-agentinstruct-1M-v1.

2024-10-25T23:41:54.000Z

67335bb8f014ee49558ef3fe

PleIAs/common_corpus

PleIAs

{"language": ["en", "fr", "de", "it", "pt", "nl", "es"], "pretty_name": "Common Corpus", "size_categories": ["n>1T"], "task_categories": ["text-generation"], "tags": ["legal", "finance", "literature", "science", "code"]}

False

2024-11-15T13:43:29.000Z

ea0bf929e48db9a0155759527acb8b6856178c88

Common Corpus Common Corpus is the largest open and permissible licensed text dataset, comprising over 2 trillion tokens (2,003,039,184,047 tokens). It is a diverse dataset, consisting of books, newspapers, scientific articles, government and legal documents, code, and more. Common Corpus differs from existing open datasets in that it is: Truly Open: contains only data that is permissively licensed Multilingual: mostly representing English and French data, but contains data… See the full description on the dataset page: https://huggingface.co/datasets/PleIAs/common_corpus.

2024-11-12T13:44:24.000Z

63990f21cc50af73d29ecfa3

fka/awesome-chatgpt-prompts

fka

{"license": "cc0-1.0", "tags": ["ChatGPT"], "task_categories": ["question-answering"], "size_categories": ["100K<n<1M"]}

False

2024-09-03T21:28:41.000Z

459a66186f8f83020117b8acc5ff5af69fc95b45

🧠 Awesome ChatGPT Prompts [CSV dataset] This is a Dataset Repository of Awesome ChatGPT Prompts View All Prompts on GitHub License CC-0

2022-12-13T23:47:45.000Z

672e43b562371d59e7202334

OpenCoder-LLM/opc-sft-stage1

OpenCoder-LLM

{"configs": [{"config_name": "filtered_infinity_instruct", "data_files": [{"split": "train", "path": "data/filtered_infinity_instruct-*"}]}, {"config_name": "largescale_diverse_instruct", "data_files": [{"split": "train", "path": "data/largescale_diverse_instruct-*"}]}, {"config_name": "realuser_instruct", "data_files": [{"split": "train", "path": "data/realuser_instruct-*"}]}]}

False

2024-11-15T11:42:22.000Z

7fa66a593715cbd8de40a635697d69e9a86516a4

2024-11-08T17:00:37.000Z

67181a27dfa0b095f0902d33

qq8933/OpenLongCoT-Pretrain

qq8933

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 269352240, "num_examples": 102906}], "download_size": 64709509, "dataset_size": 269352240}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

False

2024-10-28T13:50:37.000Z

40562378be9f86728440a0fb44f07ba2bdc03646

Please cite me if this dataset is helpful for you!🥰 @article{zhang2024llama, title={LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning}, author={Zhang, Di and Wu, Jianbo and Lei, Jingdi and Che, Tong and Li, Jiatong and Xie, Tong and Huang, Xiaoshui and Zhang, Shufei and Pavone, Marco and Li, Yuqiang and others}, journal={arXiv preprint arXiv:2410.02884}, year={2024} } @article{zhang2024accessing, title={Accessing GPT-4 level Mathematical Olympiad… See the full description on the dataset page: https://huggingface.co/datasets/qq8933/OpenLongCoT-Pretrain.

2024-10-22T21:33:27.000Z

672f9b7ed7f4171f3751ffb3

OpenCoder-LLM/fineweb-code-corpus

OpenCoder-LLM

{"license": "mit", "dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "tag", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "file_path", "dtype": "string"}, {"name": "dump", "dtype": "string"}, {"name": "file_size_in_byte", "dtype": "int64"}, {"name": "line_count", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 254927419643, "num_examples": 100920235}], "download_size": 147948949488, "dataset_size": 254927419643}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

False

2024-11-14T16:39:12.000Z

1fc58728aeaa36aabf3de988b265cd89ff6a656c

This code-related data from Fineweb was specifically used in OpenCoder pre-training. We employ fastText in three iterative rounds to recall a final dataset of 55B code and math-related data. You can find math-related data at OpenCoder-LLM/fineweb-math-corpus. Citation @inproceedings{Huang2024OpenCoderTO, title={OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models}, author={Siming Huang and Tianhao Cheng and Jason Klein Liu and Jiaran Hao and Liuyihan Song… See the full description on the dataset page: https://huggingface.co/datasets/OpenCoder-LLM/fineweb-code-corpus.

2024-11-09T17:27:26.000Z

672e4b6b741fa21478bd7bc3

OpenCoder-LLM/opc-sft-stage2

OpenCoder-LLM

{"configs": [{"config_name": "educational_instruct", "data_files": [{"split": "train", "path": "data/educational_instruct-*"}]}, {"config_name": "evol_instruct", "data_files": [{"split": "train", "path": "data/evol_instruct-*"}]}, {"config_name": "mceval_instruct", "data_files": [{"split": "train", "path": "data/mceval_instruct-*"}]}, {"config_name": "package_instruct", "data_files": [{"split": "train", "path": "data/package_instruct-*"}]}]}

False

2024-11-15T12:34:54.000Z

703054242f4665d8949248f8893a0bfe91e92490

2024-11-08T17:33:31.000Z

66f5a5d9763d438dab13f188

Spawning/PD12M

Spawning

{"language": ["en"], "pretty_name": "PD12M", "license": "cdla-permissive-2.0", "tags": ["image"]}

False

2024-10-31T15:25:49.000Z

4fd5d707a72aad71bd88c7e7bc5df2ae5e0d6c53

PD12M Summary At 12.4 million image-caption pairs, PD12M is the largest public domain image-text dataset to date, with sufficient size to train foundation models while minimizing copyright concerns. Through the Source.Plus platform, we also introduce novel, community-driven dataset governance mechanisms that reduce harm and support reproducibility over time. Jordan Meyer Nicholas Padgett Cullen Miller Laura Exline Paper Datasheet Project… See the full description on the dataset page: https://huggingface.co/datasets/Spawning/PD12M.

2024-09-26T18:20:09.000Z

6735ab5ada0aa544a31cb334

Laxhar/noob-wiki

Laxhar

{"license": "apache-2.0", "task_categories": ["text-to-image"], "language": ["en"], "tags": ["wiki"]}

False

2024-11-14T09:38:13.000Z

929c972dcc8aeecde42b7cd8931afe82cd864424

Noob SDXL Wiki This is the WIKI database for Noob SDXL Models.

2024-11-14T07:48:42.000Z

67333902d741f752b8483d42

OpenCoder-LLM/opc-annealing-corpus

OpenCoder-LLM

{"configs": [{"config_name": "synthetic_code_snippet", "data_files": [{"split": "train", "path": "synthetic_code_snippet/*"}]}, {"config_name": "synthetic_qa", "data_files": [{"split": "train", "path": "synthetic_qa/*"}]}, {"config_name": "algorithmic_corpus", "data_files": [{"split": "train", "path": "algorithmic_corpus/*"}]}], "license": "odc-by"}

False

2024-11-15T02:20:23.000Z

8bcc9520a15cbacdff9608a4ddc90f1680a2f1e7

This corpus is an additional component incorporated into OpenCoder during the annealing phase, beyond the original distribution: algorithmic_corpus: Algorithm-related code sampled from The Stack v2. synthetic_code_snippet: High-quality code snippets generated by rewriting algorithmic_corpus as seeds. synthetic_qa: High-quality Q&A pairs generated by adapting algorithmic_corpus as seeds. Our ablation experiments validated the effectiveness of this batch of synthetic data.… See the full description on the dataset page: https://huggingface.co/datasets/OpenCoder-LLM/opc-annealing-corpus.

2024-11-12T11:16:18.000Z

670d4bb8207a1458e88ab1f6

gretelai/gretel-pii-masking-en-v1

gretelai

{"license": "apache-2.0", "task_categories": ["text-classification", "text-generation"], "language": ["en"], "tags": ["synthetic", "domain-specific", "text", "NER"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]}

False

2024-11-15T16:15:50.000Z

db324d63d944753b4d636296e77831af51d99355

Gretel Synthetic Domain-Specific Documents Dataset (English) This dataset is a synthetically generated collection of documents enriched with Personally Identifiable Information (PII) and Protected Health Information (PHI) entities spanning multiple domains. Created using Gretel Navigator with mistral-nemo-2407 as the backend model, it is specifically designed for fine-tuning Gliner models. The dataset contains document passages featuring PII/PHI entities from a wide range… See the full description on the dataset page: https://huggingface.co/datasets/gretelai/gretel-pii-masking-en-v1.

2024-10-14T16:50:00.000Z

66ce3cb04c3b13931e5591fa

Salesforce/blip3-kale

Salesforce

{"license": "apache-2.0", "task_categories": ["image-to-text"], "language": ["en"], "pretty_name": "KALE", "size_categories": ["100M<n<1B"], "configs": [{"config_name": "core", "data_files": [{"split": "train", "path": "data_core_set/*.parquet"}]}, {"config_name": "full", "data_files": [{"split": "train", "path": "data_full_set/*.parquet"}]}]}

False

2024-11-14T23:39:47.000Z

adbc857b863005dbed15596d49410ebcb0392922

🥬 BLIP3-KALE:Knowledge Augmented Large-scale Dense Captions BLIP3-KALE is an open-source dataset of 218 million image-text pairs, featuring knowledge-augmented dense captions combining web-scale knowledge with detailed image descriptions. Paper: [To be added] Uses BLIP3-KALE is designed to facilitate research in multimodal pretraining. The dataset can be used for training large multimodal models that require factually grounded, dense image captions. It has already been an… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/blip3-kale.

2024-08-27T20:53:04.000Z

67214aee41fba8f8b985b247

wyu1/Leopard-Instruct

wyu1

{"configs": [{"config_name": "arxiv", "data_files": [{"split": "train", "path": "arxiv/*"}]}, {"config_name": "chartgemma", "data_files": [{"split": "train", "path": "chartgemma/*"}]}, {"config_name": "chartqa", "data_files": [{"split": "train", "path": "chartqa/*"}]}, {"config_name": "dude", "data_files": [{"split": "train", "path": "dude/*"}]}, {"config_name": "dvqa", "data_files": [{"split": "train", "path": "dvqa/*"}]}, {"config_name": "figureqa", "data_files": [{"split": "train", "path": "figureqa/*"}]}, {"config_name": "iconqa", "data_files": [{"split": "train", "path": "iconqa/*"}]}, {"config_name": "infographics", "data_files": [{"split": "train", "path": "infographics/*"}]}, {"config_name": "llavar", "data_files": [{"split": "train", "path": "llavar/*"}]}, {"config_name": "mapqa", "data_files": [{"split": "train", "path": "mapqa/*"}]}, {"config_name": "mathv360k", "data_files": [{"split": "train", "path": "mathv360k/*"}]}, {"config_name": "mind2web", "data_files": [{"split": "train", "path": "mind2web/*"}]}, {"config_name": "monkey", "data_files": [{"split": "train", "path": "monkey/*"}]}, {"config_name": "mpdocvqa", "data_files": [{"split": "train", "path": "mpdocvqa/*"}]}, {"config_name": "mplugdocreason", "data_files": [{"split": "train", "path": "mplugdocreason/*"}]}, {"config_name": "multichartqa", "data_files": [{"split": "train", "path": "multi_chartqa/*"}]}, {"config_name": "multihiertt", "data_files": [{"split": "train", "path": "multihiertt/*"}]}, {"config_name": "multitab", "data_files": [{"split": "train", "path": "multitab/*"}]}, {"config_name": "omniact", "data_files": [{"split": "train", "path": "omniact/*"}]}, {"config_name": "pew_chart", "data_files": [{"split": "train", "path": "pew_chart/*"}]}, {"config_name": "rico", "data_files": [{"split": "train", "path": "rico/*"}]}, {"config_name": "slidesgeneration", "data_files": [{"split": "train", "path": "slidesgeneration/*"}]}, {"config_name": "slideshare", "data_files": [{"split": "train", "path": "slideshare/*"}]}, {"config_name": "slidevqa", "data_files": [{"split": "train", "path": "slidevqa/*"}]}, {"config_name": "docvqa", "data_files": [{"split": "train", "path": "spdocvqa/*"}]}, {"config_name": "tab_entity", "data_files": [{"split": "train", "path": "tab_entity/*"}]}, {"config_name": "tabmwp", "data_files": [{"split": "train", "path": "tabmwp/*"}]}, {"config_name": "tat_dqa", "data_files": [{"split": "train", "path": "tat_dqa/*"}]}, {"config_name": "website_screenshots", "data_files": [{"split": "train", "path": "website_screenshots/*"}]}, {"config_name": "webui", "data_files": [{"split": "train", "path": "webui/*"}]}, {"config_name": "webvision", "data_files": [{"split": "train", "path": "webvision/*"}]}], "license": "apache-2.0", "language": ["en"], "tags": ["multimodal", "instruction-following", "multi-image", "lmm", "vlm", "mllm"], "size_categories": ["100K<n<1M"]}

False

2024-11-08T00:12:25.000Z

93317b272c5a9d9c0417fa6ea6e2be89ac9215ea

Leopard-Instruct Paper | Github | Models-LLaVA | Models-Idefics2 Summaries Leopard-Instruct is a large instruction-tuning dataset, comprising 925K instances, with 739K specifically designed for text-rich, multiimage scenarios. It's been used to train Leopard-LLaVA [checkpoint] and Leopard-Idefics2 [checkpoint]. Loading dataset to load the dataset without automatically downloading and process the images (Please run the following codes with… See the full description on the dataset page: https://huggingface.co/datasets/wyu1/Leopard-Instruct.

2024-10-29T20:51:58.000Z

67323181adc3df46516a0611

nyuuzyou/suno

nyuuzyou

{"pretty_name": "Suno Music Generation Dataset", "size_categories": ["100K<n<1M"], "task_categories": ["audio-classification", "text-to-audio"], "annotations_creators": ["found"], "language": ["en", "ja", "multilingual"], "license": "cc0-1.0", "multilinguality": ["multilingual"], "source_datasets": ["original"], "tags": ["audio", "video", "image", "text"]}

False

2024-11-11T16:33:16.000Z

6d2c778c9fcad49a435d1eec1d4711e2985185e0

Dataset Card for Suno.ai Music Generation Dataset Summary This dataset contains metadata for 659,788 AI-generated songs from the suno.com platform, a service that generates music using AI. The songs were generated using search queries from the dwyl/english-words wordlist. Languages The dataset is multilingual with English as the primary language: English (en): Primary language for metadata and most lyrics Japanese (ja): Present in some song lyrics… See the full description on the dataset page: https://huggingface.co/datasets/nyuuzyou/suno.

2024-11-11T16:32:01.000Z

621ffdd236468d709f184284

wikimedia/wikipedia

wikimedia

{"language": ["ab", "ace", "ady", "af", "alt", "am", "ami", "an", "ang", "anp", "ar", "arc", "ary", "arz", "as", "ast", "atj", "av", "avk", "awa", "ay", "az", "azb", "ba", "ban", "bar", "bbc", "bcl", "be", "bg", "bh", "bi", "bjn", "blk", "bm", "bn", "bo", "bpy", "br", "bs", "bug", "bxr", "ca", "cbk", "cdo", "ce", "ceb", "ch", "chr", "chy", "ckb", "co", "cr", "crh", "cs", "csb", "cu", "cv", "cy", "da", "dag", "de", "dga", "din", "diq", "dsb", "dty", "dv", "dz", "ee", "el", "eml", "en", "eo", "es", "et", "eu", "ext", "fa", "fat", "ff", "fi", "fj", "fo", "fon", "fr", "frp", "frr", "fur", "fy", "ga", "gag", "gan", "gcr", "gd", "gl", "glk", "gn", "gom", "gor", "got", "gpe", "gsw", "gu", "guc", "gur", "guw", "gv", "ha", "hak", "haw", "hbs", "he", "hi", "hif", "hr", "hsb", "ht", "hu", "hy", "hyw", "ia", "id", "ie", "ig", "ik", "ilo", "inh", "io", "is", "it", "iu", "ja", "jam", "jbo", "jv", "ka", "kaa", "kab", "kbd", "kbp", "kcg", "kg", "ki", "kk", "kl", "km", "kn", "ko", "koi", "krc", "ks", "ksh", "ku", "kv", "kw", "ky", "la", "lad", "lb", "lbe", "lez", "lfn", "lg", "li", "lij", "lld", "lmo", "ln", "lo", "lt", "ltg", "lv", "lzh", "mad", "mai", "map", "mdf", "mg", "mhr", "mi", "min", "mk", "ml", "mn", "mni", "mnw", "mr", "mrj", "ms", "mt", "mwl", "my", "myv", "mzn", "nah", "nan", "nap", "nds", "ne", "new", "nia", "nl", "nn", "no", "nov", "nqo", "nrf", "nso", "nv", "ny", "oc", "olo", "om", "or", "os", "pa", "pag", "pam", "pap", "pcd", "pcm", "pdc", "pfl", "pi", "pih", "pl", "pms", "pnb", "pnt", "ps", "pt", "pwn", "qu", "rm", "rmy", "rn", "ro", "ru", "rue", "rup", "rw", "sa", "sah", "sat", "sc", "scn", "sco", "sd", "se", "sg", "sgs", "shi", "shn", "si", "sk", "skr", "sl", "sm", "smn", "sn", "so", "sq", "sr", "srn", "ss", "st", "stq", "su", "sv", "sw", "szl", "szy", "ta", "tay", "tcy", "te", "tet", "tg", "th", "ti", "tk", "tl", "tly", "tn", "to", "tpi", "tr", "trv", "ts", "tt", "tum", "tw", "ty", "tyv", "udm", "ug", "uk", "ur", "uz", "ve", "vec", "vep", "vi", "vls", "vo", 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False

2024-01-09T09:40:51.000Z

b04c8d1ceb2f5cd4588862100d08de323dccfbaa

Dataset Card for Wikimedia Wikipedia Dataset Summary Wikipedia dataset containing cleaned articles of all languages. The dataset is built from the Wikipedia dumps (https://dumps.wikimedia.org/) with one subset per language, each containing a single train split. Each example contains the content of one full Wikipedia article with cleaning to strip markdown and unwanted sections (references, etc.). All language subsets have already been processed for recent dump… See the full description on the dataset page: https://huggingface.co/datasets/wikimedia/wikipedia.

2022-03-02T23:29:22.000Z

66ec6e67127751a231f9ff81

Marqo/amazon-products-eval

Marqo

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False

2024-11-11T22:43:05.000Z

2689d37f8a3634629e1c4fbb002f0d0285495883

Marqo Ecommerce Embedding Models In this work, we introduce the AmazonProducts-3m dataset for evaluation. This dataset comes with the release of our state-of-the-art embedding models for ecommerce products: Marqo-Ecommerce-B and Marqo-Ecommerce-L. Released Content: Marqo-Ecommerce-B and Marqo-Ecommerce-L embedding models GoogleShopping-1m and AmazonProducts-3m for evaluation Evaluation Code The benchmarking results show that the… See the full description on the dataset page: https://huggingface.co/datasets/Marqo/amazon-products-eval.

2024-09-19T18:33:11.000Z

66ec597416362f8e973776bb

Marqo/google-shopping-general-eval

Marqo

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False

2024-11-11T22:42:54.000Z

c38116449e89d18c6690e7f3917c71c496c76c9f

Marqo Ecommerce Embedding Models In this work, we introduce the GoogleShopping-1m dataset for evaluation. This dataset comes with the release of our state-of-the-art embedding models for ecommerce products: Marqo-Ecommerce-B and Marqo-Ecommerce-L. Released Content: Marqo-Ecommerce-B and Marqo-Ecommerce-L embedding models GoogleShopping-1m and AmazonProducts-3m for evaluation Evaluation Code The benchmarking results show that the… See the full description on the dataset page: https://huggingface.co/datasets/Marqo/google-shopping-general-eval.

2024-09-19T17:03:48.000Z

67305871af69498e43ccb14c

OpenCoder-LLM/fineweb-math-corpus

OpenCoder-LLM

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False

2024-11-14T16:38:45.000Z

f369aeecd8c17425d5822573ad8e923791c2d450

This math-related data from Fineweb was specifically used in OpenCoder pre-training. We employ fastText in three iterative rounds to recall a final dataset of 55B code and math-related data. You can find code-related data at OpenCoder-LLM/fineweb-code-corpus. Citation @inproceedings{Huang2024OpenCoderTO, title={OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models}, author={Siming Huang and Tianhao Cheng and Jason Klein Liu and Jiaran Hao and Liuyihan Song… See the full description on the dataset page: https://huggingface.co/datasets/OpenCoder-LLM/fineweb-math-corpus.

2024-11-10T06:53:37.000Z

672c9031c87c7e3ef53af830

OpenGVLab/MMPR

OpenGVLab

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False

2024-11-14T19:28:46.000Z

e4a8292eeb22affd399f23edfbe0274dae1df444

MMPR [📂 GitHub] [🆕 Blog] [📜 Paper] [📖 Documents] Introduction MMPR is a large-scale and high-quality multimodal reasoning preference dataset. This dataset includes about 3 million samples. We finetune InternVL2-8B with MPO using this dataset. The resulting model, InternVL2-8B-MPO, achieves superior performance across 8 benchmarks, particularly excelling in multimodal reasoning tasks. On the MathVista benchmark, our model achieves an accuracy of 67.0%… See the full description on the dataset page: https://huggingface.co/datasets/OpenGVLab/MMPR.

2024-11-07T10:02:25.000Z

66212f29fb07c3e05ad0432e

HuggingFaceFW/fineweb

HuggingFaceFW

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False

2024-07-16T16:04:38.000Z

cd850543a88ba055067841ce91d2669344ff7b7a

🍷 FineWeb 15 trillion tokens of the finest data the 🌐 web has to offer What is it? The 🍷 FineWeb dataset consists of more than 15T tokens of cleaned and deduplicated english web data from CommonCrawl. The data processing pipeline is optimized for LLM performance and ran on the 🏭 datatrove library, our large scale data processing library. 🍷 FineWeb was originally meant to be a fully open replication of 🦅 RefinedWeb, with a release of the full… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb.

2024-04-18T14:33:13.000Z

67307c8682e7b4c983ba2965

DeepMount00/GPT-4o-ITA-INSTRUCT

DeepMount00

{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19344607, "num_examples": 10003}], "download_size": 10819439, "dataset_size": 19344607}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "mit", "language": ["it"], "size_categories": ["10K<n<100K"]}

auto

2024-11-13T12:41:23.000Z

eb91f26acda991c27d64f880c87d637cabd1c678

2024-11-10T09:27:34.000Z

673504ea42d8e0256cb8664c

maxiw/hf-posts

maxiw

{"size_categories": ["1K<n<10K"]}

False

2024-11-13T20:41:53.000Z

bcf4b212681895145fd34eb022153653ae0460d8

Hugging Face Posts This dataset contains posts scraped from https://huggingface.co/posts. It includes all posts published from the launch date on December 23, 2023, up to November 12, 2024, at 07:48.

2024-11-13T19:58:34.000Z

656d9c2bc497edf0a7be5959

tomytjandra/h-and-m-fashion-caption

tomytjandra

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 7843224039.084, "num_examples": 20491}], "download_size": 6302088359, "dataset_size": 7843224039.084}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

False

2023-12-04T11:07:53.000Z

2083a7e30878af2993632b2fc3565ed4a2159534

Dataset Card for "h-and-m-fashion-caption" More Information needed

2023-12-04T09:30:19.000Z

66fc03bc2d7c7dffd1d95786

argilla/Synth-APIGen-v0.1

argilla

{"dataset_info": {"features": [{"name": "func_name", "dtype": "string"}, {"name": "func_desc", "dtype": "string"}, {"name": "tools", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "answers", "dtype": "string"}, {"name": "model_name", "dtype": "string"}, {"name": "hash_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 77390022, "num_examples": 49402}], "download_size": 29656761, "dataset_size": 77390022}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["synthetic", "distilabel", "function-calling"], "size_categories": ["10K<n<100K"]}

False

2024-10-10T11:52:03.000Z

20107f6709aabd18c7f7b4afc96fe7bfe848b5bb

Dataset card for Synth-APIGen-v0.1 This dataset has been created with distilabel. Pipeline script: pipeline_apigen_train.py. Dataset creation It has been created with distilabel==1.4.0 version. This dataset is an implementation of APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets in distilabel, generated from synthetic functions. The process can be summarized as follows: Generate (or in this case modify)… See the full description on the dataset page: https://huggingface.co/datasets/argilla/Synth-APIGen-v0.1.

2024-10-01T14:14:20.000Z

670d0cb9d905bbbc78d7a18a

neuralwork/arxiver

neuralwork

{"license": "cc-by-nc-sa-4.0", "size_categories": ["10K<n<100K"]}

False

2024-11-01T21:18:04.000Z

698a6662e77fd5dd45dbbec988abc8123e5fa086

Arxiver Dataset Arxiver consists of 63,357 arXiv papers converted to multi-markdown (.mmd) format. Our dataset includes original arXiv article IDs, titles, abstracts, authors, publication dates, URLs and corresponding markdown files published between January 2023 and October 2023. We hope our dataset will be useful for various applications such as semantic search, domain specific language modeling, question answering and summarization. Curation The Arxiver dataset… See the full description on the dataset page: https://huggingface.co/datasets/neuralwork/arxiver.

2024-10-14T12:21:13.000Z

6655eb19d17e141dcb546ed5

HuggingFaceFW/fineweb-edu

HuggingFaceFW

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False

2024-10-11T07:55:10.000Z

651a648da38bf545cc5487530dbf59d8168c8de3

📚 FineWeb-Edu 1.3 trillion tokens of the finest educational data the 🌐 web has to offer Paper: https://arxiv.org/abs/2406.17557 What is it? 📚 FineWeb-Edu dataset consists of 1.3T tokens and 5.4T tokens (FineWeb-Edu-score-2) of educational web pages filtered from 🍷 FineWeb dataset. This is the 1.3 trillion version. To enhance FineWeb's quality, we developed an educational quality classifier using annotations generated by LLama3-70B-Instruct. We… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu.

2024-05-28T14:32:57.000Z

665c1855221dda498772b8b5

nvidia/HelpSteer2

nvidia

{"license": "cc-by-4.0", "language": ["en"], "pretty_name": "HelpSteer2", "size_categories": ["10K<n<100K"], "tags": ["human-feedback"]}

False

2024-10-15T16:07:56.000Z

c459751b0b10466341949a26998f4537c9abc755

HelpSteer2: Open-source dataset for training top-performing reward models HelpSteer2 is an open-source Helpfulness Dataset (CC-BY-4.0) that supports aligning models to become more helpful, factually correct and coherent, while being adjustable in terms of the complexity and verbosity of its responses. This dataset has been created in partnership with Scale AI. When used to tune a Llama 3.1 70B Instruct Model, we achieve 94.1% on RewardBench, which makes it the best Reward… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/HelpSteer2.

2024-06-02T06:59:33.000Z

666ae33f611afe17cd982829

BAAI/Infinity-Instruct

BAAI

{"configs": [{"config_name": "3M", "data_files": [{"split": "train", "path": "3M/*"}]}, {"config_name": "7M", "data_files": [{"split": "train", "path": "7M/*"}]}, {"config_name": "0625", "data_files": [{"split": "train", "path": "0625/*"}]}, {"config_name": "Gen", "data_files": [{"split": "train", "path": "Gen/*"}]}, {"config_name": "7M_domains", "data_files": [{"split": "train", "path": "7M_domains/*/*"}]}], "task_categories": ["text-generation"], "language": ["en", "zh"], "size_categories": ["1M<n<10M"], "license": "cc-by-sa-4.0", "extra_gated_prompt": "You agree to not use the dataset to conduct experiments that cause harm to human subjects.", "extra_gated_fields": {"Company/Organization": "text", "Country": "country"}}

auto

2024-10-31T15:06:59.000Z

05cd7e304312b9afc9c4cb5817927805554af437

Infinity Instruct Beijing Academy of Artificial Intelligence (BAAI) [Paper][Code][🤗] (would be released soon) The quality and scale of instruction data are crucial for model performance. Recently, open-source models have increasingly relied on fine-tuning datasets comprising millions of instances, necessitating both high quality and large scale. However, the open-source community has long been constrained by the high costs associated with building such extensive and… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/Infinity-Instruct.

2024-06-13T12:17:03.000Z

670f08ae2e97b2afe4d2df9b

GAIR/o1-journey

GAIR

{"language": ["en"], "size_categories": ["n<1K"]}

False

2024-10-16T00:42:02.000Z

32deef4773fe1f9488ff2052daf64035c034c0ea

Dataset for O1 Replication Journey: A Strategic Progress Report Usage 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… See the full description on the dataset page: https://huggingface.co/datasets/GAIR/o1-journey.

2024-10-16T00:28:30.000Z

67324e20809e988d76c9e982

eltorio/ROCOv2-radiology

eltorio

{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "image_id", "dtype": "string"}, {"name": "caption", "dtype": "string"}, {"name": "cui", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 13464639396.75, "num_examples": 59962}, {"name": "validation", "num_bytes": 2577450447, "num_examples": 9904}, {"name": "test", "num_bytes": 2584850128.125, "num_examples": 9927}], "download_size": 18621371902, "dataset_size": 18626939971.875}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "language": ["en"], "license": "cc-by-nc-sa-4.0", "pretty_name": "ROCOv2", "tags": ["medical"]}

False

2024-11-13T08:49:36.000Z

80ffeef4eb8d34d27cb5c2815305f1d8aee8a83c

ROCOv2: Radiology Object in COntext version 2 Introduction ROCOv2 is a multimodal dataset consisting of radiological images and associated medical concepts and captions extracted from the PMC Open Access Subset. It is an updated version of the ROCO dataset, adding 35,705 new images and improving concept extraction and filtering. Dataset Overview The ROCOv2 dataset contains 79,789 radiological images, each with a corresponding caption and medical… See the full description on the dataset page: https://huggingface.co/datasets/eltorio/ROCOv2-radiology.

2024-11-11T18:34:08.000Z

6732589af21b5584072d297b

gretelai/gretel-text-to-python-fintech-en-v1

gretelai

{"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["synthetic", "domain-specific", "text-to-code", "fintech"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]}

False

2024-11-11T20:13:57.000Z

a126172a7ed9017932fecc9aad04ffd6f2bdb9cf

Gretel Synthetic Text-to-Python Dataset for FinTech This dataset is a synthetically generated collection of natural language prompts paired with their corresponding Python code snippets, specifically tailored for the FinTech industry. Created using Gretel Navigator's Data Designer, with mistral-nemo-2407 and Qwen/Qwen2.5-Coder-7B as the backend models, it aims to bridge the gap between natural language inputs and high-quality Python code, empowering professionals to implement… See the full description on the dataset page: https://huggingface.co/datasets/gretelai/gretel-text-to-python-fintech-en-v1.

2024-11-11T19:18:50.000Z

625552d2b339bb03abe3432d

openai/gsm8k

openai

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "task_ids": [], "paperswithcode_id": "gsm8k", "pretty_name": "Grade School Math 8K", "tags": ["math-word-problems"], "dataset_info": [{"config_name": "main", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3963202, "num_examples": 7473}, {"name": "test", "num_bytes": 713732, "num_examples": 1319}], "download_size": 2725633, "dataset_size": 4676934}, {"config_name": "socratic", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5198108, "num_examples": 7473}, {"name": "test", "num_bytes": 936859, "num_examples": 1319}], "download_size": 3164254, "dataset_size": 6134967}], "configs": [{"config_name": "main", "data_files": [{"split": "train", "path": "main/train-*"}, {"split": "test", "path": "main/test-*"}]}, {"config_name": "socratic", "data_files": [{"split": "train", "path": "socratic/train-*"}, {"split": "test", "path": "socratic/test-*"}]}]}

False

2024-01-04T12:05:15.000Z

e53f048856ff4f594e959d75785d2c2d37b678ee

Dataset Card for GSM8K Dataset Summary GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning. These problems take between 2 and 8 steps to solve. Solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to… See the full description on the dataset page: https://huggingface.co/datasets/openai/gsm8k.

2022-04-12T10:22:10.000Z

gsm8k

639244f571c51c43091df168

Anthropic/hh-rlhf

Anthropic

{"license": "mit", "tags": ["human-feedback"]}

False

2023-05-26T18:47:34.000Z

09be8c5bbc57cb3887f3a9732ad6aa7ec602a1fa

Dataset Card for HH-RLHF Dataset Summary This repository provides access to two different kinds of data: Human preference data about helpfulness and harmlessness from Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. These data are meant to train preference (or reward) models for subsequent RLHF training. These data are not meant for supervised training of dialogue agents. Training dialogue agents on these data is likely… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/hh-rlhf.

2022-12-08T20:11:33.000Z

63f95489ce1f61ce15165ace

wangrui6/Zhihu-KOL

wangrui6

{"dataset_info": {"features": [{"name": "INSTRUCTION", "dtype": "string"}, {"name": "RESPONSE", "dtype": "string"}, {"name": "SOURCE", "dtype": "string"}, {"name": "METADATA", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2295601241, "num_examples": 1006218}], "download_size": 1501204472, "dataset_size": 2295601241}, "task_categories": ["question-answering"], "language": ["zh"]}

False

2023-04-23T13:26:03.000Z

f66a447c8bcc75cc0d393ba629cd8b70444ee774

Dataset Card for "Zhihu-KOL" Zhihu data for training Open Assitant More Information needed

2023-02-25T00:21:29.000Z

641debae1d05404efd046a4f

yahma/alpaca-cleaned

yahma

{"license": "cc-by-4.0", "language": ["en"], "tags": ["instruction-finetuning"], "pretty_name": "Alpaca-Cleaned", "task_categories": ["text-generation"]}

False

2023-04-10T20:29:06.000Z

12567cabf869d7c92e573c7c783905fc160e9639

Dataset Card for Alpaca-Cleaned Repository: https://github.com/gururise/AlpacaDataCleaned Dataset Description This is a cleaned version of the original Alpaca Dataset released by Stanford. The following issues have been identified in the original release and fixed in this dataset: Hallucinations: Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer. "instruction":"Summarize… See the full description on the dataset page: https://huggingface.co/datasets/yahma/alpaca-cleaned.

2023-03-24T18:27:58.000Z

66bc06dc6da7aec8413d35ba

NousResearch/hermes-function-calling-v1

NousResearch

{"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"}]}

False

2024-08-30T06:07:08.000Z

8f025148382537ba84cd325e1834b706e1461692

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… See the full description on the dataset page: https://huggingface.co/datasets/NousResearch/hermes-function-calling-v1.

2024-08-14T01:22:36.000Z

66e46a3f6e6ce3af7295dde6

openai/MMMLU

openai

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False

2024-10-16T18:39:00.000Z

325a01dc3e173cac1578df94120499aaca2e2504

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… See the full description on the dataset page: https://huggingface.co/datasets/openai/MMMLU.

2024-09-13T16:37:19.000Z

671bd07135c5f1daad8e834b

eltorio/ROCO-radiology

eltorio

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False

2024-11-07T18:15:10.000Z

de62692232af189f188f81bdbb02bc7123ee3e3e

The "ROCO-radiology" dataset is derived from the Radiology Objects in COntext (ROCO) dataset, a large-scale medical and multimodal imaging collection. The language used is primarily English, and it covers the domain of medical imaging, specifically radiology. We only modified the dataset by choosing only for radiology dataset and convert the image into PIL Object. For further details and citation, pleaser refer to original author.… See the full description on the dataset page: https://huggingface.co/datasets/eltorio/ROCO-radiology.

2024-10-25T17:08:01.000Z

621ffdd236468d709f181dd1

hendrycks/competition_math

hendrycks

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False

2023-06-08T06:40:09.000Z

71b758ecc688b2822d07ffa7f8393299f1dc7cac

The Mathematics Aptitude Test of Heuristics (MATH) dataset consists of problems from mathematics competitions, including the AMC 10, AMC 12, AIME, and more. Each problem in MATH has a full step-by-step solution, which can be used to teach models to generate answer derivations and explanations.

2022-03-02T23:29:22.000Z

@article{hendrycksmath2021, title={Measuring Mathematical Problem Solving With the MATH Dataset}, author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Arora and Steven Basart and Eric Tang and Dawn Song and Jacob Steinhardt}, journal={arXiv preprint arXiv:2103.03874}, year={2021} }

621ffdd236468d709f182a80

allenai/c4

allenai

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{"config_name": "xh", "data_files": [{"split": "train", "path": "multilingual/c4-xh.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-xh-validation.*.json.gz"}]}, {"config_name": "yi", "data_files": [{"split": "train", "path": "multilingual/c4-yi.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-yi-validation.*.json.gz"}]}, {"config_name": "yo", "data_files": [{"split": "train", "path": "multilingual/c4-yo.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-yo-validation.*.json.gz"}]}, {"config_name": "zh", "data_files": [{"split": "train", "path": "multilingual/c4-zh.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-zh-validation.*.json.gz"}]}, {"config_name": "zh-Latn", "data_files": [{"split": "train", "path": "multilingual/c4-zh-Latn.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-zh-Latn-validation.*.json.gz"}]}, {"config_name": "zu", "data_files": [{"split": "train", "path": "multilingual/c4-zu.*.json.gz"}, {"split": 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False

2024-01-09T19:14:03.000Z

1588ec454efa1a09f29cd18ddd04fe05fc8653a2

C4 Dataset Summary A colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: "https://commoncrawl.org". This is the processed version of Google's C4 dataset We prepared five variants of the data: en, en.noclean, en.noblocklist, realnewslike, and multilingual (mC4). For reference, these are the sizes of the variants: en: 305GB en.noclean: 2.3TB en.noblocklist: 380GB realnewslike: 15GB multilingual (mC4): 9.7TB (108 subsets, one… See the full description on the dataset page: https://huggingface.co/datasets/allenai/c4.

2022-03-02T23:29:22.000Z

c4

64382440c212a363c3ac15c8

OpenAssistant/oasst1

OpenAssistant

{"license": "apache-2.0", "dataset_info": {"features": [{"name": "message_id", "dtype": "string"}, {"name": "parent_id", "dtype": "string"}, {"name": "user_id", "dtype": "string"}, {"name": "created_date", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "role", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "review_count", "dtype": "int32"}, {"name": "review_result", "dtype": "bool"}, {"name": "deleted", "dtype": "bool"}, {"name": "rank", "dtype": "int32"}, {"name": "synthetic", "dtype": "bool"}, {"name": "model_name", "dtype": "string"}, {"name": "detoxify", "struct": [{"name": "toxicity", "dtype": "float64"}, {"name": "severe_toxicity", "dtype": "float64"}, {"name": "obscene", "dtype": "float64"}, {"name": "identity_attack", "dtype": "float64"}, {"name": "insult", "dtype": "float64"}, {"name": "threat", "dtype": "float64"}, {"name": "sexual_explicit", "dtype": "float64"}]}, {"name": "message_tree_id", "dtype": "string"}, {"name": "tree_state", "dtype": "string"}, {"name": "emojis", "sequence": [{"name": "name", "dtype": "string"}, {"name": "count", "dtype": "int32"}]}, {"name": "labels", "sequence": [{"name": "name", "dtype": "string"}, {"name": "value", "dtype": "float64"}, {"name": "count", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 100367999, "num_examples": 84437}, {"name": "validation", "num_bytes": 5243405, "num_examples": 4401}], "download_size": 41596430, "dataset_size": 105611404}, "language": ["en", "es", "ru", "de", "pl", "th", "vi", "sv", "bn", "da", "he", "it", "fa", "sk", "id", "nb", "el", "nl", "hu", "eu", "zh", "eo", "ja", "ca", "cs", "bg", "fi", "pt", "tr", "ro", "ar", "uk", "gl", "fr", "ko"], "tags": ["human-feedback"], "size_categories": ["100K<n<1M"], "pretty_name": "OpenAssistant Conversations"}

False

2023-05-02T13:21:21.000Z

fdf72ae0827c1cda404aff25b6603abec9e3399b

OpenAssistant Conversations Dataset (OASST1) Dataset Summary In an effort to democratize research on large-scale alignment, we release OpenAssistant Conversations (OASST1), a human-generated, human-annotated assistant-style conversation corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292 quality ratings, resulting in over 10,000 fully annotated conversation trees. The corpus is a product of a worldwide crowd-sourcing effort… See the full description on the dataset page: https://huggingface.co/datasets/OpenAssistant/oasst1.

2023-04-13T15:48:16.000Z

649f37af37bfb5202beabdf4

allenai/dolma

allenai

{"license": "odc-by", "viewer": false, "task_categories": ["text-generation"], "language": ["en"], "tags": ["language-modeling", "casual-lm", "llm"], "pretty_name": "Dolma", "size_categories": ["n>1T"]}

False

2024-04-17T02:57:00.000Z

7f48140530a023e9ea4c5cfb141160922727d4d3

Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research

2023-06-30T20:14:39.000Z

@article{dolma, title = {{Dolma: An Open Corpus of Three Trillion Tokens for Language Model Pretraining Research}}, author = { Luca Soldaini and Rodney Kinney and Akshita Bhagia and Dustin Schwenk and David Atkinson and Russell Authur and Ben Bogin and Khyathi Chandu and Jennifer Dumas and Yanai Elazar and Valentin Hofmann and Ananya Harsh Jha and Sachin Kumar and Li Lucy and Xinxi Lyu and Ian Magnusson and Jacob Morrison and Niklas Muennighoff and Aakanksha Naik and Crystal Nam and Matthew E. Peters and Abhilasha Ravichander and Kyle Richardson and Zejiang Shen and Emma Strubell and Nishant Subramani and Oyvind Tafjord and Evan Pete Walsh and Hannaneh Hajishirzi and Noah A. Smith and Luke Zettlemoyer and Iz Beltagy and Dirk Groeneveld and Jesse Dodge and Kyle Lo }, year = {2024}, journal={arXiv preprint}, }

64dbd28f00b80a024c762bd8

glaiveai/glaive-function-calling-v2

glaiveai

{"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "size_categories": ["100K<n<1M"]}

False

2023-09-27T18:04:08.000Z

e7f4b6456019f5d8bcb991ef0dd67d8ff23221ac

2023-08-15T19:31:27.000Z

660e7b9b4636ce2b0e77b699

mozilla-foundation/common_voice_17_0

mozilla-foundation

{"pretty_name": "Common Voice Corpus 17.0", "annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["ab", "af", "am", "ar", "as", "ast", "az", "ba", "bas", "be", "bg", "bn", "br", "ca", "ckb", "cnh", "cs", "cv", "cy", "da", "de", "dv", "dyu", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gl", "gn", "ha", "he", "hi", "hsb", "ht", "hu", "hy", "ia", "id", "ig", "is", "it", "ja", "ka", "kab", "kk", "kmr", "ko", "ky", "lg", "lij", "lo", "lt", "ltg", "lv", "mdf", "mhr", "mk", "ml", "mn", "mr", "mrj", "mt", "myv", "nan", "ne", "nhi", "nl", "nn", "nso", "oc", "or", "os", "pa", "pl", "ps", "pt", "quy", "rm", "ro", "ru", "rw", "sah", "sat", "sc", "sk", "skr", "sl", "sq", "sr", "sv", "sw", "ta", "te", "th", "ti", "tig", "tk", "tok", "tr", "tt", "tw", "ug", "uk", "ur", "uz", "vi", "vot", "yi", "yo", "yue", "zgh", "zh", "zu", "zza"], "language_bcp47": ["zh-CN", "zh-HK", "zh-TW", "sv-SE", "rm-sursilv", "rm-vallader", "pa-IN", "nn-NO", "ne-NP", "nan-tw", "hy-AM", "ga-IE", "fy-NL"], "license": ["cc0-1.0"], "multilinguality": ["multilingual"], "source_datasets": ["extended|common_voice"], "paperswithcode_id": "common-voice", "extra_gated_prompt": "By clicking on \u201cAccess repository\u201d below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset."}

auto

2024-06-16T13:50:23.000Z

b10d53980ef166bc24ce3358471c1970d7e6b5ec

Dataset Card for Common Voice Corpus 17.0 Dataset Summary The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 31175 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help improve the accuracy of speech recognition engines. The dataset currently consists of 20408 validated hours in 124 languages, but more voices and languages are always added. Take a look at the Languages… See the full description on the dataset page: https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0.

2024-04-04T10:06:19.000Z

common-voice

@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 }

662300e51e129fc4cc2892ae

bigcode/self-oss-instruct-sc2-exec-filter-50k

bigcode

{"dataset_info": {"features": [{"name": "fingerprint", "dtype": "null"}, {"name": "sha1", "dtype": "string"}, {"name": "seed", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "concepts", "sequence": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 261340280, "num_examples": 50661}], "download_size": 90128158, "dataset_size": 261340280}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "odc-by", "pretty_name": "StarCoder2-15b Self-Alignment Dataset (50K)"}

False

2024-11-04T19:00:05.000Z

356bb069eee815daa6e23e9a282eeefe1490ad44

Final self-alignment training dataset for StarCoder2-Instruct. seed: Contains the seed Python function concepts: Contains the concepts generated from the seed instruction: Contains the instruction generated from the concepts response: Contains the execution-validated response to the instruction This dataset utilizes seed Python functions derived from the MultiPL-T pipeline.

2024-04-19T23:40:21.000Z

66670ea06e382e809d2bca3b

linxy/LaTeX_OCR

linxy

{"license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["image-to-text"], "dataset_info": [{"config_name": "default", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 392473380.05, "num_examples": 76318}], "download_size": 383401054, "dataset_size": 392473380.05}, {"config_name": "full", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 392478490.025, "num_examples": 76319}, {"name": "validation", "num_bytes": 43364061.55, "num_examples": 8475}, {"name": "test", "num_bytes": 47643036.303, "num_examples": 9443}], "download_size": 473618552, "dataset_size": 483485587.878}, {"config_name": "human_handwrite", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16181778, "num_examples": 1200}, {"name": "validation", "num_bytes": 962283, "num_examples": 68}, {"name": "test", "num_bytes": 906906, "num_examples": 70}], "download_size": 18056029, "dataset_size": 18050967}, {"config_name": "human_handwrite_print", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3152122.8, "num_examples": 1200}, {"name": "validation", "num_bytes": 182615, "num_examples": 68}, {"name": "test", "num_bytes": 181698, "num_examples": 70}], "download_size": 1336052, "dataset_size": 3516435.8}, {"config_name": "small", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 261296, "num_examples": 50}, {"name": "validation", "num_bytes": 156489, "num_examples": 30}, {"name": "test", "num_bytes": 156489, "num_examples": 30}], "download_size": 588907, "dataset_size": 574274}, {"config_name": "synthetic_handwrite", "features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 496610333.066, "num_examples": 76266}, {"name": "validation", "num_bytes": 63147351.515, "num_examples": 9565}, {"name": "test", "num_bytes": 62893132.805, "num_examples": 9593}], "download_size": 616418996, "dataset_size": 622650817.3859999}], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "full/train-*"}]}, {"config_name": "full", "data_files": [{"split": "train", "path": "full/train-*"}, {"split": "validation", "path": "full/validation-*"}, {"split": "test", "path": "full/test-*"}]}, {"config_name": "human_handwrite", "data_files": [{"split": "train", "path": "human_handwrite/train-*"}, {"split": "validation", "path": "human_handwrite/validation-*"}, {"split": "test", "path": "human_handwrite/test-*"}]}, {"config_name": "human_handwrite_print", "data_files": [{"split": "train", "path": "human_handwrite_print/train-*"}, {"split": "validation", "path": "human_handwrite_print/validation-*"}, {"split": "test", "path": "human_handwrite_print/test-*"}]}, {"config_name": "small", "data_files": [{"split": "train", "path": "small/train-*"}, {"split": "validation", "path": "small/validation-*"}, {"split": "test", "path": "small/test-*"}]}, {"config_name": "synthetic_handwrite", "data_files": [{"split": "train", "path": "synthetic_handwrite/train-*"}, {"split": "validation", "path": "synthetic_handwrite/validation-*"}, {"split": "test", "path": "synthetic_handwrite/test-*"}]}], "tags": ["code"]}

False

2024-10-23T03:07:48.000Z

89aa6e447dd7afb4dec927af549df766539b6f9c

LaTeX OCR 的数据仓库 本数据仓库是专为 LaTeX_OCR 及 LaTeX_OCR_PRO 制作的数据，来源于 https://zenodo.org/record/56198#.V2p0KTXT6eA 以及 https://www.isical.ac.in/~crohme/ 以及我们自己构建。 如果这个数据仓库有帮助到你的话，请点亮 ❤️like ++ 后续追加新的数据也会放在这个仓库 ~~ 原始数据仓库在github LinXueyuanStdio/Data-for-LaTeX_OCR. 数据集 本仓库有 5 个数据集 small 是小数据集，样本数 110 条，用于测试 full 是印刷体约 100k 的完整数据集。实际上样本数略小于 100k，因为用 LaTeX 的抽象语法树剔除了很多不能渲染的 LaTeX。 synthetic_handwrite 是手写体 100k 的完整数据集，基于 full 的公式，使用手写字体合成而来，可以视为人类在纸上的手写体。样本数实际上略小于 100k，理由同上。… See the full description on the dataset page: https://huggingface.co/datasets/linxy/LaTeX_OCR.

2024-06-10T14:33:04.000Z

66e1a2fb91e57a0788b501cb

jackyhate/text-to-image-2M

jackyhate

{"license": "mit", "task_categories": ["text-to-image", "image-to-text", "image-classification"], "language": ["en"], "size_categories": ["1M<n<10M"]}

False

2024-09-22T09:38:54.000Z

e4ece89e640210e9fc3fd0966f5a45291bdb665c

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… See the full description on the dataset page: https://huggingface.co/datasets/jackyhate/text-to-image-2M.

2024-09-11T14:02:35.000Z

66eb894483591125987548f7

google/frames-benchmark

google

{"license": "apache-2.0", "language": ["en"], "tags": ["rag", "long-context", "llm-search", "reasoning", "factuality", "retrieval", "question-answering", "iterative-search"], "task_categories": ["text-classification", "token-classification", "table-question-answering", "question-answering"], "pretty_name": "Who are I or you", "size_categories": ["n>1T"]}

False

2024-10-15T18:18:24.000Z

58d9fb6330f3ab1316d1eca12e5e8ef23dcc22ef

FRAMES: Factuality, Retrieval, And reasoning MEasurement Set FRAMES is a comprehensive evaluation dataset designed to test the capabilities of Retrieval-Augmented Generation (RAG) systems across factuality, retrieval accuracy, and reasoning. Our paper with details and experiments is available on arXiv: https://arxiv.org/abs/2409.12941. Dataset Overview 824 challenging multi-hop questions requiring information from 2-15 Wikipedia articles Questions span diverse… See the full description on the dataset page: https://huggingface.co/datasets/google/frames-benchmark.

2024-09-19T02:15:32.000Z

670bd71d721603bf001c0399

opencsg/chinese-fineweb-edu-v2

opencsg

{"language": ["zh"], "pipeline_tag": "text-generation", "license": "apache-2.0", "task_categories": ["text-generation"], "size_categories": ["10B<n<100B"]}

False

2024-10-26T04:51:41.000Z

bd123e34c706a1b34274a79e1e1cd81b18cda5cc

Chinese Fineweb Edu Dataset V2 [中文] [English] [OpenCSG Community] [github] [wechat] [Twitter] Chinese Fineweb Edu Dataset V2 is a comprehensive upgrade of the original Chinese Fineweb Edu, designed and optimized for natural language processing (NLP) tasks in the education sector. This high-quality Chinese pretraining dataset has undergone significant improvements and expansions, aimed at providing researchers and developers with more diverse and broadly… See the full description on the dataset page: https://huggingface.co/datasets/opencsg/chinese-fineweb-edu-v2.

2024-10-13T14:20:13.000Z

670e1f14c308791317666994

BAAI/Infinity-MM

BAAI

{"license": "cc-by-sa-4.0", "configs": [{"config_name": "stage1", "data_files": [{"split": "train", "path": "stage1/*/*"}]}, {"config_name": "stage2", "data_files": [{"split": "train", "path": "stage2/*/*/*"}]}, {"config_name": "stage3", "data_files": [{"split": "train", "path": "stage3/*/*"}]}, {"config_name": "stage4", "data_files": [{"split": "train", "path": "stage4/*/*/*"}]}], "language": ["en", "zh"], "size_categories": ["10M<n<100M"], "task_categories": ["image-to-text"], "extra_gated_prompt": "You agree to not use the dataset to conduct experiments that cause harm to human subjects.", "extra_gated_fields": {"Company/Organization": "text", "Country": "country"}}

auto

2024-11-05T06:57:13.000Z

79e444ad1cf4744630e75964b277944bbc44f837

Introduction Beijing Academy of Artificial Intelligence (BAAI) We collect, organize and open-source the large-scale multimodal instruction dataset, Infinity-MM, consisting of tens of millions of samples. Through quality filtering and deduplication, the dataset has high quality and diversity. We propose a synthetic data generation method based on open-source models and labeling system, using detailed image annotations and diverse question generation. News… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/Infinity-MM.

2024-10-15T07:51:48.000Z

671221694da2bd63c6bdcc32

Salesforce/GiftEval

Salesforce

{"license": "apache-2.0", "task_categories": ["time-series-forecasting"], "tags": ["timeseries", "forecasting", "benchmark", "gifteval"], "size_categories": ["100K<n<1M"]}

False

2024-11-07T05:55:35.000Z

930b5513aed532a99dc260c38893223738858044

GIFT-Eval We present GIFT-Eval, a benchmark designed to advance zero-shot time series forecasting by facilitating evaluation across diverse datasets. GIFT-Eval includes 23 datasets covering 144,000 time series and 177 million data points, with data spanning seven domains, 10 frequencies, and a range of forecast lengths. This benchmark aims to set a new standard, guiding future innovations in time series foundation models. To facilitate the effective pretraining and evaluation… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/GiftEval.

2024-10-18T08:50:49.000Z

672229780d9d384e49c2ce10

HKAIR-Lab/HK-O1aw-SFT-16K

HKAIR-Lab

{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": ["data/train.jsonl"]}, {"split": "test", "path": ["data/test.jsonl"]}]}], "license": "cc-by-nc-4.0"}

False

2024-11-01T07:20:35.000Z

67c330a75827dcadfd624e80ceedbea41a0dc666

Dataset Card for O1aw-sft-16k (v0) O1aw-Dataset is a comprehensive legal question-thought-answer dataset, designed to evaluate and enhance legal reasoning capabilities in language models. The dataset follows the O1-style format, featuring complex legal scenarios that require multi-step reasoning. Data Collection First, we crawl and clean raw legal materials from the internet, including Hong Kong e-Legislation. Then, we use GPT-4o to generate corresponding… See the full description on the dataset page: https://huggingface.co/datasets/HKAIR-Lab/HK-O1aw-SFT-16K.

2024-10-30T12:41:28.000Z

672e4097987efdc25b010447

ChicagoHAI/CaseSumm

ChicagoHAI

{"license": "cc-by-nc-3.0", "task_categories": ["summarization"], "language": ["en"], "tags": ["legal"]}

False

2024-11-08T21:28:45.000Z

d121a154587f61a56c0f794217cc7ea72e04b157

The CaseSumm dataset consists of U.S. Supreme Court cases and their official summaries, called syllabuses, from the period 1815-2019. Syllabuses are written by an attorney employed by the Court and approved by the Justices. The syllabus is therefore the gold standard for summarizing majority opinions, and ideal for evaluating other summaries of the opinion. We obtain the opinions from Public Resource Org's archive and extract syllabuses from the official opinions published in the U.S. Reporter… See the full description on the dataset page: https://huggingface.co/datasets/ChicagoHAI/CaseSumm.

2024-11-08T16:47:19.000Z

6732353b218d0500f8ff12c5

louisbrulenaudet/mergekit-configs

louisbrulenaudet

{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "sha", "dtype": "null"}, {"name": "created_at", "dtype": "timestamp[us, tz=UTC]"}, {"name": "last_modified", "dtype": "null"}, {"name": "disabled", "dtype": "null"}, {"name": "downloads", "dtype": "int64"}, {"name": "downloads_all_time", "dtype": "null"}, {"name": "gated", "dtype": "bool"}, {"name": "gguf", "dtype": "null"}, {"name": "inference", "dtype": "null"}, {"name": "likes", "dtype": "int64"}, {"name": "library_name", "dtype": "string"}, {"name": "tags", "sequence": "string"}, {"name": "pipeline_tag", "dtype": "string"}, {"name": "mask_token", "dtype": "null"}, {"name": "model_index", "dtype": "null"}, {"name": "trending_score", "dtype": "int64"}, {"name": "architectures", "sequence": "string"}, {"name": "bos_token_id", "dtype": "int64"}, {"name": "eos_token_id", "dtype": "int64"}, {"name": "hidden_act", "dtype": "string"}, {"name": "hidden_size", "dtype": "int64"}, {"name": "initializer_range", "dtype": "float64"}, {"name": "intermediate_size", "dtype": "int64"}, {"name": "max_position_embeddings", "dtype": "int64"}, {"name": "model_type", "dtype": "string"}, {"name": "num_attention_heads", "dtype": "int64"}, {"name": "num_hidden_layers", "dtype": "int64"}, {"name": "num_key_value_heads", "dtype": "int64"}, {"name": "rms_norm_eps", "dtype": "float64"}, {"name": "rope_theta", "dtype": "float64"}, {"name": "sliding_window", "dtype": "int64"}, {"name": "tie_word_embeddings", "dtype": "bool"}, {"name": "torch_dtype", "dtype": "string"}, {"name": "transformers_version", "dtype": "string"}, {"name": "use_cache", "dtype": "bool"}, {"name": "vocab_size", "dtype": "int64"}, {"name": "attention_bias", "dtype": "bool"}, {"name": "attention_dropout", "dtype": "float64"}, {"name": "head_dim", "dtype": "int64"}, {"name": "mlp_bias", "dtype": "bool"}, {"name": "pretraining_tp", "dtype": "int64"}, {"name": "rope_scaling", "struct": [{"name": "factor", "dtype": "float64"}, {"name": "original_max_position_embeddings", "dtype": "float64"}]}], "splits": [{"name": "raw", "num_bytes": 70119636, "num_examples": 129379}], "download_size": 9132674, "dataset_size": 70119636}, "configs": [{"config_name": "default", "data_files": [{"split": "raw", "path": "data/raw-*"}]}], "license": "apache-2.0", "task_categories": ["question-answering"], "language": ["en", "fr"], "tags": ["merge", "mergekit", "configs", "code", "automation"], "pretty_name": "mergekit-configs: access all Hub architecture", "size_categories": ["100K<n<1M"]}

False

2024-11-14T08:16:26.000Z

6676d6e3bdb4760b42b3c861cc28d00fe01690ea

MergeKit-configs: access all Hub architectures and automate your model merging process This dataset facilitates the search for compatible architectures for model merging with MergeKit, streamlining the automation of high-performance merge searches. It provides a snapshot of the Hub’s configuration state, eliminating the need to manually open configuration files. import polars as pl # Login using e.g. `huggingface-cli login` to access this dataset df =… See the full description on the dataset page: https://huggingface.co/datasets/louisbrulenaudet/mergekit-configs.

2024-11-11T16:47:55.000Z

6732ddc4c1f20c742b110950

Marqo/google-shopping-general-eval-100k

Marqo

{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "item_ID", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "position", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 2212236284.504, "num_examples": 99808}], "download_size": 2234691014, "dataset_size": 2212236284.504}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]}

False

2024-11-12T04:58:46.000Z

2ca53e383f42304f9b81a01fbd7b654cfc20aecd

Marqo Ecommerce Embedding Models In this work, we introduce the GoogleShopping-1m dataset for evaluation. This dataset comes with the release of our state-of-the-art embedding models for ecommerce products: Marqo-Ecommerce-B and Marqo-Ecommerce-L. Released Content: Marqo-Ecommerce-B and Marqo-Ecommerce-L embedding models GoogleShopping-1m and AmazonProducts-3m for evaluation Evaluation Code The benchmarking results show that the… See the full description on the dataset page: https://huggingface.co/datasets/Marqo/google-shopping-general-eval-100k.

2024-11-12T04:47:00.000Z

6732dee4f6f2d658c7f92fbc

Marqo/amazon-products-eval-100k

Marqo

{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "item_ID", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "position", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 1443194395, "num_examples": 100000}], "download_size": 1132417749, "dataset_size": 1443194395}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]}

False

2024-11-12T05:00:10.000Z

2c8fa19f002308e7d03676b02534a74cef804172

Marqo Ecommerce Embedding Models In this work, we introduce the GoogleShopping-1m dataset for evaluation. This dataset comes with the release of our state-of-the-art embedding models for ecommerce products: Marqo-Ecommerce-B and Marqo-Ecommerce-L. Released Content: Marqo-Ecommerce-B and Marqo-Ecommerce-L embedding models GoogleShopping-1m and AmazonProducts-3m for evaluation Evaluation Code The benchmarking results show that the… See the full description on the dataset page: https://huggingface.co/datasets/Marqo/amazon-products-eval-100k.

2024-11-12T04:51:48.000Z

621ffdd236468d709f181e41

google-research-datasets/go_emotions

google-research-datasets

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False

2024-01-04T11:56:51.000Z

add492243ff905527e67aeb8b80c082af02207c3

Dataset Card for GoEmotions Dataset Summary The GoEmotions dataset contains 58k carefully curated Reddit comments labeled for 27 emotion categories or Neutral. The raw data is included as well as the smaller, simplified version of the dataset with predefined train/val/test splits. Supported Tasks and Leaderboards This dataset is intended for multi-class, multi-label emotion classification. Languages The data is in English.… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/go_emotions.

2022-03-02T23:29:22.000Z

goemotions

633751b65dfc73bbc0c36a0e

laion/laion-coco

laion

auto

2024-07-14T07:19:09.000Z

247da52346b23b7ff399a67c452d0588416e47bf

2022-09-30T20:29:42.000Z

640f5b2fb63b6f18522d6d44

tatsu-lab/alpaca

tatsu-lab

{"license": "cc-by-nc-4.0", "language": ["en"], "tags": ["instruction-finetuning"], "pretty_name": "Alpaca", "task_categories": ["text-generation"]}

False

2023-05-22T20:33:36.000Z

dce01c9b08f87459cf36a430d809084718273017

Dataset Card for Alpaca Dataset Summary Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's text-davinci-003 engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better. The authors built on the data generation pipeline from Self-Instruct framework and made the following modifications: The text-davinci-003 engine to generate the instruction data… See the full description on the dataset page: https://huggingface.co/datasets/tatsu-lab/alpaca.

2023-03-13T17:19:43.000Z

643ce713099590e9ed8f29f7

togethercomputer/RedPajama-Data-1T

togethercomputer

{"task_categories": ["text-generation"], "language": ["en"], "pretty_name": "Red Pajama 1T"}

False

2024-06-17T11:36:03.000Z

398f92572e94f4793e41c22ab7ea2a788d9e7de4

RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset.

2023-04-17T06:28:35.000Z

650a9248d26103b6eee3ea7b

lmsys/lmsys-chat-1m

lmsys

{"size_categories": ["1M<n<10M"], "task_categories": ["conversational"], "extra_gated_prompt": "You agree to the [LMSYS-Chat-1M Dataset License Agreement](https://huggingface.co/datasets/lmsys/lmsys-chat-1m#lmsys-chat-1m-dataset-license-agreement).", "extra_gated_fields": {"Name": "text", "Email": "text", "Affiliation": "text", "Country": "text"}, "extra_gated_button_content": "I agree to the terms and conditions of the LMSYS-Chat-1M Dataset License Agreement.", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "conversation_id", "dtype": "string"}, {"name": "model", "dtype": "string"}, {"name": "conversation", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"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": "redacted", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 2626438904, "num_examples": 1000000}], "download_size": 1488850250, "dataset_size": 2626438904}}

auto

2024-07-27T09:28:42.000Z

200748d9d3cddcc9d782887541057aca0b18c5da

LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset This dataset contains one million real-world conversations with 25 state-of-the-art LLMs. It is collected from 210K unique IP addresses in the wild on the Vicuna demo and Chatbot Arena website from April to August 2023. Each sample includes a conversation ID, model name, conversation text in OpenAI API JSON format, detected language tag, and OpenAI moderation API tag. User consent is obtained through the "Terms of… See the full description on the dataset page: https://huggingface.co/datasets/lmsys/lmsys-chat-1m.

2023-09-20T06:33:44.000Z

6596b26db31c349cd75eb40e

nyanko7/danbooru2023

nyanko7

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False

2024-05-22T18:43:24.000Z

4ddd8c6504b1381716bbeb2cb3f502eeb14e48d2

Danbooru2023: A Large-Scale Crowdsourced and Tagged Anime Illustration Dataset Danbooru2023 is a large-scale anime image dataset with over 5 million images contributed and annotated in detail by an enthusiast community. Image tags cover aspects like characters, scenes, copyrights, artists, etc with an average of 30 tags per image. Danbooru is a veteran anime image board with high-quality images and extensive tag metadata. The dataset can be used to train image classification… See the full description on the dataset page: https://huggingface.co/datasets/nyanko7/danbooru2023.

2024-01-04T13:28:13.000Z

65dc13085ca10be41fdd8b27

bigcode/the-stack-v2

bigcode

{"annotations_creators": [], "language_creators": ["crowdsourced", "expert-generated"], "language": ["code"], "license": ["other"], "multilinguality": ["multilingual"], "pretty_name": "The-Stack-v2", "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["text-generation"], "task_ids": [], "extra_gated_prompt": "## Terms of Use for The Stack v2\n\nThe Stack v2 dataset is a collection of source code in over 600 programming languages. We ask that you read and acknowledge the following points before using the dataset:\n1. Downloading the dataset in bulk requires a an agreement with SoftwareHeritage and INRIA. Contact [[email protected]](mailto:[email protected]?subject=TheStackV2%20request%20for%20dataset%20access%20information) for more information.\n2. If you are using the dataset to train models you must adhere to the SoftwareHeritage [principles for language model training](https://www.softwareheritage.org/2023/10/19/swh-statement-on-llm-for-code/).\n3. The Stack v2 is a collection of source code from repositories with various licenses. Any use of all or part of the code gathered in The Stack v2 must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point.\n4. The Stack v2 is regularly updated to enact validated data removal requests. 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"data_files": [{"split": "train", "path": "data/nesC/*.parquet"}]}, {"config_name": "ooc", "data_files": [{"split": "train", "path": "data/ooc/*.parquet"}]}, {"config_name": "q", "data_files": [{"split": "train", "path": "data/q/*.parquet"}]}, {"config_name": "reStructuredText", "data_files": [{"split": "train", "path": "data/reStructuredText/*.parquet"}]}, {"config_name": "robots.txt", "data_files": [{"split": "train", "path": "data/robots.txt/*.parquet"}]}, {"config_name": "sed", "data_files": [{"split": "train", "path": "data/sed/*.parquet"}]}, {"config_name": "wdl", "data_files": [{"split": "train", "path": "data/wdl/*.parquet"}]}, {"config_name": "wisp", "data_files": [{"split": "train", "path": "data/wisp/*.parquet"}]}, {"config_name": "xBase", "data_files": [{"split": "train", "path": "data/xBase/*.parquet"}]}]}

auto

2024-04-23T15:52:32.000Z

7408bfbcfd48e5833d62fd3dba48afd20d109473

The Stack v2 The dataset consists of 4 versions: bigcode/the-stack-v2: the full "The Stack v2" dataset <-- you are here bigcode/the-stack-v2-dedup: based on the bigcode/the-stack-v2 but further near-deduplicated bigcode/the-stack-v2-train-full-ids: based on the bigcode/the-stack-v2-dedup dataset but further filtered with heuristics and spanning 600+ programming languages. The data is grouped into repositories. bigcode/the-stack-v2-train-smol-ids: based on the… See the full description on the dataset page: https://huggingface.co/datasets/bigcode/the-stack-v2.

2024-02-26T04:26:48.000Z

66a53dc7d40a13036c5f2ebe

mlabonne/FineTome-100k

mlabonne

{"dataset_info": {"features": [{"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "source", "dtype": "string"}, {"name": "score", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 239650960.7474458, "num_examples": 100000}], "download_size": 116531415, "dataset_size": 239650960.7474458}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

False

2024-07-29T09:52:30.000Z

c2343c1372ff31f51aa21248db18bffa3193efdb

FineTome-100k The FineTome dataset is a subset of arcee-ai/The-Tome (without arcee-ai/qwen2-72b-magpie-en), re-filtered using HuggingFaceFW/fineweb-edu-classifier. It was made for my article "Fine-tune Llama 3.1 Ultra-Efficiently with Unsloth".

2024-07-27T18:34:47.000Z

66ebfe842f79fb99cc65038e

BAAI/IndustryInstruction

BAAI

{"license": "apache-2.0", "task_categories": ["question-answering"], "language": ["zh", "en"], "size_categories": ["10M<n<100M"], "extra_gated_prompt": "You agree to not use the dataset to conduct experiments that cause harm to human subjects.", "extra_gated_fields": {"Company/Organization": "text", "Country": "country"}}

auto

2024-11-12T08:25:33.000Z

6b5ff74e8747950df4d22d9857bcc2512865dbd8

本数据集为行业指令数据集，目前包含的行业中英文对照名称如下，本次数据旨在补充当前行业指令数据的空白，并挖掘BAAI/IndustryCorpus2预训练数据集中高质量预训练语料中包含的行业高价值知识。 汽车 : Automobiles 航空航天 : Aerospace 人工智能_机器学习 : Artificial-Intelligence 交通运输 : Transportation 科技_科学研究 : Technology-Research 法律_司法 : Law-Justice 金融_经济 : Finance-Economics 文学_情感 : Literature-Emotions 旅游_地理 : Travel-Geography 住宿_餐饮_酒店 : Hospitality-Catering 医疗 : Health-Medicine 学科教育 : Subject-Education 我们为每个数据集目录下面都提供了对应行业数据的 词云可视化和 数据质量分布曲线。如果需要单独行业的数据，可以跳转到单独的行业数据集地址… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/IndustryInstruction.

2024-09-19T10:35:48.000Z

66fd6222d935294087b8513e

KingNish/reasoning-base-20k

KingNish

{"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["reasoning", "synthetic"], "pretty_name": "Reasoning 20k Data", "size_categories": ["10K<n<100K"]}

False

2024-10-05T14:19:30.000Z

ae93576e3b315cf876e7429b7fa1fd041df72d29

Dataset Card for Reasoning Base 20k Dataset Details Dataset Description This dataset is designed to train a reasoning model. That can think through complex problems before providing a response, similar to how a human would. The dataset includes a wide range of problems from various domains (science, coding, math, etc.), each with a detailed chain of thought (COT) and the correct answer. The goal is to enable the model to learn and refine its… See the full description on the dataset page: https://huggingface.co/datasets/KingNish/reasoning-base-20k.

2024-10-02T15:09:22.000Z

6725a55725d211ee49b48352

RLHFlow/Mistral-PRM-Data

RLHFlow

{"dataset_info": {"features": [{"name": "conversations", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 240544210, "num_examples": 273226}], "download_size": 106069510, "dataset_size": 240544210}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

False

2024-11-09T18:52:44.000Z

a18b43d250c8d900b0857fecf2e74d2c87145e1a

See https://github.com/RLHFlow/RLHF-Reward-Modeling/tree/main/math-rm for more data information.

2024-11-02T04:06:47.000Z

672e801fa0fed24bd6a3843c

openlanguagedata/oldi_seed

openlanguagedata

{"annotations_creators": ["found"], "language_creators": ["expert-generated"], "license": "cc-by-sa-4.0", "pretty_name": "OLDI-Seed", "task_categories": ["text2text-generation", "translation"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "seed/*.parquet"}]}]}

False

2024-11-15T11:06:56.000Z

562611430fbfb4ca3accccf18176953fd87e3e72

OLDI Seed Machine Translation Datacard OLDI Seed is a machine translation dataset designed to be used to kick-start machine translation models for language directions which currently lack large-scale datasets. Dataset Details Dataset Description OLDI Seed is a parallel corpus which consists of 6,193 sentences sampled from English Wikipedia and translated into 44 languages. It can be used to kick-start machine translation models for language… See the full description on the dataset page: https://huggingface.co/datasets/openlanguagedata/oldi_seed.

2024-11-08T21:18:23.000Z

672ee266742ea2c59e3d6fac

jirvin16/TEOChatlas

jirvin16

{"license": "apache-2.0", "language": ["en"], "size_categories": ["100K<n<1M"]}

False

2024-11-14T19:13:18.000Z

c8462b433fe4a73144b71341a2c22dabe1afc04f

TEOChatlas is the first instruction-following dataset for temporal EO data. It contains 554,071 examples spanning dozens of temporal instruction-following tasks.

2024-11-09T04:17:42.000Z

@article{irvin2024teochat, title={TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data}, author={Irvin, Jeremy Andrew and Liu, Emily Ruoyu and Chen, Joyce Chuyi and Dormoy, Ines and Kim, Jinyoung and Khanna, Samar and Zheng, Zhuo and Ermon, Stefano}, journal={arXiv preprint arXiv:2410.06234}, year={2024} }

67364ee39929c7864dfe5043

babytreecc/ICLR2025review

babytreecc

{"license": "apache-2.0"}

False

2024-11-14T19:31:53.000Z

452fab7741febd072e4cb15f6c2015b463b98465

2024-11-14T19:26:27.000Z

621ffdd236468d709f181f09

Skylion007/openwebtext

Skylion007

{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": ["cc0-1.0"], "multilinguality": ["monolingual"], "pretty_name": "OpenWebText", "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "paperswithcode_id": "openwebtext", "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "config_name": "plain_text", "splits": [{"name": "train", "num_bytes": 39769491688, "num_examples": 8013769}], "download_size": 12880189440, "dataset_size": 39769491688}}

False

2024-05-17T17:56:27.000Z

f3808c30e817981b845ec549c43e82bb467d8144

An open-source replication of the WebText dataset from OpenAI.

2022-03-02T23:29:22.000Z

openwebtext

@misc{Gokaslan2019OpenWeb, title={OpenWebText Corpus}, author={Aaron Gokaslan*, Vanya Cohen*, Ellie Pavlick, Stefanie Tellex}, howpublished{\\url{http://Skylion007.github.io/OpenWebTextCorpus}}, year={2019} }

627007d3becab9e2dcf15a40

ILSVRC/imagenet-1k

ILSVRC

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["other"], "license_details": "imagenet-agreement", "multilinguality": ["monolingual"], "paperswithcode_id": "imagenet-1k-1", "pretty_name": "ImageNet", "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["image-classification"], "task_ids": ["multi-class-image-classification"], "extra_gated_prompt": "By clicking on \u201cAccess repository\u201d below, you also agree to ImageNet Terms of Access:\n[RESEARCHER_FULLNAME] (the \"Researcher\") has requested permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n1. Researcher shall use the Database only for non-commercial research and educational purposes.\n2. Princeton University, Stanford University and Hugging Face make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.\n3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, Stanford University and Hugging Face, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database.\n4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.\n5. Princeton University, Stanford University and Hugging Face reserve the right to terminate Researcher's access to the Database at any time.\n6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.\n7. The law of the State of New Jersey shall apply to all disputes under this agreement.", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "tench, Tinca tinca", "1": "goldfish, Carassius auratus", "2": "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias", "3": "tiger shark, Galeocerdo cuvieri", "4": "hammerhead, hammerhead shark", "5": "electric ray, crampfish, numbfish, torpedo", "6": "stingray", "7": "cock", "8": "hen", "9": "ostrich, Struthio camelus", "10": "brambling, Fringilla montifringilla", "11": "goldfinch, Carduelis carduelis", "12": "house finch, linnet, Carpodacus mexicanus", "13": "junco, snowbird", "14": "indigo bunting, indigo finch, indigo bird, Passerina cyanea", "15": "robin, American robin, Turdus migratorius", "16": "bulbul", "17": "jay", "18": "magpie", "19": "chickadee", "20": "water ouzel, dipper", "21": "kite", "22": "bald eagle, American eagle, Haliaeetus leucocephalus", "23": "vulture", "24": "great grey owl, great gray owl, Strix nebulosa", "25": "European fire salamander, Salamandra salamandra", "26": "common newt, Triturus vulgaris", "27": "eft", "28": "spotted salamander, Ambystoma maculatum", "29": "axolotl, mud puppy, Ambystoma mexicanum", "30": "bullfrog, Rana catesbeiana", "31": "tree frog, tree-frog", "32": "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui", "33": "loggerhead, loggerhead turtle, Caretta caretta", "34": "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea", "35": "mud turtle", "36": "terrapin", "37": "box turtle, box tortoise", "38": "banded gecko", "39": "common iguana, iguana, Iguana iguana", "40": "American chameleon, anole, Anolis carolinensis", "41": "whiptail, whiptail lizard", "42": "agama", "43": "frilled lizard, Chlamydosaurus kingi", "44": "alligator lizard", "45": "Gila monster, Heloderma suspectum", "46": "green lizard, Lacerta viridis", "47": "African chameleon, Chamaeleo chamaeleon", "48": "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis", "49": "African crocodile, Nile crocodile, Crocodylus niloticus", "50": "American alligator, Alligator mississipiensis", "51": "triceratops", "52": "thunder snake, worm snake, Carphophis amoenus", "53": "ringneck snake, ring-necked snake, ring snake", "54": "hognose snake, puff adder, sand viper", "55": "green snake, grass snake", "56": "king snake, kingsnake", "57": "garter snake, grass snake", "58": "water snake", "59": "vine snake", "60": "night snake, Hypsiglena torquata", "61": "boa constrictor, Constrictor constrictor", "62": "rock python, rock snake, Python sebae", "63": "Indian cobra, Naja naja", "64": "green mamba", "65": "sea snake", "66": "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus", "67": "diamondback, diamondback rattlesnake, Crotalus adamanteus", "68": "sidewinder, horned rattlesnake, Crotalus cerastes", "69": "trilobite", "70": "harvestman, daddy longlegs, Phalangium opilio", "71": "scorpion", "72": "black and gold garden spider, Argiope aurantia", "73": "barn spider, Araneus cavaticus", "74": "garden spider, Aranea diademata", "75": "black widow, Latrodectus mactans", "76": "tarantula", "77": "wolf spider, hunting spider", "78": "tick", "79": "centipede", "80": "black grouse", "81": "ptarmigan", "82": "ruffed grouse, partridge, Bonasa umbellus", "83": "prairie chicken, prairie grouse, prairie fowl", "84": "peacock", "85": "quail", "86": "partridge", "87": "African grey, African gray, Psittacus erithacus", "88": "macaw", "89": "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita", "90": "lorikeet", "91": "coucal", "92": "bee eater", "93": "hornbill", "94": "hummingbird", "95": "jacamar", "96": "toucan", "97": "drake", "98": "red-breasted merganser, Mergus serrator", "99": "goose", "100": "black swan, Cygnus atratus", "101": "tusker", "102": "echidna, spiny anteater, anteater", "103": "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus", "104": "wallaby, brush kangaroo", "105": "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus", "106": "wombat", "107": "jellyfish", "108": "sea anemone, anemone", "109": "brain coral", "110": "flatworm, platyhelminth", "111": "nematode, nematode worm, roundworm", "112": "conch", "113": "snail", "114": "slug", "115": "sea slug, nudibranch", "116": "chiton, coat-of-mail shell, sea cradle, polyplacophore", "117": "chambered nautilus, pearly nautilus, nautilus", "118": "Dungeness crab, Cancer magister", "119": "rock crab, Cancer irroratus", "120": "fiddler crab", "121": "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica", "122": "American lobster, Northern lobster, Maine lobster, Homarus americanus", "123": "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "124": "crayfish, crawfish, crawdad, crawdaddy", "125": "hermit crab", "126": "isopod", "127": "white stork, Ciconia ciconia", "128": "black stork, Ciconia nigra", "129": "spoonbill", "130": "flamingo", "131": "little blue heron, Egretta caerulea", "132": "American egret, great white heron, Egretta albus", "133": "bittern", "134": "crane", "135": "limpkin, Aramus pictus", "136": "European gallinule, Porphyrio porphyrio", "137": "American coot, marsh hen, mud hen, water hen, Fulica americana", "138": "bustard", "139": "ruddy turnstone, Arenaria interpres", "140": "red-backed sandpiper, dunlin, Erolia alpina", "141": "redshank, Tringa totanus", "142": "dowitcher", "143": "oystercatcher, oyster catcher", "144": "pelican", "145": "king penguin, Aptenodytes patagonica", "146": "albatross, mollymawk", "147": "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus", "148": "killer whale, killer, orca, grampus, sea wolf, Orcinus orca", "149": "dugong, Dugong dugon", "150": "sea lion", "151": "Chihuahua", "152": "Japanese spaniel", "153": "Maltese dog, Maltese terrier, Maltese", "154": "Pekinese, Pekingese, Peke", "155": "Shih-Tzu", "156": "Blenheim spaniel", "157": "papillon", "158": "toy terrier", "159": "Rhodesian ridgeback", "160": "Afghan hound, Afghan", "161": "basset, basset hound", "162": "beagle", "163": "bloodhound, sleuthhound", "164": "bluetick", "165": "black-and-tan coonhound", "166": "Walker hound, Walker foxhound", "167": "English foxhound", "168": "redbone", "169": "borzoi, Russian wolfhound", "170": "Irish wolfhound", "171": "Italian greyhound", "172": "whippet", "173": "Ibizan hound, Ibizan Podenco", "174": "Norwegian elkhound, elkhound", "175": "otterhound, otter hound", "176": "Saluki, gazelle hound", "177": "Scottish deerhound, deerhound", "178": "Weimaraner", "179": "Staffordshire bullterrier, Staffordshire bull terrier", "180": "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier", "181": "Bedlington terrier", "182": "Border terrier", "183": "Kerry blue terrier", "184": "Irish terrier", "185": "Norfolk terrier", "186": "Norwich terrier", "187": "Yorkshire terrier", "188": "wire-haired fox terrier", "189": "Lakeland terrier", "190": "Sealyham terrier, Sealyham", "191": "Airedale, Airedale terrier", "192": "cairn, cairn terrier", "193": "Australian terrier", "194": "Dandie Dinmont, Dandie Dinmont terrier", "195": "Boston bull, Boston terrier", "196": "miniature schnauzer", "197": "giant schnauzer", "198": "standard schnauzer", "199": "Scotch terrier, Scottish terrier, Scottie", "200": "Tibetan terrier, chrysanthemum dog", "201": "silky terrier, Sydney silky", "202": "soft-coated wheaten terrier", "203": "West Highland white terrier", "204": "Lhasa, Lhasa apso", "205": "flat-coated retriever", "206": "curly-coated retriever", "207": "golden retriever", "208": "Labrador retriever", "209": "Chesapeake Bay retriever", "210": "German short-haired pointer", "211": "vizsla, Hungarian pointer", "212": "English setter", "213": "Irish setter, red setter", "214": "Gordon setter", "215": "Brittany spaniel", "216": "clumber, clumber spaniel", "217": "English springer, English springer spaniel", "218": "Welsh springer spaniel", "219": "cocker spaniel, English cocker spaniel, cocker", "220": "Sussex spaniel", "221": "Irish water spaniel", "222": "kuvasz", "223": "schipperke", "224": "groenendael", "225": "malinois", "226": "briard", "227": "kelpie", "228": "komondor", "229": "Old English sheepdog, bobtail", "230": "Shetland sheepdog, Shetland sheep dog, Shetland", "231": "collie", "232": "Border collie", "233": "Bouvier des Flandres, Bouviers des Flandres", "234": "Rottweiler", "235": "German shepherd, German shepherd dog, German police dog, alsatian", "236": "Doberman, Doberman pinscher", "237": "miniature pinscher", "238": "Greater Swiss Mountain dog", "239": "Bernese mountain dog", "240": "Appenzeller", "241": "EntleBucher", "242": "boxer", "243": "bull mastiff", "244": "Tibetan mastiff", "245": "French bulldog", "246": "Great Dane", "247": "Saint Bernard, St Bernard", "248": "Eskimo dog, husky", "249": "malamute, malemute, Alaskan malamute", "250": "Siberian husky", "251": "dalmatian, coach dog, carriage dog", "252": "affenpinscher, monkey pinscher, monkey dog", "253": "basenji", "254": "pug, pug-dog", "255": "Leonberg", "256": "Newfoundland, Newfoundland dog", "257": "Great Pyrenees", "258": "Samoyed, Samoyede", "259": "Pomeranian", "260": "chow, chow chow", "261": "keeshond", "262": "Brabancon griffon", "263": "Pembroke, Pembroke Welsh corgi", "264": "Cardigan, Cardigan Welsh corgi", "265": "toy poodle", "266": "miniature poodle", "267": "standard poodle", "268": "Mexican hairless", "269": "timber wolf, grey wolf, gray wolf, Canis lupus", "270": "white wolf, Arctic wolf, Canis lupus tundrarum", "271": "red wolf, maned wolf, Canis rufus, Canis niger", "272": "coyote, prairie wolf, brush wolf, Canis latrans", "273": "dingo, warrigal, warragal, Canis dingo", "274": "dhole, Cuon alpinus", "275": "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus", "276": "hyena, hyaena", "277": "red fox, Vulpes vulpes", "278": "kit fox, Vulpes macrotis", "279": "Arctic fox, white fox, Alopex lagopus", "280": "grey fox, gray fox, Urocyon cinereoargenteus", "281": "tabby, tabby cat", "282": "tiger cat", "283": "Persian cat", "284": "Siamese cat, Siamese", "285": "Egyptian cat", "286": "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor", "287": "lynx, catamount", "288": "leopard, Panthera pardus", "289": "snow leopard, ounce, Panthera uncia", "290": "jaguar, panther, Panthera onca, Felis onca", "291": "lion, king of beasts, Panthera leo", "292": "tiger, Panthera tigris", "293": "cheetah, chetah, Acinonyx jubatus", "294": "brown bear, bruin, Ursus arctos", "295": "American black bear, black bear, Ursus americanus, Euarctos americanus", "296": "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus", "297": "sloth bear, Melursus ursinus, Ursus ursinus", "298": "mongoose", "299": "meerkat, mierkat", "300": "tiger beetle", "301": "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "302": "ground beetle, carabid beetle", "303": "long-horned beetle, longicorn, longicorn beetle", "304": "leaf beetle, chrysomelid", "305": "dung beetle", "306": "rhinoceros beetle", "307": "weevil", "308": "fly", "309": "bee", "310": "ant, emmet, pismire", "311": "grasshopper, hopper", "312": "cricket", "313": "walking stick, walkingstick, stick insect", "314": "cockroach, roach", "315": "mantis, mantid", "316": "cicada, cicala", "317": "leafhopper", "318": "lacewing, lacewing fly", "319": "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "320": "damselfly", "321": "admiral", "322": "ringlet, ringlet butterfly", "323": "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus", "324": "cabbage butterfly", "325": "sulphur butterfly, sulfur butterfly", "326": "lycaenid, lycaenid butterfly", "327": "starfish, sea star", "328": "sea urchin", "329": "sea cucumber, holothurian", "330": "wood rabbit, cottontail, cottontail rabbit", "331": "hare", "332": "Angora, Angora rabbit", "333": "hamster", "334": "porcupine, hedgehog", "335": "fox squirrel, eastern fox squirrel, Sciurus niger", "336": "marmot", "337": "beaver", "338": "guinea pig, Cavia cobaya", "339": "sorrel", "340": "zebra", "341": "hog, pig, grunter, squealer, Sus scrofa", "342": "wild boar, boar, Sus scrofa", "343": "warthog", "344": "hippopotamus, hippo, river horse, Hippopotamus amphibius", "345": "ox", "346": "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis", "347": "bison", "348": "ram, tup", "349": "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis", "350": "ibex, Capra ibex", "351": "hartebeest", "352": "impala, Aepyceros melampus", "353": "gazelle", "354": "Arabian camel, dromedary, Camelus dromedarius", "355": "llama", "356": "weasel", "357": "mink", "358": "polecat, fitch, foulmart, foumart, Mustela putorius", "359": "black-footed ferret, ferret, Mustela nigripes", "360": "otter", "361": "skunk, polecat, wood pussy", "362": "badger", "363": "armadillo", "364": "three-toed sloth, ai, Bradypus tridactylus", "365": "orangutan, orang, orangutang, Pongo pygmaeus", "366": "gorilla, Gorilla gorilla", "367": "chimpanzee, chimp, Pan troglodytes", "368": "gibbon, Hylobates lar", "369": "siamang, Hylobates syndactylus, Symphalangus syndactylus", "370": "guenon, guenon monkey", "371": "patas, hussar monkey, Erythrocebus patas", "372": "baboon", "373": "macaque", "374": "langur", "375": "colobus, colobus monkey", "376": "proboscis monkey, Nasalis larvatus", "377": "marmoset", "378": "capuchin, ringtail, Cebus capucinus", "379": "howler monkey, howler", "380": "titi, titi monkey", "381": "spider monkey, Ateles geoffroyi", "382": "squirrel monkey, Saimiri sciureus", "383": "Madagascar cat, ring-tailed lemur, Lemur catta", "384": "indri, indris, Indri indri, Indri brevicaudatus", "385": "Indian elephant, Elephas maximus", "386": "African elephant, Loxodonta africana", "387": "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens", "388": "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca", "389": "barracouta, snoek", "390": "eel", "391": "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch", "392": "rock beauty, Holocanthus tricolor", "393": "anemone fish", "394": "sturgeon", "395": "gar, garfish, garpike, billfish, Lepisosteus osseus", "396": "lionfish", "397": "puffer, pufferfish, blowfish, globefish", "398": "abacus", "399": "abaya", "400": "academic gown, academic robe, judge's robe", "401": "accordion, piano accordion, squeeze box", "402": "acoustic guitar", "403": "aircraft carrier, carrier, flattop, attack aircraft carrier", "404": "airliner", "405": "airship, dirigible", "406": "altar", "407": "ambulance", "408": "amphibian, amphibious vehicle", "409": "analog clock", "410": "apiary, bee house", "411": "apron", "412": "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "413": "assault rifle, assault gun", "414": "backpack, back pack, knapsack, packsack, rucksack, haversack", "415": "bakery, bakeshop, bakehouse", "416": "balance beam, beam", "417": "balloon", "418": "ballpoint, ballpoint pen, ballpen, Biro", "419": "Band Aid", "420": "banjo", "421": "bannister, banister, balustrade, balusters, handrail", "422": "barbell", "423": "barber chair", "424": "barbershop", "425": "barn", "426": "barometer", "427": "barrel, cask", "428": "barrow, garden cart, lawn cart, wheelbarrow", "429": "baseball", "430": "basketball", "431": "bassinet", "432": "bassoon", "433": "bathing cap, swimming cap", "434": "bath towel", "435": "bathtub, bathing tub, bath, tub", "436": "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "437": "beacon, lighthouse, beacon light, pharos", "438": "beaker", "439": "bearskin, busby, shako", "440": "beer bottle", "441": "beer glass", "442": "bell cote, bell cot", "443": "bib", "444": "bicycle-built-for-two, tandem bicycle, tandem", "445": "bikini, two-piece", "446": "binder, ring-binder", "447": "binoculars, field glasses, opera glasses", "448": "birdhouse", "449": "boathouse", "450": "bobsled, bobsleigh, bob", "451": "bolo tie, bolo, bola tie, bola", "452": "bonnet, poke bonnet", "453": "bookcase", "454": "bookshop, bookstore, bookstall", "455": "bottlecap", "456": "bow", "457": "bow tie, bow-tie, bowtie", "458": "brass, memorial tablet, plaque", "459": "brassiere, bra, bandeau", "460": "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "461": "breastplate, aegis, egis", "462": "broom", "463": "bucket, pail", "464": "buckle", "465": "bulletproof vest", "466": "bullet train, bullet", "467": "butcher shop, meat market", "468": "cab, hack, taxi, taxicab", "469": "caldron, cauldron", "470": "candle, taper, wax light", "471": "cannon", "472": "canoe", "473": "can opener, tin opener", "474": "cardigan", "475": "car mirror", "476": "carousel, carrousel, merry-go-round, roundabout, whirligig", "477": "carpenter's kit, tool kit", "478": "carton", "479": "car wheel", "480": "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM", "481": "cassette", "482": "cassette player", "483": "castle", "484": "catamaran", "485": "CD player", "486": "cello, violoncello", "487": "cellular telephone, cellular phone, cellphone, cell, mobile phone", "488": "chain", "489": "chainlink fence", "490": "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "491": "chain saw, chainsaw", "492": "chest", "493": "chiffonier, commode", "494": "chime, bell, gong", "495": "china cabinet, china closet", "496": "Christmas stocking", "497": "church, church building", "498": "cinema, movie theater, movie theatre, movie house, picture palace", "499": "cleaver, meat cleaver, chopper", "500": "cliff dwelling", "501": "cloak", "502": "clog, geta, patten, sabot", "503": "cocktail shaker", "504": "coffee mug", "505": "coffeepot", "506": "coil, spiral, volute, whorl, helix", "507": "combination lock", "508": "computer keyboard, keypad", "509": "confectionery, confectionary, candy store", "510": "container ship, containership, container vessel", "511": "convertible", "512": "corkscrew, bottle screw", "513": "cornet, horn, trumpet, trump", "514": "cowboy boot", "515": "cowboy hat, ten-gallon hat", "516": "cradle", "517": "crane2", "518": "crash helmet", "519": "crate", "520": "crib, cot", "521": "Crock Pot", "522": "croquet ball", "523": "crutch", "524": "cuirass", "525": "dam, dike, dyke", "526": "desk", "527": "desktop computer", "528": "dial telephone, dial phone", "529": "diaper, nappy, napkin", "530": "digital clock", "531": "digital watch", "532": "dining table, board", "533": "dishrag, dishcloth", "534": "dishwasher, dish washer, dishwashing machine", "535": "disk brake, disc brake", "536": "dock, dockage, docking facility", "537": "dogsled, dog sled, dog sleigh", "538": "dome", "539": "doormat, welcome mat", "540": "drilling platform, offshore rig", "541": "drum, membranophone, tympan", "542": "drumstick", "543": "dumbbell", "544": "Dutch oven", "545": "electric fan, blower", "546": "electric guitar", "547": "electric locomotive", "548": "entertainment center", "549": "envelope", "550": "espresso maker", "551": "face powder", "552": "feather boa, boa", "553": "file, file cabinet, filing cabinet", "554": "fireboat", "555": "fire engine, fire truck", "556": "fire screen, fireguard", "557": "flagpole, flagstaff", "558": "flute, transverse flute", "559": "folding chair", "560": "football helmet", "561": "forklift", "562": "fountain", "563": "fountain pen", "564": "four-poster", "565": "freight car", "566": "French horn, horn", "567": "frying pan, frypan, skillet", "568": "fur coat", "569": "garbage truck, dustcart", "570": "gasmask, respirator, gas helmet", "571": "gas pump, gasoline pump, petrol pump, island dispenser", "572": "goblet", "573": "go-kart", "574": "golf ball", "575": "golfcart, golf cart", "576": "gondola", "577": "gong, tam-tam", "578": "gown", "579": "grand piano, grand", "580": "greenhouse, nursery, glasshouse", "581": "grille, radiator grille", "582": "grocery store, grocery, food market, market", "583": "guillotine", "584": "hair slide", "585": "hair spray", "586": "half track", "587": "hammer", "588": "hamper", "589": "hand blower, blow dryer, blow drier, hair dryer, hair drier", "590": "hand-held computer, hand-held microcomputer", "591": "handkerchief, hankie, hanky, hankey", "592": "hard disc, hard disk, fixed disk", "593": "harmonica, mouth organ, harp, mouth harp", "594": "harp", "595": "harvester, reaper", "596": "hatchet", "597": "holster", "598": "home theater, home theatre", "599": "honeycomb", "600": "hook, claw", "601": "hoopskirt, crinoline", "602": "horizontal bar, high bar", "603": "horse cart, horse-cart", "604": "hourglass", "605": "iPod", "606": "iron, smoothing iron", "607": "jack-o'-lantern", "608": "jean, blue jean, denim", "609": "jeep, landrover", "610": "jersey, T-shirt, tee shirt", "611": "jigsaw puzzle", "612": "jinrikisha, ricksha, rickshaw", "613": "joystick", "614": "kimono", "615": "knee pad", "616": "knot", "617": "lab coat, laboratory coat", "618": "ladle", "619": "lampshade, lamp shade", "620": "laptop, laptop computer", "621": "lawn mower, mower", "622": "lens cap, lens cover", "623": "letter opener, paper knife, paperknife", "624": "library", "625": "lifeboat", "626": "lighter, light, igniter, ignitor", "627": "limousine, limo", "628": "liner, ocean liner", "629": "lipstick, lip rouge", "630": "Loafer", "631": "lotion", "632": "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "633": "loupe, jeweler's loupe", "634": "lumbermill, sawmill", "635": "magnetic compass", "636": "mailbag, postbag", "637": "mailbox, letter box", "638": "maillot", "639": "maillot, tank suit", "640": "manhole cover", "641": "maraca", "642": "marimba, xylophone", "643": "mask", "644": "matchstick", "645": "maypole", "646": "maze, labyrinth", "647": "measuring cup", "648": "medicine chest, medicine cabinet", "649": "megalith, megalithic structure", "650": "microphone, mike", "651": "microwave, microwave oven", "652": "military uniform", "653": "milk can", "654": "minibus", "655": "miniskirt, mini", "656": "minivan", "657": "missile", "658": "mitten", "659": "mixing bowl", "660": "mobile home, manufactured home", "661": "Model T", "662": "modem", "663": "monastery", "664": "monitor", "665": "moped", "666": "mortar", "667": "mortarboard", "668": "mosque", "669": "mosquito net", "670": "motor scooter, scooter", "671": "mountain bike, all-terrain bike, off-roader", "672": "mountain tent", "673": "mouse, computer mouse", "674": "mousetrap", "675": "moving van", "676": "muzzle", "677": "nail", "678": "neck brace", "679": "necklace", "680": "nipple", "681": "notebook, notebook computer", "682": "obelisk", "683": "oboe, hautboy, hautbois", "684": "ocarina, sweet potato", "685": "odometer, hodometer, mileometer, milometer", "686": "oil filter", "687": "organ, pipe organ", "688": "oscilloscope, scope, cathode-ray oscilloscope, CRO", "689": "overskirt", "690": "oxcart", "691": "oxygen mask", "692": "packet", "693": "paddle, boat paddle", "694": "paddlewheel, paddle wheel", "695": "padlock", "696": "paintbrush", "697": "pajama, pyjama, pj's, jammies", "698": "palace", "699": "panpipe, pandean pipe, syrinx", "700": "paper towel", "701": "parachute, chute", "702": "parallel bars, bars", "703": "park bench", "704": "parking meter", "705": "passenger car, coach, carriage", "706": "patio, terrace", "707": "pay-phone, pay-station", "708": "pedestal, plinth, footstall", "709": "pencil box, pencil case", "710": "pencil sharpener", "711": "perfume, essence", "712": "Petri dish", "713": "photocopier", "714": "pick, plectrum, plectron", "715": "pickelhaube", "716": "picket fence, paling", "717": "pickup, pickup truck", "718": "pier", "719": "piggy bank, penny bank", "720": "pill bottle", "721": "pillow", "722": "ping-pong ball", "723": "pinwheel", "724": "pirate, pirate ship", "725": "pitcher, ewer", "726": "plane, carpenter's plane, woodworking plane", "727": "planetarium", "728": "plastic bag", "729": "plate rack", "730": "plow, plough", "731": "plunger, plumber's helper", "732": "Polaroid camera, Polaroid Land camera", "733": "pole", "734": "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria", "735": "poncho", "736": "pool table, billiard table, snooker table", "737": "pop bottle, soda bottle", "738": "pot, flowerpot", "739": "potter's wheel", "740": "power drill", "741": "prayer rug, prayer mat", "742": "printer", "743": "prison, prison house", "744": "projectile, missile", "745": "projector", "746": "puck, hockey puck", "747": "punching bag, punch bag, punching ball, punchball", "748": "purse", "749": "quill, quill pen", "750": "quilt, comforter, comfort, puff", "751": "racer, race car, racing car", "752": "racket, racquet", "753": "radiator", "754": "radio, wireless", "755": "radio telescope, radio reflector", "756": "rain barrel", "757": "recreational vehicle, RV, R.V.", "758": "reel", "759": "reflex camera", "760": "refrigerator, icebox", "761": "remote control, remote", "762": "restaurant, eating house, eating place, eatery", "763": "revolver, six-gun, six-shooter", "764": "rifle", "765": "rocking chair, rocker", "766": "rotisserie", "767": "rubber eraser, rubber, pencil eraser", "768": "rugby ball", "769": "rule, ruler", "770": "running shoe", "771": "safe", "772": "safety pin", "773": "saltshaker, salt shaker", "774": "sandal", "775": "sarong", "776": "sax, saxophone", "777": "scabbard", "778": "scale, weighing machine", "779": "school bus", "780": "schooner", "781": "scoreboard", "782": "screen, CRT screen", "783": "screw", "784": "screwdriver", "785": "seat belt, seatbelt", "786": "sewing machine", "787": "shield, buckler", "788": "shoe shop, shoe-shop, shoe store", "789": "shoji", "790": "shopping basket", "791": "shopping cart", "792": "shovel", "793": "shower cap", "794": "shower curtain", "795": "ski", "796": "ski mask", "797": "sleeping bag", "798": "slide rule, slipstick", "799": "sliding door", "800": "slot, one-armed bandit", "801": "snorkel", "802": "snowmobile", "803": "snowplow, snowplough", "804": "soap dispenser", "805": "soccer ball", "806": "sock", "807": "solar dish, solar collector, solar furnace", "808": "sombrero", "809": "soup bowl", "810": "space bar", "811": "space heater", "812": "space shuttle", "813": "spatula", "814": "speedboat", "815": "spider web, spider's web", "816": "spindle", "817": "sports car, sport car", "818": "spotlight, spot", "819": "stage", "820": "steam locomotive", "821": "steel arch bridge", "822": "steel drum", "823": "stethoscope", "824": "stole", "825": "stone wall", "826": "stopwatch, stop watch", "827": "stove", "828": "strainer", "829": "streetcar, tram, tramcar, trolley, trolley car", "830": "stretcher", "831": "studio couch, day bed", "832": "stupa, tope", "833": "submarine, pigboat, sub, U-boat", "834": "suit, suit of clothes", "835": "sundial", "836": "sunglass", "837": "sunglasses, dark glasses, shades", "838": "sunscreen, sunblock, sun blocker", "839": "suspension bridge", "840": "swab, swob, mop", "841": "sweatshirt", "842": "swimming trunks, bathing trunks", "843": "swing", "844": "switch, electric switch, electrical switch", "845": "syringe", "846": "table lamp", "847": "tank, army tank, armored combat vehicle, armoured combat vehicle", "848": "tape player", "849": "teapot", "850": "teddy, teddy bear", "851": "television, television system", "852": "tennis ball", "853": "thatch, thatched roof", "854": "theater curtain, theatre curtain", "855": "thimble", "856": "thresher, thrasher, threshing machine", "857": "throne", "858": "tile roof", "859": "toaster", "860": "tobacco shop, tobacconist shop, tobacconist", "861": "toilet seat", "862": "torch", "863": "totem pole", "864": "tow truck, tow car, wrecker", "865": "toyshop", "866": "tractor", "867": "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "868": "tray", "869": "trench coat", "870": "tricycle, trike, velocipede", "871": "trimaran", "872": "tripod", "873": "triumphal arch", "874": "trolleybus, trolley coach, trackless trolley", "875": "trombone", "876": "tub, vat", "877": "turnstile", "878": "typewriter keyboard", "879": "umbrella", "880": "unicycle, monocycle", "881": "upright, upright piano", "882": "vacuum, vacuum cleaner", "883": "vase", "884": "vault", "885": "velvet", "886": "vending machine", "887": "vestment", "888": "viaduct", "889": "violin, fiddle", "890": "volleyball", "891": "waffle iron", "892": "wall clock", "893": "wallet, billfold, notecase, pocketbook", "894": "wardrobe, closet, press", "895": "warplane, military plane", "896": "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "897": "washer, automatic washer, washing machine", "898": "water bottle", "899": "water jug", "900": "water tower", "901": "whiskey jug", "902": "whistle", "903": "wig", "904": "window screen", "905": "window shade", "906": "Windsor tie", "907": "wine bottle", "908": "wing", "909": "wok", "910": "wooden spoon", "911": "wool, woolen, woollen", "912": "worm fence, snake fence, snake-rail fence, Virginia fence", "913": "wreck", "914": "yawl", "915": "yurt", "916": "web site, website, internet site, site", "917": "comic book", "918": "crossword puzzle, crossword", "919": "street sign", "920": "traffic light, traffic signal, stoplight", "921": "book jacket, dust cover, dust jacket, dust wrapper", "922": "menu", "923": "plate", "924": "guacamole", "925": "consomme", "926": "hot pot, hotpot", "927": "trifle", "928": "ice cream, icecream", "929": "ice lolly, lolly, lollipop, popsicle", "930": "French loaf", "931": "bagel, beigel", "932": "pretzel", "933": "cheeseburger", "934": "hotdog, hot dog, red hot", "935": "mashed potato", "936": "head cabbage", "937": "broccoli", "938": "cauliflower", "939": "zucchini, courgette", "940": "spaghetti squash", "941": "acorn squash", "942": "butternut squash", "943": "cucumber, cuke", "944": "artichoke, globe artichoke", "945": "bell pepper", "946": "cardoon", "947": "mushroom", "948": "Granny Smith", "949": "strawberry", "950": "orange", "951": "lemon", "952": "fig", "953": "pineapple, ananas", "954": "banana", "955": "jackfruit, jak, jack", "956": "custard apple", "957": "pomegranate", "958": "hay", "959": "carbonara", "960": "chocolate sauce, chocolate syrup", "961": "dough", "962": "meat loaf, meatloaf", "963": "pizza, pizza pie", "964": "potpie", "965": "burrito", "966": "red wine", "967": "espresso", "968": "cup", "969": "eggnog", "970": "alp", "971": "bubble", "972": "cliff, drop, drop-off", "973": "coral reef", "974": "geyser", "975": "lakeside, lakeshore", "976": "promontory, headland, head, foreland", "977": "sandbar, sand bar", "978": "seashore, coast, seacoast, sea-coast", "979": "valley, vale", "980": "volcano", "981": "ballplayer, baseball player", "982": "groom, bridegroom", "983": "scuba diver", "984": "rapeseed", "985": "daisy", "986": "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", "987": "corn", "988": "acorn", "989": "hip, rose hip, rosehip", "990": "buckeye, horse chestnut, conker", "991": "coral fungus", "992": "agaric", "993": "gyromitra", "994": "stinkhorn, carrion fungus", "995": "earthstar", "996": "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa", "997": "bolete", "998": "ear, spike, capitulum", "999": "toilet tissue, toilet paper, bathroom tissue"}}}}], "splits": [{"name": "test", "num_bytes": 13613661561, "num_examples": 100000}, {"name": "train", "num_bytes": 146956944242, "num_examples": 1281167}, {"name": "validation", "num_bytes": 6709003386, "num_examples": 50000}], "download_size": 166009941208, "dataset_size": 167279609189}}

auto

2024-07-16T13:30:57.000Z

4603483700ee984ea9debe3ddbfdeae86f6489eb

ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated. In its completion, ImageNet hopes to offer tens of millions of cleanly sorted images for most of the concepts in the WordNet hierarchy. ImageNet 2012 is the most commonly used subset of ImageNet. This dataset spans 1000 object classes and contains 1,281,167 training images, 50,000 validation images and 100,000 test images

2022-05-02T16:33:23.000Z

imagenet-1k-1

@article{imagenet15russakovsky, Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei}, Title = { {ImageNet Large Scale Visual Recognition Challenge} }, Year = {2015}, journal = {International Journal of Computer Vision (IJCV)}, doi = {10.1007/s11263-015-0816-y}, volume={115}, number={3}, pages={211-252} }

627651b12c670b8e9d7aa0bd

laion/laion-high-resolution

laion

auto

2024-07-14T07:40:14.000Z

a4d20d6dfd2a1664aabaf6df80fcf740d180cc62

2022-05-07T11:02:09.000Z

62bcdb618d752f69a96e34eb

codeparrot/github-code-clean

codeparrot

{"license": "apache-2.0"}

False

2022-07-05T09:35:14.000Z

c48d40f9e70f0196f8236901ee35807f7d6c44c0

The GitHub Code clean dataset in a more filtered version of codeparrot/github-code dataset, it consists of 115M code files from GitHub in 32 programming languages with 60 extensions totaling in almost 1TB of text data.

2022-06-29T23:08:17.000Z

62d6d5afa543b9b79216a8a6

deepmind/code_contests

deepmind

{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "paperswithcode_id": "codecontests", "pretty_name": "CodeContests"}

False

2023-06-11T12:22:30.000Z

802411c3010cb00d1b05bad57ca77365a3c699d6

Dataset Card for CodeContests Dataset Summary CodeContests is a competitive programming dataset for machine-learning. This dataset was used when training AlphaCode. It consists of programming problems, from a variety of sources: Site URL Source Aizu https://judge.u-aizu.ac.jp CodeNet AtCoder https://atcoder.jp CodeNet CodeChef https://www.codechef.com description2code Codeforces https://codeforces.com description2code and Codeforces HackerEarth… See the full description on the dataset page: https://huggingface.co/datasets/deepmind/code_contests.

2022-07-19T16:02:55.000Z

codecontests

64358e2179c45fcf1ada09f4

databricks/databricks-dolly-15k

databricks

{"license": "cc-by-sa-3.0", "task_categories": ["question-answering", "summarization"], "language": ["en"], "size_categories": ["10K<n<100K"]}

False

2023-06-30T18:34:13.000Z

bdd27f4d94b9c1f951818a7da7fd7aeea5dbff1a

Summary databricks-dolly-15k is an open source dataset of instruction-following records generated by thousands of Databricks employees in several of the behavioral categories outlined in the InstructGPT paper, including brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization. This dataset can be used for any purpose, whether academic or commercial, under the terms of the Creative Commons Attribution-ShareAlike 3.0 Unported… See the full description on the dataset page: https://huggingface.co/datasets/databricks/databricks-dolly-15k.

2023-04-11T16:43:13.000Z

64415710006550f1ed6fe5b8

stingning/ultrachat

stingning

{"license": "mit", "task_categories": ["conversational", "text-generation"], "language": ["en"], "size_categories": ["1M<n<10M"], "pretty_name": "UltraChat"}

False

2024-02-22T02:26:29.000Z

f220fe796ce3ed62fbe1681b45ce6cbc9c6cabe0

Dataset Card for Dataset Name Dataset Description An open-source, large-scale, and multi-round dialogue data powered by Turbo APIs. In consideration of factors such as safeguarding privacy, we do not directly use any data available on the Internet as prompts. To ensure generation quality, two separate ChatGPT Turbo APIs are adopted in generation, where one plays the role of the user to generate queries and the other generates the response. We instruct the user… See the full description on the dataset page: https://huggingface.co/datasets/stingning/ultrachat.

2023-04-20T15:15:28.000Z

647b4f534d7c0c3fcccee09c

flaviagiammarino/vqa-rad

flaviagiammarino

{"license": "cc0-1.0", "task_categories": ["visual-question-answering"], "language": ["en"], "paperswithcode_id": "vqa-rad", "tags": ["medical"], "pretty_name": "VQA-RAD", "size_categories": ["1K<n<10K"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 95883938.139, "num_examples": 1793}, {"name": "test", "num_bytes": 23818877, "num_examples": 451}], "download_size": 34496718, "dataset_size": 119702815.139}}

False

2023-06-03T18:38:48.000Z

bcf91e7654fb9d51c8ab6a5b82cacf3fafd2fae9

Dataset Card for VQA-RAD Dataset Description VQA-RAD is a dataset of question-answer pairs on radiology images. The dataset is intended to be used for training and testing Medical Visual Question Answering (VQA) systems. The dataset includes both open-ended questions and binary "yes/no" questions. The dataset is built from MedPix, which is a free open-access online database of medical images. The question-answer pairs were manually generated by a team of… See the full description on the dataset page: https://huggingface.co/datasets/flaviagiammarino/vqa-rad.

2023-06-03T14:33:55.000Z

vqa-rad

649444227853dd12c3bbadd8

Amod/mental_health_counseling_conversations

Amod

{"license": "openrail", "task_categories": ["text-generation", "question-answering"], "language": ["en"], "tags": ["medical"], "size_categories": ["1K<n<10K"]}

False

2024-04-05T08:30:03.000Z

4672e03c7f1a7b2215eb4302b83ca50449ce2553

Amod/mental_health_counseling_conversations Dataset Summary This dataset is a collection of questions and answers sourced from two online counseling and therapy platforms. The questions cover a wide range of mental health topics, and the answers are provided by qualified psychologists. The dataset is intended to be used for fine-tuning language models to improve their ability to provide mental health advice. Supported Tasks and Leaderboards The… See the full description on the dataset page: https://huggingface.co/datasets/Amod/mental_health_counseling_conversations.

2023-06-22T12:52:50.000Z

64c523313f3387bcfa19d940

THUDM/LongBench

THUDM

{"task_categories": ["question-answering", "text-generation", "summarization", "conversational", "text-classification"], "language": ["en", "zh"], "tags": ["Long Context"], "size_categories": ["1K<n<10K"]}

False

2023-08-29T04:51:14.000Z

f72191f71cd6fcd0da8a54f0915078efda579449

LongBench is a comprehensive benchmark for multilingual and multi-task purposes, with the goal to fully measure and evaluate the ability of pre-trained language models to understand long text. This dataset consists of twenty different tasks, covering key long-text application scenarios such as multi-document QA, single-document QA, summarization, few-shot learning, synthetic tasks, and code completion.

2023-07-29T14:33:21.000Z

64da299a1d19239f503eb299

neural-bridge/rag-hallucination-dataset-1000

neural-bridge

{"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2917432.8, "num_examples": 800}, {"name": "test", "num_bytes": 729358.2, "num_examples": 200}], "download_size": 2300801, "dataset_size": 3646791}, "task_categories": ["question-answering"], "language": ["en"], "size_categories": ["1K<n<10K"], "license": "apache-2.0", "tags": ["retrieval-augmented-generation", "hallucination"]}

False

2024-02-05T18:26:49.000Z

b6b03f0f204ae0dd476094d6382d319a99ca93f3

Retrieval-Augmented Generation (RAG) Hallucination Dataset 1000 Retrieval-Augmented Generation (RAG) Hallucination Dataset 1000 is an English dataset designed to reduce the hallucination in RAG-optimized models, built by Neural Bridge AI, and released under Apache license 2.0. Dataset Description Dataset Summary Hallucination in large language models (LLMs) refers to the generation of incorrect, nonsensical, or unrelated text that does not stem from… See the full description on the dataset page: https://huggingface.co/datasets/neural-bridge/rag-hallucination-dataset-1000.

2023-08-14T13:18:18.000Z

64e80de1d021ea7dfaa73d23

clouditera/security-paper-datasets

clouditera

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1690579945, "num_examples": 428155}], "download_size": 751689097, "dataset_size": 1690579945}}

False

2023-10-16T10:34:13.000Z

885885f783710efc13e62c0667fb4c86b0f6e465

Dataset Card for "security-paper-datasets" More Information needed

2023-08-25T02:11:45.000Z

64ef8ede6c34f89ab1791292

monology/pile-uncopyrighted

monology

{"license": "other"}

False

2023-08-31T03:45:38.000Z

3be90335b66f24456a5d6659d9c8d208c0357119

Pile Uncopyrighted In response to authors demanding that LLMs stop using their works, here's a copy of The Pile with all copyrighted content removed.Please consider using this dataset to train your future LLMs, to respect authors and abide by copyright law.Creating an uncopyrighted version of a larger dataset (ie RedPajama) is planned, with no ETA. MethodologyCleaning was performed by removing everything from the Books3, BookCorpus2, OpenSubtitles, YTSubtitles, and OWT2… See the full description on the dataset page: https://huggingface.co/datasets/monology/pile-uncopyrighted.

2023-08-30T18:47:58.000Z

65b06a4e0eef9221e4d77da4

math-ai/AutoMathText

math-ai

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"code-python-0.70-to-1.00", "data_files": [{"split": "train", "path": ["data/code/python/0.95-1.00.jsonl", "data/code/python/0.90-0.95.jsonl", "data/code/python/0.85-0.90.jsonl", "data/code/python/0.80-0.85.jsonl", "data/code/python/0.75-0.80.jsonl", "data/code/python/0.70-0.75.jsonl"]}]}, {"config_name": "code-python-0.80-to-1.00", "data_files": [{"split": "train", "path": ["data/code/python/0.95-1.00.jsonl", "data/code/python/0.90-0.95.jsonl", "data/code/python/0.85-0.90.jsonl", "data/code/python/0.80-0.85.jsonl"]}]}, {"config_name": "code-jupyter-notebook-0.50-to-1.00", "data_files": [{"split": "train", "path": ["data/code/jupyter-notebook/0.95-1.00.jsonl", "data/code/jupyter-notebook/0.90-0.95.jsonl", "data/code/jupyter-notebook/0.85-0.90.jsonl", "data/code/jupyter-notebook/0.80-0.85.jsonl", "data/code/jupyter-notebook/0.75-0.80.jsonl", "data/code/jupyter-notebook/0.70-0.75.jsonl", "data/code/jupyter-notebook/0.65-0.70.jsonl", "data/code/jupyter-notebook/0.60-0.65.jsonl", "data/code/jupyter-notebook/0.55-0.60.jsonl", "data/code/jupyter-notebook/0.50-0.55.jsonl"]}]}, {"config_name": "code-jupyter-notebook-0.60-to-1.00", "data_files": [{"split": "train", "path": ["data/code/jupyter-notebook/0.95-1.00.jsonl", "data/code/jupyter-notebook/0.90-0.95.jsonl", "data/code/jupyter-notebook/0.85-0.90.jsonl", "data/code/jupyter-notebook/0.80-0.85.jsonl", "data/code/jupyter-notebook/0.75-0.80.jsonl", "data/code/jupyter-notebook/0.70-0.75.jsonl", "data/code/jupyter-notebook/0.65-0.70.jsonl", "data/code/jupyter-notebook/0.60-0.65.jsonl"]}]}, {"config_name": "code-jupyter-notebook-0.70-to-1.00", "data_files": [{"split": "train", "path": ["data/code/jupyter-notebook/0.95-1.00.jsonl", "data/code/jupyter-notebook/0.90-0.95.jsonl", "data/code/jupyter-notebook/0.85-0.90.jsonl", "data/code/jupyter-notebook/0.80-0.85.jsonl", "data/code/jupyter-notebook/0.75-0.80.jsonl", "data/code/jupyter-notebook/0.70-0.75.jsonl"]}]}, {"config_name": "code-jupyter-notebook-0.80-to-1.00", "data_files": [{"split": "train", "path": ["data/code/jupyter-notebook/0.95-1.00.jsonl", "data/code/jupyter-notebook/0.90-0.95.jsonl", "data/code/jupyter-notebook/0.85-0.90.jsonl", "data/code/jupyter-notebook/0.80-0.85.jsonl"]}]}, {"config_name": "code-full", "data_files": [{"split": "train", "path": ["data/code/*/*.jsonl"]}]}], "tags": ["mathematical-reasoning", "reasoning", "finetuning", "pretraining", "llm"]}

False

2024-10-30T21:19:01.000Z

e894d840ac3c86d4d0645d2a0e43895c0d1e73b6

AutoMathText AutoMathText is an extensive and carefully curated dataset encompassing around 200 GB of mathematical texts. It's a compilation sourced from a diverse range of platforms including various websites, arXiv, and GitHub (OpenWebMath, RedPajama, Algebraic Stack). This rich repository has been autonomously selected (labeled) by the state-of-the-art open-source language model, Qwen-72B. Each piece of content in the dataset is assigned a score lm_q1q2_score within the range… See the full description on the dataset page: https://huggingface.co/datasets/math-ai/AutoMathText.

2024-01-24T01:39:26.000Z

662005a74360f44332b11379

mlabonne/orpo-dpo-mix-40k

mlabonne

{"language": ["en"], "license": "apache-2.0", "task_categories": ["text-generation"], "pretty_name": "ORPO-DPO-mix-40k", "dataset_info": {"features": [{"name": "source", "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": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 238639013, "num_examples": 44245}], "download_size": 126503374, "dataset_size": 238639013}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["dpo", "rlhf", "preference", "orpo"]}

False

2024-10-17T21:44:52.000Z

0f72511202b8f093e9be60e1683d84b046062e36

ORPO-DPO-mix-40k v1.2 This dataset is designed for ORPO or DPO training. See Fine-tune Llama 3 with ORPO for more information about how to use it. It is a combination of the following high-quality DPO datasets: argilla/Capybara-Preferences: highly scored chosen answers >=5 (7,424 samples) argilla/distilabel-intel-orca-dpo-pairs: highly scored chosen answers >=9, not in GSM8K (2,299 samples) argilla/ultrafeedback-binarized-preferences-cleaned: highly scored chosen answers >=5… See the full description on the dataset page: https://huggingface.co/datasets/mlabonne/orpo-dpo-mix-40k.

2024-04-17T17:23:51.000Z

66558cea3e96e1c5975420f6

OpenGVLab/ShareGPT-4o

OpenGVLab

{"license": "mit", "extra_gated_prompt": "You agree to not use the dataset to conduct experiments that cause harm to human subjects. Please note that the data in this dataset may be subject to other agreements. Before using the data, be sure to read the relevant agreements carefully to ensure compliant use. Video copyrights belong to the original video creators or platforms and are for academic research use only.", "task_categories": ["visual-question-answering", "question-answering"], "extra_gated_fields": {"Name": "text", "Company/Organization": "text", "Country": "text", "E-Mail": "text"}, "language": ["en"], "size_categories": ["100K<n<1M"], "configs": [{"config_name": "image_caption", "data_files": [{"split": "images", "path": "image_conversations/gpt-4o.jsonl"}]}, {"config_name": "video_caption", "data_files": [{"split": "ptest", "path": "video_conversations/gpt4o.jsonl"}]}]}

auto

2024-08-17T07:51:28.000Z

a69d5b4d2c5343146e27b46a22638d346f14f013

2024-05-28T07:51:06.000Z

6668402237ffdd8f1bb47e6b

Multilingual-Multimodal-NLP/McEval-Instruct

Multilingual-Multimodal-NLP

{"license": "cc-by-sa-4.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["croissant"]}

False

2024-06-12T03:20:57.000Z

57bd46727e49ac3e2890218a146d35c2454a8496

McEval-Instruct data as described in the McEval Paper. Code for the evaluation and sft can be found on Github as McEval.

2024-06-11T12:16:34.000Z

6681ba8ef854e0b8718abd86

madebyollin/megalith-10m

madebyollin

{"license": "mit"}

False

2024-10-20T21:51:57.000Z

bb358738b6a5260a9214dd0c2891ab5451df36b4

🗿 Megalith-10m What is Megalith-10m? Megalith-10m is a dataset of ~10 million links to Flickr images that were categorized as "photo" with license info of: No known copyright restrictions (Flickr commons), or United States Government Work, or Public Domain Dedication (CC0), or Public Domain Mark What's the intended use of Megalith-10m? Megalith-10m is intended to contain only links to wholesome unedited uncopyrighted photographs - the sort of… See the full description on the dataset page: https://huggingface.co/datasets/madebyollin/megalith-10m.

2024-06-30T20:05:34.000Z

6697abec43d9faa413ca745c

HuggingFaceM4/Docmatix

HuggingFaceM4

{"language": ["en"], "license": "mit", "size_categories": ["1M<n<10M"], "task_categories": ["visual-question-answering"], "pretty_name": "Docmatix", "tags": ["docvqa"], "configs": [{"config_name": "images", "data_files": [{"split": "train", "path": "data/train-*"}]}, {"config_name": "pdf", "data_files": [{"split": "train", "path": "pdf/train-*"}]}, {"config_name": "zero-shot-exp", "data_files": [{"split": "train", "path": "zero-shot-exp/train-*"}, {"split": "test", "path": "zero-shot-exp/test-*"}]}], "dataset_info": [{"config_name": "images", "features": [{"name": "images", "sequence": "image"}, {"name": "texts", "list": [{"name": "user", "dtype": "string"}, {"name": "assistant", "dtype": "string"}, {"name": "source", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 552957537722.77, "num_examples": 1273215}], "download_size": 159404414330, "dataset_size": 552957537722.77}, {"config_name": "pdf", "features": [{"name": "pdf", "dtype": "binary"}, {"name": "texts", "list": [{"name": "user", "dtype": "string"}, {"name": "assistant", "dtype": "string"}, {"name": "source", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 458612867150, "num_examples": 1273245}], "download_size": 431829972210, "dataset_size": 458612867150}, {"config_name": "zero-shot-exp", "features": [{"name": "images", "sequence": "image"}, {"name": "texts", "list": [{"name": "user", "dtype": "string"}, {"name": "assistant", "dtype": "string"}, {"name": "source", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 68900253, "num_examples": 200}, {"name": "train", "num_bytes": 578335690.5, "num_examples": 1700}], "download_size": 642963847, "dataset_size": 647235943.5}]}

False

2024-08-26T08:15:21.000Z

0725b65616e0e5f6024be10e38ddf8d8c48664fd

Dataset Card for Docmatix Dataset description Docmatix is part of the Idefics3 release (stay tuned). It is a massive dataset for Document Visual Question Answering that was used for the fine-tuning of the vision-language model Idefics3. Load the dataset To load the dataset, install the library datasets with pip install datasets. Then, from datasets import load_dataset ds = load_dataset("HuggingFaceM4/Docmatix") If you want the dataset to link to… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceM4/Docmatix.

2024-07-17T11:33:00.000Z

66a0eaa871e33c33323f0507

argilla/magpie-ultra-v0.1

argilla

{"language": ["en"], "license": "llama3.1", "size_categories": "n<1K", "task_categories": ["text-generation"], "pretty_name": "Magpie Ultra v0.1", "dataset_info": {"features": [{"name": "model_name_response_base", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "response_base", "dtype": "string"}, {"name": "intent", "dtype": "string"}, {"name": "knowledge", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "model_name_difficulty", "dtype": "string"}, {"name": "explanation", "dtype": "string"}, {"name": "quality", "dtype": "string"}, {"name": "model_name_quality", "dtype": "string"}, {"name": "primary_tag", "dtype": "string"}, {"name": "other_tags", "sequence": "string"}, {"name": "model_name_classification", "dtype": "string"}, {"name": "embedding", "sequence": "float64"}, {"name": "model_name_embeddings", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "score_base", "dtype": "float64"}, {"name": "distilabel_metadata", "struct": [{"name": "raw_output_assign_tags_0", "dtype": "string"}]}, {"name": "nn_indices", "sequence": "int64"}, {"name": "nn_scores", "sequence": "float64"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "guard", "dtype": "string"}, {"name": "model_name_guard", "dtype": "string"}, {"name": "safe", "dtype": "bool"}, {"name": "hazard_category", "dtype": "string"}, {"name": "score_difference", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 837917458, "num_examples": 50000}], "download_size": 527647487, "dataset_size": 837917458}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["synthetic", "distilabel", "rlaif"]}

False

2024-10-09T08:23:50.000Z

cea3b9e0a4a7707656998beeffbe3f3570852816

Dataset Card for magpie-ultra-v0.1 This dataset has been created with distilabel. 📰 News [08/02/2024] Release of the first unfiltered version of the dataset containing 50K instruction-response pairs that can be used for SFT or DPO. Dataset Summary magpie-ultra it's a synthetically generated dataset for supervised fine-tuning using the new Llama 3.1 405B-Instruct model, together with other Llama models like Llama-Guard-3-8B… See the full description on the dataset page: https://huggingface.co/datasets/argilla/magpie-ultra-v0.1.

2024-07-24T11:51:04.000Z

66abc8f220040892547e2e04

yuxiang630/hqcode

yuxiang630

{"dataset_info": {"features": [{"name": "language", "dtype": "string"}, {"name": "topic", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1352157430, "num_examples": 220976}], "download_size": 516479291, "dataset_size": 1352157430}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "apache-2.0"}

False

2024-08-01T17:57:02.000Z

5277c8cb5cfe5110de64f30852b702d72dc603ad

Generated with gpt-4o-mini. Please check OpenAI's ToS (https://openai.com/policies/row-terms-of-use/) before using.

2024-08-01T17:42:10.000Z

66c582fe30010c0f2bba4176

Team-ACE/ToolACE

Team-ACE

{"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en", "zh"], "tags": ["synthetic", "tools"], "size_categories": ["10K<n<100K"]}

False

2024-09-04T02:37:59.000Z

6bda777c88d21e5a204703c1ee45597a8fa4f734

ToolACE ToolACE is an automatic agentic pipeline designed to generate Accurate, Complex, and divErse tool-learning data. ToolACE leverages a novel self-evolution synthesis process to curate a comprehensive API pool of 26,507 diverse APIs. Dialogs are further generated through the interplay among multiple agents, guided by a formalized thinking process. To ensure data accuracy, we implement a dual-layer verification system combining rule-based and model-based checks. More… See the full description on the dataset page: https://huggingface.co/datasets/Team-ACE/ToolACE.

2024-08-21T06:02:38.000Z

66c84764a47b2d6c582bbb02

amphion/Emilia-Dataset

amphion

{"license": "cc-by-nc-4.0", "task_categories": ["text-to-speech", "automatic-speech-recognition"], "language": ["zh", "en", "ja", "fr", "de", "ko"], "pretty_name": "Emilia", "size_categories": ["10M<n<100M"], "extra_gated_prompt": "Terms of Access: The researcher has requested permission to use the Emilia dataset and the Emilia-Pipe preprocessing pipeline. In exchange for such permission, the researcher hereby agrees to the following terms and conditions:\n1. The researcher shall use the dataset ONLY for non-commercial research and educational purposes.\n2. The authors make no representations or warranties regarding the dataset, \n including but not limited to warranties of non-infringement or fitness for a particular purpose.\n\n3. The researcher accepts full responsibility for their use of the dataset and shall defend and indemnify the authors of Emilia, \n including their employees, trustees, officers, and agents, against any and all claims arising from the researcher's use of the dataset, \n including but not limited to the researcher's use of any copies of copyrighted content that they may create from the dataset.\n\n4. The researcher may provide research associates and colleagues with access to the dataset,\n provided that they first agree to be bound by these terms and conditions.\n \n5. The authors reserve the right to terminate the researcher's access to the dataset at any time.\n6. If the researcher is employed by a for-profit, commercial entity, the researcher's employer shall also be bound by these terms and conditions, and the researcher hereby represents that they are fully authorized to enter into this agreement on behalf of such employer.", "extra_gated_fields": {"Name": "text", "Email": "text", "Affiliation": "text", "Position": "text", "Your Supervisor/manager/director": "text", "I agree to the Terms of Access": "checkbox"}}

auto

2024-09-06T13:29:55.000Z

bcaad00d13e7c101485990a46e88f5884ffed3fc

Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation This is the official repository 👑 for the Emilia dataset and the source code for the Emilia-Pipe speech data preprocessing pipeline. News 🔥 2024/08/28: Welcome to join Amphion's Discord channel to stay connected and engage with our community! 2024/08/27: The Emilia dataset is now publicly available! Discover the most extensive and diverse speech generation… See the full description on the dataset page: https://huggingface.co/datasets/amphion/Emilia-Dataset.

2024-08-23T08:25:08.000Z

66d1976f5072573d2a1f95f7

neo4j/text2cypher-2024v1

neo4j

{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "schema", "dtype": "string"}, {"name": "cypher", "dtype": "string"}, {"name": "data_source", "dtype": "string"}, {"name": "instance_id", "dtype": "string"}, {"name": "database_reference_alias", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 88717369, "num_examples": 39554}, {"name": "test", "num_bytes": 11304360, "num_examples": 4833}], "download_size": 8169979, "dataset_size": 100021729}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "license": "apache-2.0", "task_categories": ["text2text-generation"], "language": ["en"], "tags": ["neo4j", "cypher", "text2cypher"], "pretty_name": "Neo4j-Text2Cypher Dataset (2024)", "size_categories": ["10K<n<100K"]}

False

2024-11-07T21:51:53.000Z

7b796e303e745fcc86f76e372fb02345472e2df6

Neo4j-Text2Cypher (2024) Dataset The Neo4j-Text2Cypher (2024) Dataset brings together instances from publicly available datasets, cleaning and organizing them for smoother use. Each entry includes a “question, schema, cypher” triplet at minimum, with a total of 44,387 instances — 39,554 for training and 4,833 for testing. An overview of the dataset is shared at Link Fields Fields and their descriptions are as follows: Field Description “question”… See the full description on the dataset page: https://huggingface.co/datasets/neo4j/text2cypher-2024v1.

2024-08-30T09:57:03.000Z

66ebb7af703a567feca77e83

BAAI/CCI3-HQ

BAAI

{"task_categories": ["text-generation"], "language": ["zh"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "score", "dtype": "float"}], "splits": [{"name": "train"}]}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/part_*"}]}], "extra_gated_prompt": "You agree to not use the dataset to conduct experiments that cause harm to human subjects.", "extra_gated_fields": {"Company/Organization": "text", "Country": "country"}}

auto

2024-11-11T12:27:29.000Z

d6f3aa30cebfef497e822ff968ed68a18bf90b8f

Data Description To address the scarcity of high-quality safety datasets in the Chinese, we open-sourced the CCI (Chinese Corpora Internet) dataset on November 29, 2023. Building on this foundation, we continue to expand the data source, adopt stricter data cleaning methods, and complete the construction of the CCI 3.0 dataset. This dataset is composed of high-quality, reliable Internet data from trusted sources. And then with more stricter filtering, The CCI 3.0 HQ corpus… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/CCI3-HQ.

2024-09-19T05:33:35.000Z

66fffa6a09e6898775c9b854

LuKoi/ComfyUI-Custom-Scripts_autocomplete_csv

LuKoi

False

2024-11-14T02:26:48.000Z

3d420eb479aa59394420c70b8a4289049d514ab3

2024-10-04T14:23:38.000Z

6707c8a87a737319934442a6

openlanguagedata/flores_plus

openlanguagedata

{"annotations_creators": ["found"], "language_creators": ["expert-generated"], "language": ["ace", "acm", "acq", "aeb", "af", "ajp", "ak", "als", "am", "apc", "ar", "ars", "ary", "arz", "as", "ast", "awa", "ayr", "azb", "azj", "ba", "bm", "ban", "be", "bem", "bn", "bho", "bjn", "bo", "bs", "bug", "bg", "ca", "ceb", "cs", "cjk", "ckb", "crh", "cy", "da", "de", "dik", "dyu", "dz", "el", "en", "eo", "et", "eu", "ee", "fo", "fj", "fi", "fon", "fr", "fur", "fuv", "gaz", "gd", "ga", "gl", "gn", "gu", "ht", "ha", "he", "hi", "hne", "hr", "hu", "hy", "ig", "ilo", "id", "is", "it", "jv", "ja", "kab", "kac", "kam", "kn", "ks", "ka", "kk", "kbp", "kea", "khk", "km", "ki", "rw", "ky", "kmb", "kmr", "knc", "kg", "ko", "lo", "lij", "li", "ln", "lt", "lmo", "ltg", "lb", "lua", "lg", "luo", "lus", "lvs", "mag", "mai", "ml", "mar", "min", "mk", "mt", "mni", "mos", "mi", "my", "nl", "nn", "nb", "npi", "nso", "nus", "ny", "oc", "ory", "pag", "pa", "pap", "pbt", "pes", "plt", "pl", "pt", "prs", "quy", "ro", "rn", "ru", "sg", "sa", "sat", "scn", "shn", "si", "sk", "sl", "sm", "sn", "sd", "so", "st", "es", "sc", "sr", "ss", "su", "sv", "swh", "szl", "ta", "taq", "tt", "te", "tg", "tl", "th", "ti", "tpi", "tn", "ts", "tk", "tum", "tr", "tw", "tzm", "ug", "uk", "umb", "ur", "uzn", "vec", "vi", "war", "wo", "xh", "ydd", "yo", "yue", "zh", "zsm", "zu"], "license": ["cc-by-sa-4.0"], "multilinguality": ["multilingual", "translation"], "size_categories": ["unknown"], "source_datasets": ["extended|flores"], "task_categories": ["text2text-generation", "translation"], "task_ids": [], "pretty_name": "flores+", "language_details": "ace_Arab, ace_Latn, acm_Arab, acq_Arab, aeb_Arab, afr_Latn, ajp_Arab, aka_Latn, amh_Ethi, apc_Arab, arb_Arab, ars_Arab, ary_Arab, arz_Arab, asm_Beng, ast_Latn, awa_Deva, ayr_Latn, azb_Arab, azj_Latn, bak_Cyrl, bam_Latn, ban_Latn, bel_Cyrl, bem_Latn, ben_Beng, bho_Deva, bjn_Arab, bjn_Latn, bod_Tibt, bos_Latn, bug_Latn, bul_Cyrl, cat_Latn, ceb_Latn, ces_Latn, cjk_Latn, ckb_Arab, crh_Latn, cym_Latn, dan_Latn, deu_Latn, dik_Latn, dyu_Latn, dzo_Tibt, ell_Grek, eng_Latn, epo_Latn, est_Latn, eus_Latn, ewe_Latn, fao_Latn, pes_Arab, fij_Latn, fin_Latn, fon_Latn, fra_Latn, fur_Latn, fuv_Latn, gla_Latn, gle_Latn, glg_Latn, grn_Latn, guj_Gujr, hat_Latn, hau_Latn, heb_Hebr, hin_Deva, hne_Deva, hrv_Latn, hun_Latn, hye_Armn, ibo_Latn, ilo_Latn, ind_Latn, isl_Latn, ita_Latn, jav_Latn, jpn_Jpan, kab_Latn, kac_Latn, kam_Latn, kan_Knda, kas_Arab, kas_Deva, kat_Geor, knc_Arab, knc_Latn, kaz_Cyrl, kbp_Latn, kea_Latn, khm_Khmr, kik_Latn, kin_Latn, kir_Cyrl, kmb_Latn, kon_Latn, kor_Hang, kmr_Latn, lao_Laoo, lvs_Latn, lij_Latn, lim_Latn, lin_Latn, lit_Latn, lmo_Latn, ltg_Latn, ltz_Latn, lua_Latn, lug_Latn, luo_Latn, lus_Latn, mag_Deva, mai_Deva, mal_Mlym, mar_Deva, min_Latn, mkd_Cyrl, plt_Latn, mlt_Latn, mni_Beng, khk_Cyrl, mos_Latn, mri_Latn, zsm_Latn, mya_Mymr, nld_Latn, nno_Latn, nob_Latn, npi_Deva, nso_Latn, nus_Latn, nya_Latn, oci_Latn, gaz_Latn, ory_Orya, pag_Latn, pan_Guru, pap_Latn, pol_Latn, por_Latn, prs_Arab, pbt_Arab, quy_Latn, ron_Latn, run_Latn, rus_Cyrl, sag_Latn, san_Deva, sat_Beng, scn_Latn, shn_Mymr, sin_Sinh, slk_Latn, slv_Latn, smo_Latn, sna_Latn, snd_Arab, som_Latn, sot_Latn, spa_Latn, als_Latn, srd_Latn, srp_Cyrl, ssw_Latn, sun_Latn, swe_Latn, swh_Latn, szl_Latn, tam_Taml, tat_Cyrl, tel_Telu, tgk_Cyrl, tgl_Latn, tha_Thai, tir_Ethi, taq_Latn, taq_Tfng, tpi_Latn, tsn_Latn, tso_Latn, tuk_Latn, tum_Latn, tur_Latn, twi_Latn, tzm_Tfng, uig_Arab, ukr_Cyrl, umb_Latn, urd_Arab, uzn_Latn, vec_Latn, vie_Latn, war_Latn, wol_Latn, xho_Latn, ydd_Hebr, yor_Latn, yue_Hant, zho_Hans, zho_Hant, zul_Latn", "tags": ["text"], "configs": [{"config_name": "default", "data_files": [{"split": "dev", "path": "dev/*.parquet"}, {"split": "devtest", "path": "devtest/*.parquet"}]}], "extra_gated_heading": "Protecting the integrity of FLORES+ for evaluation", "extra_gated_fields": {"I agree not to re-host FLORES+ in places where it could be picked up by web crawlers": "checkbox", "If I evaluate using FLORES+, I will ensure that its contents are not in the training data": "checkbox"}}

auto

2024-11-15T17:18:29.000Z

00748651fbd725985ead0c8c21046b41737c99bd

Dataset Card for FLORES+ FLORES+ is an evaluation benchmark dataset for multilingual machine translation. Dataset Details Dataset Description FLORES+ is a multilingual machine translation benchmark released under CC BY-SA 4.0. This dataset was originally released by FAIR researchers at Meta under the name FLORES. Further information about these initial releases can be found in Dataset Sources below. The data is now being managed by OLDI, the Open… See the full description on the dataset page: https://huggingface.co/datasets/openlanguagedata/flores_plus.

2024-10-10T12:29:28.000Z

672ae2f3fc20b2b214ec9e5c

Tongyi-ConvAI/MMEvol

Tongyi-ConvAI

{"license": "apache-2.0", "task_categories": ["visual-question-answering", "question-answering"], "language": ["en", "zh"], "size_categories": ["100K<n<1M"]}

False

2024-11-16T15:57:48.000Z

c364e01337a225eefe4faf8f654c390d89e5d156

Dataset Card for MMEvol-480K This is the official data collection of the paper "MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct" Please see paper & website for more information: arXiv: https://arxiv.org/pdf/2409.05840 website: https://mmevol.github.io/home_page.html Overview The Tongyi-ConvAI generates this dataset for multi-modal supervised fine-tuning. This dataset was used to train our Evol-Llama3-8B-Instruct and Evol-Qwen2-7B reported… See the full description on the dataset page: https://huggingface.co/datasets/Tongyi-ConvAI/MMEvol.

2024-11-06T03:30:59.000Z

672d0546fa66c52dc45aa952

AmanPriyanshu/Dynamic-Topic-RedPajama-Data-1T-100k-SubSample-max-1k-tokens

AmanPriyanshu

{"task_categories": ["summarization", "text-generation", "text2text-generation"], "language": ["en"], "tags": ["topic-modeling", "code", "github", "c4", "common-crawl", "wikipedia", "book3", "gutenburg", "arxiv"], "pretty_name": "Dynamic Topic Modeling Dataset: RedPajama-1T SubSample (100k samples, 1k tokens)", "size_categories": ["100K<n<1M"], "license": "mit"}

False

2024-11-11T16:46:03.000Z

bbb7c0602af4d5fba16eeafe047a0f15f6a06fb9

Dynamic Topic Modeling Dataset: RedPajama-1T SubSample (100k samples, 1k tokens) 📝Check out the Blog Post This dataset represents a curated subset of the RedPajama-1T Sample dataset, specifically processed for dynamic topic modeling applications. It contains 100,000 samples from the original dataset, with each document limited to the first 1,024 tokens for consistent processing. Dataset Overview Name:… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/Dynamic-Topic-RedPajama-Data-1T-100k-SubSample-max-1k-tokens.

2024-11-07T18:21:58.000Z

672f0e56f84c8aac97b9ae0e

prithivMLmods/Spam-Text-Detect-Analysis

prithivMLmods

{"license": "creativeml-openrail-m", "language": ["en"], "tags": ["spam", "text", "analysis", "detect"], "size_categories": ["1K<n<10K"]}

False

2024-11-09T07:26:30.000Z

38b9e9c8f3109f451b15a00001f1d2db43c6c726

2024-11-09T07:25:10.000Z