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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
66212f29fb07c3e05ad0432e | HuggingFaceFW/fineweb | HuggingFaceFW | {"license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "FineWeb", "size_categories": ["n>1T"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/*/*"}]}, {"config_name": "sample-10BT", "data_files": [{"split": "train", "path": "sample/10BT/*... | false | False | 2025-07-11T20:16:53 | 3,225 | 67 | false | 9bb295ddab0e05d785b879661af7260fed5140fc |
🍷 FineWeb
15 trillion tokens of the finest data the 🌐 web has to offer
What is it?
The 🍷 FineWeb dataset consists of more than 18.5T tokens (originally 15T tokens) of cleaned and deduplicated english web data from CommonCrawl. The data processing pipeline is optimized for LLM ... | 402,264 | 9,558,368 | 54,812,538,723,397 | [
"task_categories:text-generation",
"language:en",
"license:odc-by",
"size_categories:10B<n<100B",
"modality:tabular",
"modality:text",
"arxiv:2306.01116",
"arxiv:2109.07445",
"arxiv:2406.17557",
"doi:10.57967/hf/2493",
"region:us"
] | 2024-04-18T14:33:13 | null | null |
6a669b60c7c5f26e04472453 | r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation | r0b0tlab | {"license": "other", "language": ["en", "zh", "es", "fr", "de", "ja"], "task_categories": ["text-generation", "conversational", "text2text-generation"], "tags": ["distillation", "sft", "reasoning", "tool-use", "multi-turn", "multi-teacher"], "size_categories": ["10K<n<100K"], "configs": [{"config_name": "sft_balanced",... | false | False | 2026-08-02T01:32:23 | 121 | 59 | false | 7a3473446840bcc397928cd8183d4b3ba3ca13a7 |
Multi-Teacher Distillation Dataset (57,937 traces)
A quality-filtered, deduplicated, multi-teacher SFT corpus combining traces from three frontier models across math, code, reasoning, instruction-following, tool-use, science, long-context, multilingual, and creative dialogue domains.
Teachers... | 4,060 | 4,060 | 827,297,284 | [
"task_categories:text-generation",
"language:en",
"language:zh",
"language:es",
"language:fr",
"language:de",
"language:ja",
"license:other",
"size_categories:10M<n<100M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"libr... | 2026-07-26T23:42:24 | null | null |
639244f571c51c43091df168 | Anthropic/hh-rlhf | Anthropic | {"license": "mit", "tags": ["human-feedback"]} | false | False | 2023-05-26T18:47:34 | 1,989 | 45 | false | 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 p... | 34,314 | 1,992,808 | 94,745,957 | [
"license:mit",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2204.05862",
"region:us",
"human-feedback"
] | 2022-12-08T20:11:33 | null | null |
6a7930b01702714af35a9dce | ostris/minimax_h3_1k | ostris | null | false | False | 2026-08-10T02:58:09 | 56 | 39 | false | f159a1a121dbefbf3d14d695fb4542e1cddb2271 |
MiniMax H3 - 1K
I generated a dataset to test the knowledge scope and capabilities of MiniMax H3.
Samples are of various aspect sizes, and cover a wide range of media types and themes.
The videos are 768 base resolution (~0.6 MP).
They were generated with minimax_h3_fl2va_pruned_int8_convrot.safetens... | 6,523 | 6,523 | 1,430,611,086 | [
"size_categories:1K<n<10K",
"format:text",
"modality:text",
"modality:video",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 2026-08-10T02:00:16 | null | null |
681af44a01120059cabf265d | ChartGalaxy/ChartGalaxy | ChartGalaxy | {"license": "cc-by-nc-4.0", "task_categories": ["visual-question-answering"], "language": ["en"], "tags": ["infographic", "chart"], "size_categories": ["1M<n<10M"], "configs": [{"config_name": "default", "data_files": [{"split": "preview", "path": "synthetic/preview.parquet"}]}]} | false | False | 2026-08-11T09:18:31 | 52 | 25 | false | e884919774a12edc4c06ba363a740173bf7e94e4 |
ChartGalaxy: A Dataset for Infographic Chart Understanding and Generation
🤗 Dataset | 🖥️ Code | 📄 Paper | 📄 Arxiv
🔥 News
[2026.02] 🎉🎉 A new batch of data has been added, comprising 108,208 infographic charts.
This update features broader diversity in title designs and more polished... | 109,487 | 170,870 | 182,039,203,507 | [
"task_categories:visual-question-answering",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2505.18668",
"doi:10.57967/hf/5638",
"r... | 2025-05-07T05:48:58 | null | null |
6a387563b57803e61682564f | MatrAIx2026/MatrAIx_Persona_1M | MatrAIx2026 | {"pretty_name": "MatrAIx Persona 1M Public Release", "task_categories": ["text-generation"], "tags": ["persona", "coreset", "synthetic", "survey", "parquet"], "size_categories": ["100K<n<1M"], "configs": [{"config_name": "sample", "data_files": [{"split": "train", "path": "sample/*.parquet"}]}]} | false | False | 2026-08-01T21:26:02 | 45 | 20 | false | 74f1edf9c9d024e6d3e412c3fda0efccfb2029c7 |
MatrAIx Persona 1M
999,847 personas, each described by 1,290 categorical attributes.
599,847 are derived from real records, 400,000 are synthetic.
10 Zstandard Parquet shards, 4.17 GB.
Read it with pyarrow, not datasets
Attributes are packed: one persona's 1,290 attributes are 645 bytes of... | 12,919 | 12,958 | 6,804,852,174 | [
"task_categories:text-generation",
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"persona",
"coreset",
"synthetic",
"survey",
"parquet"
] | 2026-06-21T23:36:03 | null | null |
6a615c95fb10b1093e0ea9ed | HuggingFaceCode/stack-v3-train | HuggingFaceCode | {"thumbnail": "https://huggingface.co/datasets/HuggingFaceCode/stack-v3-train/resolve/main/assets/banner.png", "annotations_creators": [], "language_creators": ["crowdsourced", "expert-generated"], "language": ["code"], "license": ["odc-by"], "multilinguality": ["multilingual"], "size_categories": ["100M<n<1B"], "sourc... | false | False | 2026-08-17T09:21:44 | 344 | 20 | false | df4b205fbba4cc1c2fd1f205b10d66f730798bb9 |
🥞 The Stack v3
What is it?
What is being released
How to download and use it
Dataset statistics
Dataset structure
Dataset creation
Considerations for using the data
Additional information
What is it?
The Stack v3 is the largest, most up-to-date open dataset of source code, crawled dir... | 250,906 | 250,907 | 3,544,637,688,742 | [
"task_categories:text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"language:code",
"license:odc-by",
"size_categories:100M<n<1B",
"arxiv:2402.19173",
"region:us",
"code"
] | 2026-07-23T00:13:09 | null | null |
6a74d6d5eaacdf5e0d9381fa | nvidia/Nemotron-RL-Agentic-Terminal-Pivot-v1 | nvidia | {"license": ["cc-by-4.0"], "language": ["en"], "task_categories": ["text-generation"], "pretty_name": "Nemotron-RL-Agentic-Terminal-Pivot-v1", "tags": ["text", "agentic", "code", "software engineering", "tool use", "reasoning", "reinforcement-learning", "synthetic", "human", "terminal"], "size_categories": ["10K<n<100K... | false | False | 2026-08-11T18:53:45 | 17 | 17 | false | df75a0134ab603d6926f5b6efb9eacd3603b2049 |
Dataset Description
The Nemotron-RL-Agentic-Terminal-Pivot-v1 dataset provides training samples for reinforcement learning of command-line ("terminal use") LLM agents with the terminus_judge environment in NeMo Gym.
Each record is a single agent decision point extracted from a successful agent trajectory... | 609 | 609 | 1,372,472,323 | [
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"text",
"agentic",
"code",
"software engineering",
"tool use"... | 2026-08-06T18:47:49 | null | null |
6974adda4fe45f6aa5dd9294 | ulamai/UnsolvedMath | ulamai | {"license": "cc-by-4.0", "task_categories": ["question-answering", "text-generation"], "language": ["en"], "tags": ["mathematics", "unsolved-problems", "math", "research", "latex"], "size_categories": ["1K<n<10K"], "pretty_name": "UnsolvedMath"} | false | False | 2026-08-17T07:54:02 | 45 | 14 | false | af14f5cf8bea73caa1f7d1ca3daf25515c1bd0f3 | 🌐 Browse UnsolvedMath online
✅ Paper: Open Mathematical Problems as an AI Reasoning Benchmark
UnsolvedMath Dataset
A comprehensive curated collection of 8,785 open mathematics problems across all domains and difficulty levels, including the largest collection of Erdős problems available in machine-reada... | 2,039 | 2,727 | 157,531,084 | [
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"region:us",
"mathematics",
"unsolved-problems",
"math",
"research",
"latex"
] | 2026-01-24T11:32:42 | null | null |
6a60a044d3559d7ff7b5590d | r0b0tlab/qwen3.8-max-distillation-50k | r0b0tlab | {"license": "other", "task_categories": ["text-generation", "question-answering"], "language": ["en"], "tags": ["distillation", "knowledge-distillation", "reasoning", "chain-of-thought", "supervised-fine-tuning", "math", "code", "instruction-following", "tool-use", "qwen"], "size_categories": ["10K<n<100K"], "pretty_na... | false | False | 2026-07-22T11:27:58 | 95 | 14 | false | ab9f8b289423c249fc0054507f045a12efb54b1b |
Qwen3.8-Max Distillation 50K
A curated dataset of 49,772 teacher-generated traces from qwen3.8-max-preview, prepared for supervised fine-tuning and off-policy knowledge distillation.
The teacher responses are preserved as returned by the API. Where the model emitted visible <think>...</think> blocks, tho... | 2,564 | 2,564 | 70,765,792 | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"license:other",
"size_categories:10K<n<100K",
"format:parquet",
"format:optimized-parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissa... | 2026-07-22T10:49:40 | null | null |
6a6b340d42990b2a6d50b7a8 | saidutta69/fable-5-premium | saidutta69 | {"license": "mit", "language": ["en"], "tags": ["fable-5", "claude", "agent-traces", "coding", "tool-use", "sft", "fine-tuning", "distillation"], "pretty_name": "Fable-5 Premium Dataset", "task_categories": ["text-generation", "token-classification"], "size_categories": ["10K<n<100K"]} | false | False | 2026-08-01T20:49:15 | 15 | 13 | false | 684cb1f849fe4a1c96f55351e1d7366f9888bb28 |
🧠 Fable-5 Premium Dataset
A rigorously cleaned, high-quality supervised fine-tuning (SFT) dataset built from Claude Fable-5 agent traces.
Priorities: Quality > Ease of Access > Quantity
📊 Dataset Overview
Property
Value
Total Records
12,730
Train Split
5,728 (45.0%... | 1,243 | 1,243 | 2,336,061,396 | [
"task_categories:text-generation",
"task_categories:token-classification",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"fable-5",
"claude",
"agent-... | 2026-07-30T11:22:53 | null | null |
6a3404497e03daf35bd3202e | scholarweave/arxiv-latex | scholarweave | {"license": "other", "license_name": "dual-license", "license_link": "LICENSE", "task_categories": ["text-generation", "feature-extraction"], "language": ["en"], "tags": ["science", "arxiv", "latex", "academic"], "pretty_name": "arXiv LaTeX Source Dataset", "size_categories": ["1M<n<10M"], "configs": [{"config_name": "... | false | False | 2026-08-10T14:55:05 | 132 | 12 | false | a64471103c2563f6428e61ba7ab28b33417a47a5 |
arXiv LaTeX Source Dataset
This dataset provides the entire corpus of arXiv's LaTeX source files, pre-parsed, formatted, and aligned with official metadata in ready-to-query Parquet files.
Why I Built This
If you have ever tried to work with the complete histor... | 16,679 | 50,775 | 289,210,617,452 | [
"task_categories:text-generation",
"task_categories:feature-extraction",
"language:en",
"license:other",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"science",
"arxiv",
"latex",
... | 2026-06-18T14:44:25 | null | null |
6a64903713862c0dcfc56ea5 | sarvamai/indic-diarbench | sarvamai | {"license": "cc-by-4.0", "task_categories": ["automatic-speech-recognition", "audio-to-audio"], "language": ["as", "bn", "brx", "doi", "gu", "hi", "kn", "ks", "kok", "mai", "ml", "mni", "mr", "ne", "or", "pa", "sa", "sat", "sd", "ta", "te", "ur"], "pretty_name": "Indic DiarBench", "size_categories": ["1K<n<10K"], "tags... | false | False | 2026-08-11T12:17:51 | 14 | 12 | false | 92877bad8aab6e598167d91c6ee02aa8ca6ede09 |
Indic DiarBench
A multilingual joint diarization and ASR benchmark for Indian languages, spanning all 22 scheduled languages of India with approximately 108 hours of natural multi-speaker audio.
Paper: Indic DiarBench: A Multilingual Joint Diarization and ASR Benchmark for Indian Languages (Interspeech 2... | 862 | 866 | 12,055,899,193 | [
"task_categories:automatic-speech-recognition",
"task_categories:audio-to-audio",
"language:as",
"language:bn",
"language:brx",
"language:doi",
"language:gu",
"language:hi",
"language:kn",
"language:ks",
"language:kok",
"language:mai",
"language:ml",
"language:mni",
"language:mr",
"lan... | 2026-07-25T10:30:15 | null | null |
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": ["text-generation"], "task_ids": [], "paperswithcode_id": "gsm8k", "pretty_na... | false | False | 2026-03-23T10:18:13 | 1,568 | 11 | false | 740312add88f781978c0658806c59bc2815b9866 |
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.
... | 1,025,385 | 14,576,854 | 5,900,352 | [
"benchmark:official",
"benchmark:eval-yaml",
"task_categories:text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"modal... | 2022-04-12T10:22:10 | gsm8k | null |
6791fcbb49c4df6d798ca7c9 | cais/hle | cais | {"license": "mit", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "string"}, {"name": "image_preview", "dtype": "image"}, {"name": "answer", "dtype": "string"}, {"name": "answer_type", "dtype": "string"}, {"name": "author_name", "dtyp... | false | auto | 2026-01-20T22:42:17 | 911 | 11 | false | 5a81a4c7271a2a2a312b9a690f0c2fde837e4c29 |
[!NOTE]
IMPORTANT: Please help us protect the integrity of this benchmark by not publicly sharing, re-uploading, or distributing the dataset.
Humanity's Last Exam
🌐 Website | 📄 Paper | GitHub
Center for AI Safety & Scale AI
Humanity's Last Exam (HLE) is a multi-modal benchmark at the frontier of ... | 36,205 | 417,694 | 274,282,300 | [
"benchmark:official",
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2025-01-23T08:24:27 | null | null |
6a4cc0ac90ce9cc602189d11 | FlyRank/internship-warehouse | FlyRank | {"license": "other", "language": ["en"], "tags": ["seo", "content-performance", "data-warehouse", "tabular", "education", "flyrank-internship"], "pretty_name": "FlyRank Internship \u2014 Warehouse Star Schema (Pseudonymized, Gated)", "size_categories": ["10M<n<100M"], "extra_gated_prompt": "By requesting access you agr... | false | auto | 2026-07-07T10:02:21 | 491 | 11 | false | 50cbf7c3909d07be4d1b5906b4d09e882e5acbf2 |
FlyRank Internship — Pseudonymized Warehouse Release (v20260703)
The open-ended, warehouse-shaped dataset (~81.8M rows; daily fact
78,835,655 rows) for advanced capstone work. Star schema with salted, namespaced,
fingerprinted hash keys. Built from warehouse v2 full history (frozen snapshot,
export date ... | 18,486 | 20,987 | 1,168,719,310 | [
"language:en",
"license:other",
"size_categories:10M<n<100M",
"modality:tabular",
"modality:text",
"region:us",
"seo",
"content-performance",
"data-warehouse",
"tabular",
"education",
"flyrank-internship"
] | 2026-07-07T09:02:36 | null | null |
6a75128c5cd4fce0bee1c1e6 | llamaindex/ExtractBench | llamaindex | {"license": "apache-2.0", "configs": [{"config_name": "extract-bench", "features": [{"name": "id", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "pdf", "dtype": "string"}, {"name": "data_schema", "dtype": "string"}, {"name": "expected_output", "dtype": "string"}, {"name": "field_rules", "dtype":... | false | False | 2026-08-13T16:03:56 | 14 | 11 | false | 49d80b17f9071939dce90ddac7033fab5c30b977 |
ExtractBench
Quick links: [🌐 Website] [📜 Paper] [💻 Code]
Given a document and a schema, a system returns structured data with evidence. The input is a full document, born-digital or scanned, and a schema written by the user. The output is a schema-valid JSON object, with the source page and a boundin... | 2,986 | 2,986 | 847,704,739 | [
"benchmark:official",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:json",
"modality:document",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2607.29677",
"region:us",
"document-extraction",... | 2026-08-06T23:02:36 | null | null |
65377f5989dd48faca8f7cf1 | HuggingFaceH4/ultrachat_200k | HuggingFaceH4 | {"language": ["en"], "license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"], "pretty_name": "UltraChat 200k", "configs": [{"config_name": "default", "data_files": [{"split": "train_sft", "path": "data/train_sft-*"}, {"split": "test_sft", "path": "data/test_sft-*"}, {"split": "train_g... | false | False | 2024-10-16T11:52:27 | 875 | 10 | false | 8049631c405ae6576f93f445c6b8166f76f5505a |
Dataset Card for UltraChat 200k
Dataset Description
This is a heavily filtered version of the UltraChat dataset and was used to train Zephyr-7B-β, a state of the art 7b chat model.
The original datasets consists of 1.4M dialogues generated by ChatGPT and spanning a wide range of topics. To... | 86,364 | 1,154,695 | 1,624,055,929 | [
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2305.14233",
"region:us"
] | 2023-10-24T08:24:57 | null | null |
6655eb19d17e141dcb546ed5 | HuggingFaceFW/fineweb-edu | HuggingFaceFW | {"license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "FineWeb-Edu", "size_categories": ["n>1T"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/*/*"}], "features": [{"name": "text", "dtype": "string"}, {"name": "id", "dtype": "string"},... | false | False | 2025-07-11T20:16:53 | 1,259 | 10 | false | 87f09149ef4734204d70ed1d046ddc9ca3f2b8f9 |
📚 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 ... | 405,260 | 8,416,886 | 5,835,742,481,176 | [
"task_categories:text-generation",
"language:en",
"license:odc-by",
"size_categories:1B<n<10B",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2406.17557",
"arxiv:2404.14219",
"arxiv:2401.10020",
... | 2024-05-28T14:32:57 | null | null |
6a2a47c4f5ff6c6dee016974 | armand0e/claude-fable-5-claude-code | armand0e | {"pretty_name": "claude-fable-5 Agent Traces", "task_categories": ["text-generation"], "tags": ["agent-traces", "format:agent-traces", "claude", "distillation", "claude-fable-5", "teich"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "*.jsonl"}]}]} | false | False | 2026-06-19T16:23:10 | 357 | 10 | false | c19fb6831700da833b22d1c9cdac47fe8603685c |
claude-fable-5 Agent Traces
It's worth noting that our team was working with Glint-Research to collect as much fable data as possible.
These are just the anonymized raw traces of both of our teams combined. This means that Glint-Research/Fable-5-traces was created from formatting and splitting up this sa... | 4,262 | 24,205 | 75,140,629 | [
"task_categories:text-generation",
"size_categories:n<1K",
"format:json",
"format:agent-traces",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"agent-traces",
"format:agent-traces",
"claude",
"distillation",... | 2026-06-11T05:29:40 | null | null |
6a7898e5505f415acd9182e2 | histde/ddb-newspaper-corpus | histde | {"pretty_name": "DDB Newspaper Corpus", "language": ["de"], "license": "other", "license_name": "public-domain", "license_link": "https://creativecommons.org/publicdomain/mark/1.0/", "task_categories": ["text-generation", "fill-mask"], "tags": ["newspapers", "historical", "ocr", "cultural-heritage", "public-domain", "d... | false | False | 2026-08-10T17:42:59 | 11 | 10 | false | 6e6b308e1787d3ae7d3463d8eba25119047cf5eb |
📰 DDB Newspaper Corpus
A corpus of 11,551,703 pages of historical German newspapers in the public domain, harvested from the Deutsche Digitale Bibliothek (DDB) and its Zeitungsportal.
It covers 1,607,744 issues from 796 newspapers published between 1638 and 1964, totalling 25.8 billion whitespace tokens... | 519 | 519 | 68,987,130,877 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"language:de",
"license:other",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2510.13996",
"region:us",
"newspapers",
"hi... | 2026-08-09T15:12:37 | null | null |
63990f21cc50af73d29ecfa3 | fka/prompts.chat | fka | {"license": "cc0-1.0", "tags": ["ChatGPT", "prompts", "AI", "GPT", "Claude", "Gemini", "Llama", "Mistral", "LLM", "prompt-engineering", "conversational-ai", "text-generation", "chatbot", "awesome-list"], "task_categories": ["question-answering", "text-generation"], "size_categories": ["100K<n<1M"]} | false | False | 2026-08-17T03:22:44 | 9,785 | 9 | false | daeb86ab99c0ad3fd3288c2967a303cff4febe0e |
a.k.a. Awesome ChatGPT Prompts
This is a Dataset Repository mirror of prompts.chat — a social platform for AI prompts.
📢 Notice
This Hugging Face dataset is a mirror. For the latest prompts, features, and community contributions, please visit:
🌐 Website: prompts.chat
📦 GitHub: github.com/f... | 31,518 | 670,160 | 5,576,315 | [
"task_categories:question-answering",
"task_categories:text-generation",
"license:cc0-1.0",
"size_categories:1K<n<10K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"ChatGPT",
"prompts",
"AI",
"GPT",
"Claude"... | 2022-12-13T23:47:45 | null | null |
69f638c8ebff1de2d6753093 | GokuScraper/seedance-2-prompts-datasets | GokuScraper | {"license": "cc-by-4.0", "dataset_info": {"features": [{"name": "version", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "is_featured", "dtype": "bool"}, {"name": "date", "dtype": "string"}, {"name": "slug", "dtype": "string"}, {"name": "model_info", "struct": ... | false | False | 2026-08-12T10:14:58 | 31 | 9 | false | 6ecc276c958ec0ae3a06d727e1ccd9bf432a27df |
🎞️ Seedance-2-prompts-datasets
🎞️ The ultimate Seedance-2 video prompt dataset (50GB+). 8100+ video generation prompts with full metadata and preview frames. Truly open source: No login, no ads, no redirection. Just pure data for AI video creators.
This project is a massive collection of prompts ... | 228,213 | 459,040 | 55,378,822,306 | [
"task_categories:text-to-video",
"language:en",
"language:zh",
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"modality:image",
"modality:video",
"region:us",
"video-prompt",
"seedance-2",
"prompt-engineering",
"prompt-dataset",
"video-generation"
] | 2026-05-02T17:47:52 | null | null |
6a802dee081f54ef245b59d2 | CaptiveDreamer/CaraArchive | CaptiveDreamer | null | false | False | 2026-08-16T10:55:37 | 9 | 9 | false | 92ac6a1254a9c1f27b418fad21b625b65774dbbb |
CaraArchive Index Dataset
1) This is an Index Dataset, not an Image dataset
This dataset contains 0 image data.
It only contains links to Cara App's CDN and metadata.
HuggingFace may load some images in the preview because it detects the CDN links, however those images do not exist as dat... | 268 | 268 | 12,719,271,077 | [
"size_categories:1M<n<10M",
"format:text",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 2026-08-15T09:14:22 | null | null |
6a7e5e96e1986fb7b588a175 | FINAL-Bench/AX-RAY | FINAL-Bench | {"pretty_name": "AX-Ray AI/AX Safety Diagnostics Dataset", "language": ["en", "ko"], "license": "cc-by-nc-4.0", "task_categories": ["text-generation", "question-answering"], "size_categories": ["n<1K"], "tags": ["ai-safety", "ax-safety", "ai-evaluation", "model-evaluation", "model-audit", "safety-diagnostics", "deploym... | false | False | 2026-08-14T02:09:38 | 37 | 8.5 | false | cc8cbe237ec816c750eae3bcb571ccbdf6b05de7 |
AX-RAY
AX-RAY is the versioned, machine-readable AI safety, AX safety, model evaluation, and deployment-readiness criteria catalog behind the FINAL-Bench AX-Ray Space. It organizes 117 AI/AX safety diagnostic criteria, including causal-leakage and causal-integrity review items, across model-intrinsic a... | 148 | 148 | 879,127 | [
"task_categories:text-generation",
"task_categories:question-answering",
"annotations_creators:expert-generated",
"source_datasets:original",
"language:en",
"language:ko",
"license:cc-by-nc-4.0",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"l... | 2026-08-14T00:17:26 | null | null |
681139b8ff0764f384f0b38e | SWE-bench/SWE-bench_Verified | SWE-bench | {"dataset_info": {"features": [{"name": "base_commit", "dtype": "string"}, {"name": "created_at", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "environment_setup_commit", "dtype": "string"}, {"name": "eval_type", "dtype": "string"}, {"name": "image", "dtype": "string"}, {"name": "instance_id"... | false | False | 2026-08-16T04:23:43 | 143 | 8 | false | 78f471bf655a3137b2e8a75af1501690ec009ec3 | Dataset Summary
SWE-bench Verified is a subset of 500 samples from the SWE-bench test set, which have been human-validated for quality. SWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. See this post for more details on the human-validation process.
The dataset collects 500 test I... | 89,372 | 1,198,741 | 6,311,085 | [
"benchmark:official",
"benchmark:eval-yaml",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2025-04-29T20:42:32 | null | null |
6a4430c54c3d2b66bc7e2f0a | finebooks/bhl-impact-gt | finebooks | {"pretty_name": "FineBooks BHL IMPACT Ground Truth", "license": "cc-by-3.0", "language": ["de", "en", "fr", "la"], "task_categories": ["image-to-text"], "size_categories": ["1K<n<10K"], "tags": ["OCR", "text-recognition", "layout-analysis", "ground-truth", "biodiversity", "BHL", "historical-documents", "GLAM"], "config... | false | False | 2026-08-10T13:48:03 | 9 | 8 | false | b7bda5fac0471d6d2237360abc799c6d13559465 |
FineBooks BHL IMPACT Ground Truth
2,165 page scans from six historical natural-history books, each paired with an expert, ~99.95%-accurate transcription and full page-layout ground truth. A benchmark for OCR, text recognition, and document layout analysis on real historical print.
This dataset is the bas... | 1,521 | 1,609 | 429,005,832 | [
"task_categories:image-to-text",
"language:de",
"language:en",
"language:fr",
"language:la",
"license:cc-by-3.0",
"size_categories:1K<n<10K",
"format:imagefolder",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us",
"OCR",
"text-recognition",
"lay... | 2026-06-30T21:10:29 | null | null |
6836f247cf9a0d4bc29a8cc5 | ai4bharat/MSMARCO-XI | ai4bharat | null | false | False | 2025-06-03T04:25:36 | 14 | 7 | false | bf5cdc1f26e581e519018e434db14edd1b77602b |
MS MARCO Translations Dataset
Dataset Description
This dataset contains the MS MARCO dataset translated into various Indic languages. The original MS MARCO dataset is a collection of queries, passages, and answers for machine reading comprehension and question answering tasks. Each example... | 6,720 | 9,393 | 55,619,613,700 | [
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2506.01615",
"region:us"
] | 2025-05-28T11:23:51 | null | null |
6a4e1fe2df56b09d5f449aa8 | Qyrou/reasoning-corpus-4K-5M-v1 | Qyrou | {"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["reasoning", "CoT", "code", "agentic", "thinking", "think", "deepseek-v4", "qwen3", "qwen3next"], "pretty_name": "Reasoning Corpus 5M", "size_categories": ["1M<n<10M"]} | false | False | 2026-07-31T02:06:14 | 204 | 7 | false | 32cda5b5cf69fae14a8620659d57aee360f1a048 | Reasoning Corpus 5M · Within 5k sequence length
About Dataset
This dataset contains reasoning chains from major AI models, such as: DeepSeek-v4 (both Pro and Flash), DeepSeek-r1 (DS-r1, Llama-DS, Qwen-DS), Qwen3, Qwen3.5/3.6 (both OpenSource and API models), Gemma4-31B derived from many other reposito... | 12,960 | 13,486 | 68,664,454,407 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"reasoning",
"CoT",
"code",
"agentic",
"thinking",
"think",
... | 2026-07-08T10:01:06 | null | null |
6a68e4ea0c35d2a3ed8c033e | ACERobotics/ACE-Data-0 | ACERobotics | {"pretty_name": "ACE-Data-0", "license": "other", "license_name": "ace-data-0-research-license", "license_link": "LICENSE", "viewer": false, "language": ["en"], "task_categories": ["robotics", "keypoint-detection", "video-classification", "audio-classification"], "size_categories": ["10K<n<100K"], "tags": ["video", "au... | false | False | 2026-08-17T13:16:20 | 21 | 7 | false | 102c96a49ab8d3aa85e34ce1e5df1cac6030baff |
ACE-Data-0
Human-Centric Ambient Capture as Embodied Data Engine
S-Lab, Nanyang Technological University, Singapore
·
ACE Robotics
ACE turns real home environments into spatially calibrated, temporally synchronized recording studios for embo... | 393 | 393 | 819,728,879,398 | [
"task_categories:robotics",
"task_categories:keypoint-detection",
"task_categories:video-classification",
"task_categories:audio-classification",
"language:en",
"license:other",
"size_categories:10K<n<100K",
"modality:video",
"modality:audio",
"modality:3d",
"modality:timeseries",
"arxiv:2607.... | 2026-07-28T17:20:42 | null | null |
6a6ae49c0ffe489af84d61ea | pymaster/CrawlSinger-OS | pymaster | {"pretty_name": "CrawlSinger-OS", "license": "mit", "language": ["zh"], "task_categories": ["text-to-speech", "text-to-audio", "automatic-speech-recognition"], "size_categories": ["100K<n<1M"], "tags": ["audio", "music", "singing", "singing-voice-synthesis", "music-score", "arxiv:2607.27768"]} | false | False | 2026-08-05T02:58:45 | 10 | 7 | false | bb7ee5de780736e44eca0a42d75e383de5875bc1 |
CrawlSinger-OS
CrawlSinger-OS is a large-scale, open-source singing corpus constructed for
score-native singing voice synthesis. It contains more than 2,300 hours of
processed singing data from multiple public song and singing collections, with
a unified annotation scheme for lyrics, MIDI pitches, symbol... | 284 | 284 | 110,502,876,040 | [
"task_categories:text-to-speech",
"task_categories:text-to-audio",
"task_categories:automatic-speech-recognition",
"language:zh",
"license:mit",
"size_categories:100K<n<1M",
"modality:audio",
"arxiv:2607.27768",
"region:us",
"audio",
"music",
"singing",
"singing-voice-synthesis",
"music-sc... | 2026-07-30T05:43:56 | null | null |
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"... | false | auto | 2024-07-27T09:28:42 | 969 | 6 | false | 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... | 7,034 | 343,760 | 1,488,861,436 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2309.11998",
"region:us"
] | 2023-09-20T06:33:44 | null | null |
67374c18c32c765810f748f6 | HuggingFaceH4/MATH-500 | HuggingFaceH4 | {"task_categories": ["text-generation"], "language": ["en"], "pretty_name": "MATH-500"} | false | False | 2025-12-15T11:01:40 | 327 | 6 | false | 6e4ed1a2a79af7d8630a6b768ec859cb5af4d3be |
Dataset Card for MATH-500
This dataset contains a subset of 500 problems from the MATH benchmark that OpenAI created in their Let's Verify Step by Step paper. See their GitHub repo for the source file: https://github.com/openai/prm800k/tree/main?tab=readme-ov-file#math-splits
| 184,582 | 1,886,320 | 450,344 | [
"task_categories:text-generation",
"language:en",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2024-11-15T13:26:48 | null | null |
67c92e867c6308c49ce2e98c | openbmb/Ultra-FineWeb | openbmb | {"language": ["en", "zh"], "license": "apache-2.0", "size_categories": ["n>1T"], "task_categories": ["text-generation"], "pretty_name": "Ultra-FineWeb", "tags": ["llm", "pretraining", "web-corpus", "data-filtering", "high-quality"], "configs": [{"config_name": "default", "data_files": [{"split": "en", "path": "data/ult... | false | False | 2026-08-15T04:46:08 | 417 | 6 | false | 578e59fd08c136f569db167d9bbf77ebc04f3559 |
Ultra-FineWeb
📜 Technical Report |
📦 UltraData Collection |
🌐 UltraData |
🤗 MiniCPM4 Series |
🤗 MiniCPM5 Series
English |
中文
📚 Introduction
Ultra-FineWeb is a large-scale, high-quality, and efficiently-filtered dataset. We use the proposed efficient verification-based hi... | 124,952 | 842,355 | 9,733,108,790,509 | [
"task_categories:text-generation",
"language:en",
"language:zh",
"license:apache-2.0",
"size_categories:1B<n<10B",
"modality:text",
"arxiv:2505.05427",
"arxiv:2602.09003",
"arxiv:2412.04315",
"region:us",
"llm",
"pretraining",
"web-corpus",
"data-filtering",
"high-quality"
] | 2025-03-06T05:11:34 | null | null |
69639f14243de820764453cc | nvidia/SPEED-Bench | nvidia | {"license": "other", "license_name": "nvidia-evaluation-dataset-license", "dataset_info": [{"config_name": "qualitative", "features": [{"name": "question_id", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "sub_category", "dtype": "string"}, {"name": "turns", "list": "string"}, {"name": "source",... | false | False | 2026-04-28T13:13:49 | 42 | 6 | false | 487aa718444e816458d1a0a52bfce7a454285cf4 |
📒 Blog |
📄 Paper |
🤗 Data |
⚙️ Measurement Framework
SPEED-Bench (SPEculative Evaluation Dataset) is a unified benchmark designed to evaluate speculative decoding (SD) across diverse semantic domains and realistic serving regimes, using production-grade inference engines.
It measures both acceptance... | 14,701 | 39,319 | 97,529,175 | [
"license:other",
"size_categories:1K<n<10K",
"format:parquet",
"format:optimized-parquet",
"modality:document",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2604.09557",
"arxiv:2401.07851",
"region:us"
] | 2026-01-11T13:01:08 | null | null |
69b1183046c6e7a964869ec4 | ropedia-ai/xperience-10m | ropedia-ai | {"pretty_name": "Xperience-10M", "language": ["en"], "task_categories": ["video-classification", "image-to-text", "depth-estimation", "robotics"], "tags": ["egocentric", "first-person", "multimodal", "3d", "4d", "embodied-ai", "robotics", "human-motion", "mocap", "imu", "audio", "depth", "captions", "video"], "size_cat... | false | manual | 2026-04-21T05:03:45 | 232 | 6 | false | ce943cf271a758b60240084892d05cf6dc12dd90 |
⚠️ Important: If you have already submitted an access request but have not completed the required DocuSign agreement, your request will remain pending. Please complete signing and we will grant access once verified.
Interactive Intelligence from Human Xperience
Xperience-10... | 99,668 | 2,759,786 | 31,877,067,197,610 | [
"task_categories:video-classification",
"task_categories:image-to-text",
"task_categories:depth-estimation",
"task_categories:robotics",
"language:en",
"license:other",
"size_categories:1M<n<10M",
"modality:3d",
"modality:audio",
"modality:video",
"region:us",
"egocentric",
"first-person",
... | 2026-03-11T07:22:24 | null | null |
6a15ea46bbc25dbed3a3b4b5 | nvidia/Nemotron-SFT-Math-v4 | nvidia | {"pretty_name": "Nemotron-SFT-Math-v4", "language": ["en"], "license": ["cc-by-4.0", "cc-by-sa-4.0"], "task_categories": ["text-generation"], "tags": ["math", "mathematical-reasoning", "text", "blend", "Nemotron_3_Ultra", "supervised-fine-tuning"], "size_categories": ["100K<n<1M"], "configs": [{"config_name": "default"... | false | False | 2026-08-12T11:25:33 | 33 | 6 | false | 84d42ad0cb960f07f951b9baa9ed2b46a5a18c66 |
Nemotron-SFT-Math-v4
Dataset Description:
Nemotron-SFT-Math-v4 is a large-scale mathematical reasoning dataset containing model-generated reasoning trajectories. Solutions in this version are generated using DeepSeek-V4-Pro on High inference mode.
The problems in this dataset are sourced f... | 3,831 | 7,601 | 5,537,906,877 | [
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"license:cc-by-sa-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2512.15489",
"region:us",
"math",
"mathematic... | 2026-05-26T18:45:26 | null | null |
6a2cd0828137fb18cecbcc06 | Glint-Research/Fable-5-traces | Glint-Research | {"license": "agpl-3.0", "pretty_name": "Fable 5 Pi Agent Traces", "annotations_creators": ["machine-generated"], "language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["text-generation"], "tags": ["agent-traces", "pi-agent", "claude-code", "fable-5", "chain-of-thought", "tool-use", "coding-agents", "s... | false | False | 2026-06-29T15:10:20 | 719 | 6 | false | e05c417852fc59fd8da758e68b352732423ca0cb |
Glint Research Dataset Card
Fable 5 Pi Agent Traces
A compact, high-signal corpus of Fable 5 coding-agent traces converted into Hugging Face Agent Traces / Pi-compatible sessions for Data Studio inspection, tool-use policy learning, and reasoning/action distillation.
... | 56,830 | 139,639 | 187,507,989 | [
"task_categories:text-generation",
"annotations_creators:machine-generated",
"language:en",
"license:agpl-3.0",
"size_categories:1K<n<10K",
"format:json",
"format:agent-traces",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
... | 2026-06-13T03:37:38 | null | null |
6a38d1636665fbc4440b1ce1 | armand0e/Fable-5-Chat | armand0e | {"task_categories": ["text-generation"], "language": ["en"], "pretty_name": "Fable 5 Chat Traces", "tags": ["conversational", "distillation", "teich", "anthropic/claude-fable-5"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "*.jsonl"}]}]} | false | False | 2026-06-22T06:10:57 | 14 | 6 | false | 38cf282edea1eac301321b04a1961d326c32293c |
TheFusionCube Fable-5 Chat Conversion
Source dataset: TheFusionCube/Fable-5-CoT-Traces
Output file: train.jsonl
Source rows: 468
Kept rows: 353
Dropped category == "decoy" rows: 115
Dropped blank prompt/response rows: 0
Each row has:
{
"prompt": "...",
"messages": [
{"role": "user", "content": ... | 426 | 544 | 1,765,879 | [
"task_categories:text-generation",
"language:en",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"conversational",
"distillation",
"teich",
"anthropic/claude-fable-5"
] | 2026-06-22T06:08:35 | null | null |
6a745e4d9f2214ec691ff687 | biglam/british-library-book-images | biglam | {"annotations_creators": ["machine-generated"], "language_creators": ["found"], "license": ["cc0-1.0"], "size_categories": ["1M<n<10M"], "source_datasets": ["blbooks"], "pretty_name": "British Library Book Images", "task_categories": ["image-classification", "image-to-text", "text-to-image"], "tags": ["image", "digital... | false | False | 2026-08-08T13:33:46 | 26 | 6 | false | c288990ce59b055e7bf9411f663d0f672ae16102 |
British Library Book Images
1,080,814 images cut out of 49,455 digitised books (65,227 volumes, ~25 million pages) published
between c. 1510 and c. 1900, digitised by the British Library in partnership
with Microsoft and released by British Library Labs
on Flickr Commons as the "1 Million Images from Sca... | 2,218 | 2,218 | 626,001,004,197 | [
"task_categories:image-classification",
"task_categories:image-to-text",
"task_categories:text-to-image",
"annotations_creators:machine-generated",
"language_creators:found",
"source_datasets:blbooks",
"license:cc0-1.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:t... | 2026-08-06T10:13:33 | null | null |
6a7609d6663f80dd30d388fc | RekaAI/RekaDaily-10k-raw | RekaAI | {"license": "apache-2.0", "task_categories": ["video-classification", "image-to-video"], "language": ["en"], "tags": ["video", "egocentric", "first-person", "household", "webdataset"], "size_categories": ["100K<n<1M"], "configs": [{"config_name": "browse", "default": true, "data_files": [{"split": "train", "path": "met... | false | False | 2026-08-09T03:08:06 | 8 | 6 | false | a42e2da9aaeef7c8653d9de50772c97ceb954251 |
RekaDaily-10k (raw)
Raw, unscripted, first-person daily-life video, collected through
Claru, Reka's data collection marketplace — recorded by
paid collectors in their own homes and workplaces on head-mounted and handheld
phones, across multiple regions.
Videos are exactly as collected — no re-encoding, n... | 162,036 | 162,036 | 70,184,198,170,183 | [
"task_categories:video-classification",
"task_categories:image-to-video",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:image",
"modality:tabular",
"modality:text",
"modality:video",
"library:datasets",
"library:pandas",
"library:polars",
"li... | 2026-08-07T16:37:42 | null | null |
666a59145c3bb7e4a6c8d180 | Salesforce/xlam-function-calling-60k | Salesforce | {"extra_gated_heading": "Acknowledge to follow corresponding license and cite APIGen to access the repository", "extra_gated_button_content": "Agree and access repository", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Country": "country", "Affiliation": "text"}, "license": "cc-by-4.0", "task_categ... | false | auto | 2025-01-24T19:25:58 | 676 | 5 | false | 26d14ebfe18b1f7b524bd39b404b50af5dc97866 |
APIGen Function-Calling Datasets
Paper | Website | Models
This repo contains 60,000 data collected by APIGen, an automated data generation pipeline designed to produce verifiable high-quality datasets for function-calling applications. Each data in our dataset is verified through three hierarchical stage... | 23,387 | 185,098 | 97,680,202 | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:reinforcement-learning",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
... | 2024-06-13T02:27:32 | null | null |
End of preview. Expand in Data Studio
Changelog
NEW Changes March 11th 2026
- Added new split:
arxiv_papers, sourced from the Hugging Face/api/papersendpoint paperscontinues to point todaily_papers.parquet, which is the Daily Papers feed
NEW Changes July 25th
- added
baseModelsfield to models which shows the models that the user tagged as base models for that model
Example:
{
"models": [
{
"_id": "687de260234339fed21e768a",
"id": "Qwen/Qwen3-235B-A22B-Instruct-2507"
}
],
"relation": "quantized"
}
NEW Changes July 9th
- Fixed issue with
ggufcolumn with integer overflow causing import pipeline to be broken over a few weeks ✅
NEW Changes Feb 27th
Added new fields on the
modelssplit:downloadsAllTime,safetensors,ggufAdded new field on the
datasetssplit:downloadsAllTimeAdded new split:
paperswhich is all of the Daily Papers
Updated Daily
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