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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 | 3,162 | 60 | 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 ... | 398,446 | 9,469,561 | 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 | 67 | 43 | 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... | 1,486 | 1,486 | 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 | 1,950 | 35 | 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... | 33,956 | 1,984,499 | 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 |
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 | 871 | 26 | 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... | 73,327 | 1,129,498 | 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 |
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 | 36 | 26 | 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... | 4,869 | 4,906 | 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 |
6a7930b01702714af35a9dce | ostris/minimax_h3_1k | ostris | null | false | False | 2026-08-10T02:58:09 | 25 | 25 | 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... | 1,490 | 1,490 | 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 |
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 | 2026-08-11T15:11:51 | 333 | 23 | false | a34523fd426a429b7686a5e5786496bf2e1b1172 |
🥞 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... | 197,489 | 197,489 | 3,544,930,743,928 | [
"task_categories:text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"language:code",
"license:odc-by",
"size_categories:100M<n<1B",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
... | 2026-07-23T00:13:09 | 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 | 20 | 19 | 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... | 1,077 | 1,077 | 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 |
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 | 471 | 16 | 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 ... | 16,216 | 16,773 | 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 |
6a5c7151790a516fd701d8de | simple-world-lab/HiFi-UMI-2K | simple-world-lab | {"language": ["en"], "license": "cc-by-4.0", "pretty_name": "HiFi-UMI-2K", "task_categories": ["robotics"], "tags": ["robotics", "robot-learning", "robot-manipulation", "imitation-learning", "vision-language-action", "world-action-model", "multimodal", "video", "lerobot", "umi", "arxiv:2607.25895"], "configs": [{"confi... | false | False | 2026-07-29T02:17:44 | 44 | 13 | false | a53b7b5784afdd50b2fda9195c9f724ef75ffdaf |
HiFi-UMI-2K: High-Fidelity Robot-Free Manipulation Data
2,000 hours released · 6 synchronized camera views · 480+ scenes · 3 mm pose accuracy · <40 µs synchronization
🌐 Project Website |
📦 Dataset |
📄 Paper: arXiv:2607.25895
Examples from the HiFi-UMI corpus. Click the i... | 93,751 | 93,751 | 16,042,058,644,025 | [
"task_categories:robotics",
"language:en",
"license:cc-by-4.0",
"size_categories:100M<n<1B",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"library:lerobot",
"arxiv:2607.25895",
... | 2026-07-19T06:40:17 | 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 | 86 | 13 | 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... | 2,035 | 2,035 | 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 |
6a60c3ba35f4a8255c1e9907 | XYZAILab/XYZ-Aquila-SFT | XYZAILab | {"license": "apache-2.0", "language": ["en", "zh"], "task_categories": ["text-generation", "question-answering"], "tags": ["agent", "tool-use", "multi-turn", "supervised-fine-tuning", "web-search", "xyz-aquila"], "pretty_name": "XYZ-Aquila SFT", "size_categories": ["1K<n<10K"], "configs": [{"config_name": "en", "data_f... | false | False | 2026-07-29T08:28:13 | 369 | 13 | false | 0e7880cc14ed5c185e30e430c4ac1dc7c5faea71 |
XYZ-Aquila SFT
XYZ-Aquila SFT is a bilingual release of 7,000 multi-turn, search-oriented
tool-use trajectories, comprising 5,000 English examples and 2,000 Chinese
examples.
This release is a sample of the broader supervised fine-tuning data used for
XYZ-Aquila-mini and
XYZ-Aquila-pro. The examples
capt... | 1,646 | 1,646 | 2,772,823,471 | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"language:zh",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"agent",
"t... | 2026-07-22T13:20:58 | null | null |
6a70e487dfc35f3accc03dfb | Zhongzhi1228/Recursive-Task-Synthesis | Zhongzhi1228 | {"license": "cc-by-4.0", "task_categories": ["reinforcement-learning"], "language": ["en"], "tags": ["recursive-task-synthesis", "command-line", "synthetic-tasks"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "metadata/tasks.parquet"}]}]} | false | False | 2026-08-06T23:30:00 | 13 | 13 | false | be44f96808d5a9b599d5cb024341ff00091adeb7 |
Recursive Task Synthesis
This dataset contains 37,484 validated command-line task instances produced
through recursive task synthesis. Public identifiers are opaque and stable.
metadata/tasks.parquet: one searchable row per task instance.
metadata/shard_manifest.jsonl: TAR sizes and SHA256 checksums.
da... | 612 | 612 | 3,931,321,525 | [
"task_categories:reinforcement-learning",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"recursive-task-synthesis",
"comman... | 2026-08-03T18:57:11 | 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 | 1,254 | 12 | 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 ... | 386,509 | 8,330,137 | 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 |
69c92321b6d6a07dac997df1 | oddadmix/dialectal-arabic-lahgtna-v2 | oddadmix | {"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcript_text", "dtype": "string"}, {"name": "language", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 345646527284.8981, "num_examples": 608124}, {"name": "test", "num_bytes": 1736975250.8978827... | false | False | 2026-08-04T01:42:53 | 19 | 11 | false | 7b6a2cc93c31ccd9de612a8ab058fda2aeb45a58 |
Dialectal Arabic Lahgtna v2
Large-scale multi-dialect Arabic speech dataset — 3,000+ hours across 13 Arabic dialects — for training and evaluating dialectal Arabic ASR systems. Part of the Lahgtna (لهجتنا) project for dialect-aware Arabic speech AI.
Dataset Summary
~611K utterances / 3,00... | 1,716 | 5,860 | 338,774,257,466 | [
"task_categories:automatic-speech-recognition",
"language:ar",
"language:en",
"size_categories:100K<n<1M",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"arabic",
"dialectal-arabic",
"speech"... | 2026-03-29T13:03:29 | 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 | 126 | 11 | 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... | 9,900 | 42,345 | 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 |
621ffdd236468d709f181f95 | rajpurkar/squad | rajpurkar | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced", "found"], "language": ["en"], "license": "cc-by-sa-4.0", "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|wikipedia"], "task_categories": ["question-answering"], "task_ids": ["extractive-... | false | False | 2024-03-04T13:54:37 | 461 | 10 | false | 7b6d24c440a36b6815f21b70d25016731768db1f |
Dataset Card for SQuAD
Dataset Summary
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading pass... | 212,020 | 7,678,308 | 16,286,997 | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:extended|wikipedia",
"language:en",
"license:cc-by-sa-4.0",
"size_categories:10K<n<100K"... | 2022-03-02T23:29:22 | squad | 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 | 1,562 | 10 | 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.
... | 960,715 | 14,331,275 | 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 |
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 | 2026-08-09T15:21:10 | 35 | 10 | false | 1c1650f4b8a882c29fd4cec41c2eaa671e6d1716 | 🌐 Browse UnsolvedMath online
✅ Paper: Open Mathematical Problems as an AI Reasoning Benchmark
UnsolvedMath Dataset
A comprehensive curated collection of 5,426 open mathematics problems across all domains and difficulty levels, including the largest collection of Erdős problems available in machine-reada... | 759 | 1,417 | 42,389,369 | [
"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 |
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 | 200 | 10 | 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... | 10,108 | 10,199 | 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 |
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 | 11 | 10 | 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... | 383 | 387 | 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 |
6a701d672864f8c3074a5577 | mfzheng/Scene2Wave | mfzheng | {"pretty_name": "Scene2Wave", "license": "other", "license_name": "scene2wave-layered-license-notice", "license_link": "https://huggingface.co/datasets/mfzheng/Scene2Wave/blob/main/LICENSE", "task_categories": ["feature-extraction"], "size_categories": ["n<1K"], "tags": ["wireless", "6g", "multimodal", "channel-impulse... | false | False | 2026-08-05T17:25:05 | 31 | 10 | false | 2e07b94a0b2ddff5643590a3c90361c3ba723479 |
Scene2Wave
Scene2Wave is a synchronized multimodal wireless-channel dataset generated
with CARLA and Sionna RT. Version 1.0.1 contains 100 formally validated
samples spanning four CARLA Towns, five motion states, four radio profiles,
multi-view cameras, Birdview, LiDAR, Radar, IMU, GNSS, poses, path-leve... | 427 | 427 | 66,904,458,545 | [
"task_categories:feature-extraction",
"license:other",
"size_categories:n<1K",
"region:us",
"wireless",
"6g",
"multimodal",
"channel-impulse-response",
"ray-tracing",
"autonomous-driving",
"lidar",
"radar",
"carla",
"sionna"
] | 2026-08-03T04:47:35 | 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 | 31 | 9 | 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... | 3,155 | 6,351 | 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 |
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 | 9 | 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... | 251 | 328 | 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 |
6a4509196c643209b19b2fc7 | Manusagents/GPT-5.5-Gemini-3.1-Pro-Grok-4-Claude-Fable-5-Mythos-5-Qwen-3.7-Max-and-more-Distillation-Dataset | Manusagents | {"license": "mit", "language": ["en", "multilingual"], "task_categories": ["text-generation", "other"], "tags": ["distillation", "instruction-tuning", "sft", "reasoning", "coding", "code-repositories", "cybersecurity", "attack", "defense", "exploit", "penetration-testing", "red-team", "blue-team", "open-source", "colle... | false | False | 2026-07-18T18:01:17 | 183 | 9 | false | f0aa1d8326d7ca5c4a01982ca8299a783bc59faf |
📖 The Open Distillation Codex
🌌 The Ultimate Open-Source Distillation Dataset — No Skip, Full, with Attack & Defense 🌌
Where 73 open-source minds converge into one unified stream of intelligence
18M+ Distilled Signals · 7,090 Raw GitHub Repositories · 8 Curated Categories · ~7... | 14,084 | 18,562 | 76,526,135,473 | [
"task_categories:text-generation",
"task_categories:other",
"language:en",
"language:multilingual",
"license:mit",
"size_categories:10M<n<100M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"distillation",
"in... | 2026-07-01T12:33:29 | 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 | 9 | 9 | 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... | 0 | 0 | 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 |
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 | 26 | 8 | 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 ... | 179,705 | 394,513 | 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 |
6a5c8a4b79e683e9ae9d6a24 | ehabnegm/100-hour-Egyption-dataset-single-speaker | ehabnegm | {"license": "cc-by-nc-4.0", "language": ["ar", "arz"], "language_bcp47": ["ar-EG"], "pretty_name": "Masri 100h \u2014 Egyptian Arabic Single-Speaker Speech Corpus", "task_categories": ["text-to-speech", "automatic-speech-recognition"], "annotations_creators": ["machine-generated"], "language_creators": ["found"], "sour... | false | False | 2026-08-07T21:59:54 | 8 | 8 | false | 03e4938f4c53b2ed6d2d36ca4dbf73175b1c43ee |
Masri 100h — Egyptian Arabic Single-Speaker Speech Corpus
A 100-hour Egyptian Arabic (مصري) single-narrator speech collection — 15,653 released clips at 24 kHz mono, with aligned transcripts.
Egyptian Arabic is the most widely understood Arabic dialect and one of the least served by open speech data.
Alm... | 338 | 338 | 12,451,957,557 | [
"task_categories:text-to-speech",
"task_categories:automatic-speech-recognition",
"annotations_creators:machine-generated",
"language_creators:found",
"source_datasets:original",
"language:ar",
"language:arz",
"license:cc-by-nc-4.0",
"size_categories:10K<n<100K",
"format:audiofolder",
"modality:... | 2026-07-19T08:26:51 | null | null |
6a770f59b4a20e9e9b17955b | bench-labs/slop-classification | bench-labs | {"license": "mit", "language": ["en"], "tags": ["text", "conversations", "classification"], "pretty_name": "Slop classifier dataset"} | false | False | 2026-08-11T19:36:23 | 8 | 8 | false | aad261413f1ace243c9e8ed44fe6ebdf3beb8439 |
Slop classifier dataset
A human-annotated dataset for studying and classifying AI-generated text that people perceive as “AI slop.”
The dataset is built from samples collected from existing public datasets and annotated through the Bench Labs SlopFinder interface.
Slop score
Each sample re... | 143 | 143 | 389,782 | [
"language:en",
"license:mit",
"size_categories:n<1K",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"text",
"conversations",
"classification"
] | 2026-08-08T11:13:29 | 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
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