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

{"license": "odc-by", "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": 18159796472, "num_examples": 5241900}], "download_size": 9949701917, "dataset_size": 18159796472}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

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