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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
698b2c8b4c9e577aa3b1fa16 | nohurry/Opus-4.6-Reasoning-3000x-filtered | nohurry | {"license": "apache-2.0"} | false | False | 2026-02-10T13:06:40 | 343 | 82 | false | 80e9226ea6168634ee2d6c010c3da619af8ad542 | Filtered from: https://huggingface.co/datasets/crownelius/Opus-4.6-Reasoning-3000x
The original dataset has 979 refusals, I removed these in this version.
| 4,646 | 4,698 | [
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-02-10T13:03:07 | null | null |
699250f08be5bf8321aeb29e | HuggingFaceFW/finephrase | HuggingFaceFW | {"language": ["en"], "license": "odc-by", "tags": ["SmolLM2-1.7B-Instruct", "fineweb-edu", "synthetic"], "annotations_creators": ["machine-generated"], "language_creators": ["found"], "pretty_name": "HuggingFaceFW/finephrase", "size_categories": ["n>1M"], "source_datasets": ["HuggingFaceFW/fineweb-edu/sample-350BT"], "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "configs": [{"config_name": "all", "data_files": [{"split": "train", "path": ["faq/**/*.parquet", "math/**/*.parquet", "table/**/*.parquet", "tutorial/**/*.parquet"]}]}, {"config_name": "faq", "data_files": [{"split": "train", "path": "faq/**/*.parquet"}]}, {"config_name": "math", "data_files": [{"split": "train", "path": "math/**/*.parquet"}]}, {"config_name": "table", "data_files": [{"split": "train", "path": "table/**/*.parquet"}]}, {"config_name": "tutorial", "data_files": [{"split": "train", "path": "tutorial/**/*.parquet"}]}], "train-eval-index": [{"config": "all", "task": "text-generation", "task_id": "language-modeling", "splits": {"train_split": "train", "eval_split": null}, "col_mapping": {"text": "text"}}]} | false | False | 2026-03-07T19:16:51 | 73 | 72 | false | a9046961aa1360172836a82f63563db9b44993d3 |
Dataset Card for HuggingFaceFW/finephrase
Dataset Summary
Synthetic data generated by DataTrove:
Model: HuggingFaceTB/SmolLM2-1.7B-Instruct (main)
Source dataset: HuggingFaceFW/fineweb-edu, config sample-350BT, split train
Generation config: temperature=1.0, top_p=1.0, top_k=50, max_tokens=2048, model_max_context=8192
Speculative decoding: {"method":"suffix","num_speculative_tokens":32}
System prompt: None
Input column: text
Prompt families:
faq prompt
Rewrite the… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/finephrase. | 76,704 | 76,704 | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:machine-generated",
"language_creators:found",
"source_datasets:HuggingFaceFW/fineweb-edu/sample-350BT",
"language:en",
"license:odc-by",
"size_categories:1B<n<10B",
"modality:tabular",
"modality:text",
"regio... | 2026-02-15T23:04:16 | null | null |
698e4ad0913c4d1f4a64479a | crownelius/Opus-4.6-Reasoning-3300x | crownelius | {"license": "apache-2.0"} | false | False | 2026-03-02T05:37:24 | 164 | 54 | false | 2aaf2ade07cefc9fa733f4ce8d9abdd152e7ec91 |
Opus-4.6-Reasoning-3000x (Cleaned)
This dataset has been automatically cleaned to remove:
Empty or missing responses
Responses shorter than 10 characters
Refusal responses ("problem is incomplete", "cannot solve", etc.)
Responses with no substantive content
Responses that just echo the problem
Cleaning Report
Original rows: 3,305
Clean rows: 2,160
Removed: 1,145 (34.6%)
Columns: ['id', 'problem', 'thinking', 'solution', 'difficulty', 'category', 'timestamp', 'hash']… See the full description on the dataset page: https://huggingface.co/datasets/crownelius/Opus-4.6-Reasoning-3300x. | 2,025 | 2,029 | [
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-02-12T21:49:04 | null | null |
69b27063693ba5b211bd0a99 | markov-ai/computer-use-large | markov-ai | {"license": "cc-by-4.0", "task_categories": ["video-classification", "robotics"], "language": ["en"], "tags": ["screen-recording", "computer-use", "software-tutorials", "gui", "desktop"], "size_categories": ["10K<n<100K"], "configs": [{"config_name": "autocad", "data_files": [{"split": "train", "path": ["data/autocad/*", "data/autocad_2/*"]}]}, {"config_name": "blender", "data_files": [{"split": "train", "path": ["data/blender/*", "data/blender_2/*"]}]}, {"config_name": "excel", "data_files": [{"split": "train", "path": "data/excel/*"}]}, {"config_name": "photoshop", "data_files": [{"split": "train", "path": ["data/photoshop/*", "data/photoshop_2/*"]}]}, {"config_name": "salesforce", "data_files": [{"split": "train", "path": "data/salesforce/*"}]}, {"config_name": "vscode", "data_files": [{"split": "train", "path": "data/vscode/*"}]}]} | false | False | 2026-03-12T13:40:50 | 44 | 44 | false | a2655997b110408aab09cfc55e2d573a2ca59a27 |
Computer Use Large
A large-scale dataset of 48,478 screen recording videos (~12,300 hours) of professional software being used, sourced from the internet. All videos have been trimmed to remove non-screen-recording content (intros, outros, talking heads, transitions) and audio has been stripped.
Dataset Summary
Category
Videos
Hours
AutoCAD
10,059
2,149
Blender
11,493
3,624
Excel
8,111
2,002
Photoshop
10,704
2,060
Salesforce
7,807
2,336
VS Code
304… See the full description on the dataset page: https://huggingface.co/datasets/markov-ai/computer-use-large. | 45,637 | 45,637 | [
"task_categories:video-classification",
"task_categories:robotics",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"region:us",
"screen-recording",
"computer-use",
"software-tutorials",
"gui",
"desktop"
] | 2026-03-12T07:50:59 | null | null |
69a5b45a59ca5dda6cff15a9 | TuringEnterprises/Open-RL | TuringEnterprises | {"license": "mit", "language": ["en"], "tags": ["chemistry", "physics", "math", "biology", "science"], "pretty_name": "open-rl", "size_categories": ["n<1K"], "task_categories": ["question-answering"]} | false | False | 2026-03-04T11:24:40 | 173 | 42 | false | cef3b89150d73474ec6b9203897ce2d8d2dcd2bf |
Open-RL
Dataset Summary
This dataset contains self-contained, verifiable, and unambiguous STEM reasoning problems across Physics, Mathematics, Biology, and Chemistry.
Each problem:
Requires multi-step reasoning
Involves symbolic manipulation and/or numerical computation
Has a deterministic, objectively verifiable final answer
The problems were evaluated against contemporary large language models. Observed pass rates indicate that the tasks are non-trivial yet… See the full description on the dataset page: https://huggingface.co/datasets/TuringEnterprises/Open-RL. | 11,823 | 11,823 | [
"task_categories:question-answering",
"language:en",
"license:mit",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"chemistry",
"physics",
"math",
"biology",
"science"
] | 2026-03-02T16:01:30 | null | null |
6988f3d2dd11cee339d8c40b | karpathy/tinystories-gpt4-clean | karpathy | {"license": "cdla-sharing-1.0"} | false | False | 2026-02-08T21:07:28 | 46 | 39 | false | 0397e27157956705a0260709da3095bb9c43d6a7 |
TinyStories GPT-4 Clean
A cleaned subset of the TinyStories dataset (Eldan & Li, 2023), keeping only GPT-4-generated stories. Adapted from this thread that pointed out many issues with the original data and proposed a cleaning process.
Overview
This cleaned dataset contains:
Stat
Value
Stories
2,732,634
Total characters
~2.19B
Min doc length
115 chars
Max doc length
4,433 chars
Median doc length
721 chars
Unique characters
74 (ASCII only)
Duplicates… See the full description on the dataset page: https://huggingface.co/datasets/karpathy/tinystories-gpt4-clean. | 1,556 | 1,581 | [
"license:cdla-sharing-1.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2305.07759",
"region:us"
] | 2026-02-08T20:36:34 | null | null |
696e2528357a40707550b1c4 | google/WaxalNLP | google | {"language_creators": ["creator_1"], "language": ["ach", "aka", "amh", "bau", "dag", "dga", "ewe", "fat", "ful", "hau", "ibo", "kik", "kpo", "lin", "lug", "luo", "mas", "mlg", "nyn", "orm", "pcm", "sid", "sna", "sog", "swa", "tir", "twi", "wal", "yor"], "license": ["cc-by-sa-4.0", "cc-by-4.0"], "multilinguality": ["multilingual"], "source_datasets": ["UGSpeechData", "DigitalUmuganda/AfriVoice", "original"], "task_categories": ["automatic-speech-recognition", "text-to-speech"], "pretty_name": "Waxal NLP Datasets", "arxiv": 2602.02734, "annotation_creators": ["human-annotated", "crowdsourced"], "tags": ["audio", "automatic-speech-recognition", "text-to-speech"], "configs": [{"config_name": "ach_asr", "data_files": [{"split": "train", "path": "data/ASR/ach/ach-train-*"}, {"split": "validation", "path": "data/ASR/ach/ach-validation-*"}, {"split": "test", "path": "data/ASR/ach/ach-test-*"}, {"split": "unlabeled", "path": "data/ASR/ach/ach-unlabeled-*"}]}, {"config_name": "ach_tts", "data_files": [{"split": "train", "path": "data/TTS/ach/ach-train-*"}, {"split": "validation", "path": "data/TTS/ach/ach-validation-*"}, {"split": "test", "path": "data/TTS/ach/ach-test-*"}]}, {"config_name": "aka_asr", "data_files": [{"split": "train", "path": "data/ASR/aka/aka-train-*"}, {"split": "validation", "path": "data/ASR/aka/aka-validation-*"}, {"split": "test", "path": "data/ASR/aka/aka-test-*"}, {"split": "unlabeled", "path": "data/ASR/aka/aka-unlabeled-*"}]}, {"config_name": "amh_asr", "data_files": [{"split": "train", "path": "data/ASR/amh/amh-train-*"}, {"split": "validation", "path": "data/ASR/amh/amh-validation-*"}, {"split": "test", "path": "data/ASR/amh/amh-test-*"}, {"split": "unlabeled", "path": "data/ASR/amh/amh-unlabeled-*"}]}, {"config_name": "bau_tts", "data_files": [{"split": "train", "path": "data/TTS/bau/bau-train-*"}, {"split": "validation", "path": "data/TTS/bau/bau-validation-*"}, {"split": "test", "path": "data/TTS/bau/bau-test-*"}]}, {"config_name": "dag_asr", "data_files": [{"split": "train", "path": "data/ASR/dag/dag-train-*"}, {"split": "validation", "path": "data/ASR/dag/dag-validation-*"}, {"split": "test", "path": "data/ASR/dag/dag-test-*"}, {"split": "unlabeled", "path": "data/ASR/dag/dag-unlabeled-*"}]}, {"config_name": "dga_asr", "data_files": [{"split": "train", "path": "data/ASR/dga/dga-train-*"}, {"split": "validation", "path": "data/ASR/dga/dga-validation-*"}, {"split": "test", "path": "data/ASR/dga/dga-test-*"}, {"split": "unlabeled", "path": "data/ASR/dga/dga-unlabeled-*"}]}, {"config_name": "ewe_asr", "data_files": [{"split": "train", "path": "data/ASR/ewe/ewe-train-*"}, {"split": "validation", "path": "data/ASR/ewe/ewe-validation-*"}, {"split": "test", "path": "data/ASR/ewe/ewe-test-*"}, {"split": "unlabeled", "path": "data/ASR/ewe/ewe-unlabeled-*"}]}, {"config_name": "ewe_tts", "data_files": [{"split": "train", "path": "data/TTS/ewe/ewe-train-*"}, {"split": "validation", "path": "data/TTS/ewe/ewe-validation-*"}, {"split": "test", "path": "data/TTS/ewe/ewe-test-*"}]}, {"config_name": "fat_tts", "data_files": [{"split": "train", "path": "data/TTS/fat/fat-train-*"}, {"split": "validation", "path": "data/TTS/fat/fat-validation-*"}, {"split": "test", "path": "data/TTS/fat/fat-test-*"}]}, {"config_name": "ful_asr", "data_files": [{"split": "train", "path": "data/ASR/ful/ful-train-*"}, {"split": "validation", "path": "data/ASR/ful/ful-validation-*"}, {"split": "test", "path": "data/ASR/ful/ful-test-*"}, {"split": "unlabeled", "path": "data/ASR/ful/ful-unlabeled-*"}]}, {"config_name": "ful_tts", "data_files": [{"split": "train", "path": "data/TTS/ful/ful-train-*"}, {"split": "validation", "path": "data/TTS/ful/ful-validation-*"}, {"split": "test", "path": "data/TTS/ful/ful-test-*"}]}, {"config_name": "hau_tts", "data_files": [{"split": "train", "path": "data/TTS/hau/hau-train-*"}, {"split": "validation", "path": "data/TTS/hau/hau-validation-*"}, {"split": "test", "path": "data/TTS/hau/hau-test-*"}]}, {"config_name": "ibo_tts", "data_files": [{"split": "train", "path": "data/TTS/ibo/ibo-train-*"}, {"split": "validation", "path": "data/TTS/ibo/ibo-validation-*"}, {"split": "test", "path": "data/TTS/ibo/ibo-test-*"}]}, {"config_name": "kik_tts", "data_files": [{"split": "train", "path": "data/TTS/kik/kik-train-*"}, {"split": "validation", "path": "data/TTS/kik/kik-validation-*"}, {"split": "test", "path": "data/TTS/kik/kik-test-*"}]}, {"config_name": "kpo_asr", "data_files": [{"split": "train", "path": "data/ASR/kpo/kpo-train-*"}, {"split": "validation", "path": "data/ASR/kpo/kpo-validation-*"}, {"split": "test", "path": "data/ASR/kpo/kpo-test-*"}, {"split": "unlabeled", "path": "data/ASR/kpo/kpo-unlabeled-*"}]}, {"config_name": "lin_asr", "data_files": [{"split": "train", "path": "data/ASR/lin/lin-train-*"}, {"split": "validation", "path": "data/ASR/lin/lin-validation-*"}, {"split": "test", "path": "data/ASR/lin/lin-test-*"}, {"split": "unlabeled", "path": "data/ASR/lin/lin-unlabeled-*"}]}, {"config_name": "lug_asr", "data_files": [{"split": "train", "path": "data/ASR/lug/lug-train-*"}, {"split": "validation", "path": "data/ASR/lug/lug-validation-*"}, {"split": "test", "path": "data/ASR/lug/lug-test-*"}, {"split": "unlabeled", "path": "data/ASR/lug/lug-unlabeled-*"}]}, {"config_name": "lug_tts", "data_files": [{"split": "train", "path": "data/TTS/lug/lug-train-*"}, {"split": "validation", "path": "data/TTS/lug/lug-validation-*"}, {"split": "test", "path": "data/TTS/lug/lug-test-*"}]}, {"config_name": "luo_tts", "data_files": [{"split": "train", "path": "data/TTS/luo/luo-train-*"}, {"split": "validation", "path": "data/TTS/luo/luo-validation-*"}, {"split": "test", "path": "data/TTS/luo/luo-test-*"}]}, {"config_name": "mas_asr", "data_files": [{"split": "train", "path": "data/ASR/mas/mas-train-*"}, {"split": "validation", "path": "data/ASR/mas/mas-validation-*"}, {"split": "test", "path": "data/ASR/mas/mas-test-*"}, {"split": "unlabeled", "path": "data/ASR/mas/mas-unlabeled-*"}]}, {"config_name": "mlg_asr", "data_files": [{"split": "train", "path": "data/ASR/mlg/mlg-train-*"}, {"split": "validation", "path": "data/ASR/mlg/mlg-validation-*"}, {"split": "test", "path": "data/ASR/mlg/mlg-test-*"}, {"split": "unlabeled", "path": "data/ASR/mlg/mlg-unlabeled-*"}]}, {"config_name": "nyn_asr", "data_files": [{"split": "train", "path": "data/ASR/nyn/nyn-train-*"}, {"split": "validation", "path": "data/ASR/nyn/nyn-validation-*"}, {"split": "test", "path": "data/ASR/nyn/nyn-test-*"}, {"split": "unlabeled", "path": "data/ASR/nyn/nyn-unlabeled-*"}]}, {"config_name": "nyn_tts", "data_files": [{"split": "train", "path": "data/TTS/nyn/nyn-train-*"}, {"split": "validation", "path": "data/TTS/nyn/nyn-validation-*"}, {"split": "test", "path": "data/TTS/nyn/nyn-test-*"}]}, {"config_name": "orm_asr", "data_files": [{"split": "train", "path": "data/ASR/orm/orm-train-*"}, {"split": "validation", "path": "data/ASR/orm/orm-validation-*"}, {"split": "test", "path": "data/ASR/orm/orm-test-*"}, {"split": "unlabeled", "path": "data/ASR/orm/orm-unlabeled-*"}]}, {"config_name": "pcm_tts", "data_files": [{"split": "train", "path": "data/TTS/pcm/pcm-train-*"}, {"split": "validation", "path": "data/TTS/pcm/pcm-validation-*"}, {"split": "test", "path": "data/TTS/pcm/pcm-test-*"}]}, {"config_name": "sid_asr", "data_files": [{"split": "train", "path": "data/ASR/sid/sid-train-*"}, {"split": "validation", "path": "data/ASR/sid/sid-validation-*"}, {"split": "test", "path": "data/ASR/sid/sid-test-*"}, {"split": "unlabeled", "path": "data/ASR/sid/sid-unlabeled-*"}]}, {"config_name": "sna_asr", "data_files": [{"split": "train", "path": "data/ASR/sna/sna-train-*"}, {"split": "validation", "path": "data/ASR/sna/sna-validation-*"}, {"split": "test", "path": "data/ASR/sna/sna-test-*"}, {"split": "unlabeled", "path": "data/ASR/sna/sna-unlabeled-*"}]}, {"config_name": "tir_asr", "data_files": [{"split": "train", "path": "data/ASR/tir/tir-train-*"}, {"split": "validation", "path": "data/ASR/tir/tir-validation-*"}, {"split": "test", "path": "data/ASR/tir/tir-test-*"}, {"split": "unlabeled", "path": "data/ASR/tir/tir-unlabeled-*"}]}, {"config_name": "sog_asr", "data_files": [{"split": "train", "path": "data/ASR/sog/sog-train-*"}, {"split": "validation", "path": "data/ASR/sog/sog-validation-*"}, {"split": "test", "path": "data/ASR/sog/sog-test-*"}, {"split": "unlabeled", "path": "data/ASR/sog/sog-unlabeled-*"}]}, {"config_name": "swa_tts", "data_files": [{"split": "train", "path": "data/TTS/swa/swa-train-*"}, {"split": "validation", "path": "data/TTS/swa/swa-validation-*"}, {"split": "test", "path": "data/TTS/swa/swa-test-*"}]}, {"config_name": "twi_tts", "data_files": [{"split": "train", "path": "data/TTS/twi/twi-train-*"}, {"split": "validation", "path": "data/TTS/twi/twi-validation-*"}, {"split": "test", "path": "data/TTS/twi/twi-test-*"}]}, {"config_name": "yor_tts", "data_files": [{"split": "train", "path": "data/TTS/yor/yor-train-*"}, {"split": "validation", "path": "data/TTS/yor/yor-validation-*"}, {"split": "test", "path": "data/TTS/yor/yor-test-*"}]}, {"config_name": "wal_asr", "data_files": [{"split": "train", "path": "data/ASR/wal/wal-train-*"}, {"split": "validation", "path": "data/ASR/wal/wal-validation-*"}, {"split": "test", "path": "data/ASR/wal/wal-test-*"}, {"split": "unlabeled", "path": "data/ASR/wal/wal-unlabeled-*"}]}], "dataset_info": [{"config_name": "ach_asr", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "ach_tts", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "aka_asr", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "amh_asr", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "bau_tts", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "dag_asr", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "dga_asr", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "ewe_asr", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "ewe_tts", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "fat_tts", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "ful_asr", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "fuf_tts", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "ful_tts", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "hau_tts", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "ibo_tts", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "kik_tts", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "kpo_asr", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "lin_asr", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "lug_asr", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "lug_tts", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "luo_tts", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "mas_asr", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "mlg_asr", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "nyn_asr", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "nyn_tts", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "orm_asr", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "pcm_tts", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "sid_asr", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "sna_asr", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "sog_asr", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "swa_tts", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "tir_asr", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "twi_tts", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "wal_asr", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}, {"config_name": "yor_tts", "features": [{"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "audio", "dtype": "audio"}]}]} | false | False | 2026-03-13T11:58:41 | 196 | 33 | false | beab143ae6d8a5e054281241afd76565ecb57e03 |
Waxal Datasets
The WAXAL dataset is a large-scale multilingual speech corpus for African languages, introduced in the paper WAXAL: A Large-Scale Multilingual African Language Speech Corpus.
Dataset Description
The Waxal project provides datasets for both Automated Speech Recognition (ASR)
and Text-to-Speech (TTS) for African languages. The goal of this dataset's
creation and release is to facilitate research that improves the accuracy and
fluency of speech and language… See the full description on the dataset page: https://huggingface.co/datasets/google/WaxalNLP. | 10,447 | 19,079 | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"language_creators:creator_1",
"multilinguality:multilingual",
"source_datasets:UGSpeechData",
"source_datasets:DigitalUmuganda/AfriVoice",
"source_datasets:original",
"language:ach",
"language:aka",
"language:amh",
... | 2026-01-19T12:35:52 | null | null |
698a9b89700a694a5b97db6f | AudioVisual-Caption/ASID-1M | AudioVisual-Caption | {"license": "cc-by-2.0", "language": ["en"], "pretty_name": "ASID-1M", "tags": ["caption", "audiovisual", "instruction-tuning", "attribute-structured", "quality-verified", "video-understanding"], "task_categories": ["image-text-to-text"], "configs": [{"config_name": "all_attributes", "data_files": [{"split": "train", "path": ["annotations/0_30_s_youtube_v0_1/train/all_attributes_0_30_s_youtube_v0_1.jsonl", "annotations/30_60_s_youtube_v0_1/train/all_attributes_30_60_s_youtube_v0_1.jsonl", "annotations/1_2_m_youtube_v0_1/train/all_attributes_1_2_m_youtube_v0_1.jsonl", "annotations/finevideo/train/all_attributes_finevideo.jsonl"]}]}, {"config_name": "single_attribute", "data_files": [{"split": "train", "path": ["annotations/0_30_s_youtube_v0_1/train/single_attribute_0_30_s_youtube_v0_1.jsonl", "annotations/30_60_s_youtube_v0_1/train/single_attribute_30_60_s_youtube_v0_1.jsonl", "annotations/1_2_m_youtube_v0_1/train/single_attribute_1_2_m_youtube_v0_1.jsonl", "annotations/finevideo/train/single_attribute_finevideo.jsonl"]}]}]} | false | False | 2026-03-11T12:26:08 | 68 | 29 | false | 209550390d32c41cb138a8503f82a663a4da357d |
ASID-1M: Attribute-Structured and Quality-Verified Audiovisual Instructions
[🏠 Homepage] [📖 Arxiv Paper] [🤗 Models & Datasets] [💻 Code]
Introduction
We introduce ASID-1M, a large-scale audiovisual instruction dataset built to support universal video understanding with fine-grained, controllable supervision.
Most existing video-instruction data represents complex audiovisual content as a single, monolithic caption. This often leads to incomplete coverage (missing audio… See the full description on the dataset page: https://huggingface.co/datasets/AudioVisual-Caption/ASID-1M. | 1,948 | 2,008 | [
"task_categories:image-text-to-text",
"language:en",
"license:cc-by-2.0",
"size_categories:100K<n<1M",
"format:json",
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"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2602.13013",
"region:us",
"caption",
"audiovisual",
"instruction-tu... | 2026-02-10T02:44:25 | null | null |
69a5c92559ca5dda6c00b2f8 | Jackrong/Qwen3.5-reasoning-700x | Jackrong | {"license": "apache-2.0", "language": ["en"], "tags": ["reasoning", "math", "distillation", "instruction-tuning", "chain-of-thought", "qwen", "qwen3.5"], "task_categories": ["question-answering"], "size_categories": ["n<1K"]} | false | False | 2026-03-02T17:44:52 | 38 | 29 | false | 1b6c703da5319ded200d9e7c91e0b57b4a7c922c |
Dataset Card (Qwen3.5-reasoning-700x)
Dataset Summary
Qwen3.5-reasoning-700x is a high-quality distilled dataset.
This dataset uses the high-quality instructions constructed by Alibaba-Superior-Reasoning-Stage2 as the seed question set. By calling the latest Qwen3.5-27B full-parameter model on the Alibaba Cloud DashScope platform as the teacher model, it generates high-quality responses featuring long-text reasoning processes (Chain-of-Thought). It covers several major… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/Qwen3.5-reasoning-700x. | 690 | 690 | [
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"reasoning",
"math",
"distillation",
"instruction-tuning",
"cha... | 2026-03-02T17:30:13 | null | null |
69a7282144067eabb6017453 | ronantakizawa/github-codereview | ronantakizawa | {"license": "other", "task_categories": ["text-generation"], "language": ["en", "code"], "tags": ["code-review", "code-generation", "software-engineering", "pull-requests", "github"], "size_categories": ["100K<n<1M"]} | false | False | 2026-03-10T00:59:34 | 35 | 29 | false | c3e3c6e7e9f61e3e7a5b52894bcd440d586ae6ca |
Code Review Dataset
A large-scale dataset of the best human-written code reviews from top GitHub repositories.
Each row captures a moment where a human code reviewer left an inline comment on a pull request, and the author subsequently modified the code in response.
The dataset also includes negative examples — code from the same PRs that passed review without comments — to help models learn when code is acceptable.
This provides a natural signal for training models to:
Generate… See the full description on the dataset page: https://huggingface.co/datasets/ronantakizawa/github-codereview. | 306 | 306 | [
"task_categories:text-generation",
"language:en",
"language:code",
"license:other",
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"code-review",
"code-generatio... | 2026-03-03T18:27:45 | null | null |
69afdb9aea6ad7cbfa28b5fe | ginigen-ai/smol-worldcup | ginigen-ai | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "shift_axis", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "subcategory", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "answer_key", "dtype": "string"}, {"name": "explanation", "dtype": "string"}, {"name": "grading_rule", "dtype": "string"}, {"name": "auto_grade", "dtype": "string"}, {"name": "max_score", "dtype": "int64"}, {"name": "anchor", "dtype": "bool"}, {"name": "season", "dtype": "int64"}, {"name": "version", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "language_name", "dtype": "string"}], "splits": [{"name": "train", "num_examples": 125}]}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "smol_worldcup_s1.jsonl"}]}], "license": "apache-2.0", "task_categories": ["text-generation", "question-answering"], "language": ["en", "ko", "ar", "pt", "tr", "bn", "th"], "tags": ["benchmark", "small-language-models", "SHIFT-framework", "WCS", "honesty", "hallucination-detection", "smol-ai-worldcup", "evaluation", "multilingual", "edge-ai", "PIR"], "pretty_name": "\ud83c\udfdf\ufe0f Smol AI WorldCup \u2014 SHIFT Benchmark", "size_categories": ["n<1K"], "models": ["meta-llama/Llama-3.2-1B-Instruct", "Qwen/Qwen3-1.7B", "openai/gpt-oss-20b", "CohereLabs/tiny-aya-fire", "Qwen/Qwen3-4B-Instruct-2507", "google/gemma-3n-E4B-it", "zai-org/GLM-4.7-Flash", "mistralai/Mistral-7B-Instruct-v0.2", "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "Qwen/Qwen3-8B", "meta-llama/Llama-3.1-8B-Instruct", "nvidia/Llama-3.1-Nemotron-Nano-8B-v1", "Qwen/Qwen3.5-9B", "allenai/Olmo-3-7B-Instruct", "google/gemma-3-12b-it", "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B", "Qwen/Qwen3.5-35B-A3B", "meta-llama/Llama-4-Scout-17B-16E-Instruct"]} | false | False | 2026-03-10T14:47:44 | 29 | 29 | false | a304802ece2692d2beb3b3a62bf67c50b7f3c60b |
🏟️ Smol AI WorldCup — SHIFT Benchmark
The world's first 5-axis evaluation framework for small language models.
Not just "how smart?" — but "how honest? how fast? how small? how efficient?"
🏟️ Leaderboard
huggingface.co/spaces/ginigen-ai/smol-worldcup
📊 Dataset
huggingface.co/datasets/ginigen-ai/smol-worldcup
🏅 ALL Bench
huggingface.co/spaces/FINAL-Bench/all-bench-leaderboard
🏆 Official Ranking: WCS (WorldCup Score)
WCS = √( SHIFT × PIR_norm )… See the full description on the dataset page: https://huggingface.co/datasets/ginigen-ai/smol-worldcup. | 1,311 | 1,311 | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"language:ko",
"language:ar",
"language:pt",
"language:tr",
"language:bn",
"language:th",
"license:apache-2.0",
"size_categories:n<1K",
"format:json",
"modality:tabular",
"modality:text",
"library:dat... | 2026-03-10T08:51:38 | null | null |
69af21616259df956494b1ce | yatin-superintelligence/Edge-Agent-Reasoning-WebSearch-260K | yatin-superintelligence | {"pretty_name": "Edge Agent Reasoning WebSearch 260K", "license": "mit", "language": ["en"], "library_name": "datasets", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "question-answering", "any-to-any", "robotics"], "tags": ["text", "3d", "image", "synthetic", "agentic", "reasoning", "RAG", "system-2", "chain-of-thought", "web-search", "document", "edge-ai", "tool-use", "software", "engineering", "code", "legal", "medical", "healthcare", "biology", "chemistry", "finance", "science", "climate", "art", "design", "music", "audio", "video", "agent", "datasets", "parquet", "pandas", "polars", "dask"], "dataset_info": {"features": [{"name": "batch_index_id", "dtype": "int64"}, {"name": "role", "dtype": "string"}, {"name": "industry", "dtype": "string"}, {"name": "os", "dtype": "string"}, {"name": "user_prompt", "dtype": "string"}, {"name": "agent_reasoning", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 712900000, "num_examples": 263098}]}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "edge_reasoning_train_*.parquet"}]}]} | false | False | 2026-03-13T21:42:21 | 23 | 23 | false | 7e8e455fff52e6d21dce4ce4a5a1bddd13031e1a |
Edge Agent Reasoning WebSearch 260K
Abstract
The Edge-Agent-Reasoning-WebSearch-260K dataset is a massive, synthetically expert-engineered corpus of over 700 Million tokens, designed to train small, local models (SLMs) and edge-deployed agents in advanced problem deconstruction and self-aware reasoning.
Rather than training a model to execute instructions directly—which often leads to hallucinations when context is missing—this dataset trains a model to act as a… See the full description on the dataset page: https://huggingface.co/datasets/yatin-superintelligence/Edge-Agent-Reasoning-WebSearch-260K. | 1,275 | 1,275 | [
"task_categories:text-generation",
"task_categories:question-answering",
"task_categories:any-to-any",
"task_categories:robotics",
"language:en",
"license:mit",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"modality:3d",
"modality:image",
"modality:document",
"modality:aud... | 2026-03-09T19:37:05 | null | null |
67e4291146baf23164358d53 | nvidia/Nemotron-ClimbMix | nvidia | {"language": ["en"], "license": "cc-by-nc-4.0", "task_categories": ["text-generation"], "configs": [{"config_name": "default", "data_files": "*.jsonl"}]} | false | False | 2025-10-21T15:05:35 | 82 | 19 | false | 5eaa64b9c0c85b7f56af01d7dffdb0795816b12b |
ClimbMix Dataset
🚀 Creating the highest-quality pre-training datasets for LLMs 🌟
📄 PAPER
🤗 CLIMBLAB
🤗 CLIMBMIX
🏠 HOMEPAGE
Figure 1: Continuously training a 1B model yields a 2.0% improvement over Llama-3.2-1B, demonstrating a more efficient scaling trend compared to prior models.
Figure 2: Pre-training a 1B model from scratch on ClimbMix shows better scaling effects than training on other datasets.… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-ClimbMix. | 9,103 | 37,549 | [
"task_categories:text-generation",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:100M<n<1B",
"format:json",
"modality:tabular",
"library:datasets",
"library:dask",
"library:mlcroissant",
"arxiv:2504.13161",
"region:us"
] | 2025-03-26T16:19:29 | null | null |
6996711477c275fd9adb7137 | nvidia/Nemotron-Terminal-Corpus | nvidia | {"license": "cc-by-4.0", "task_categories": ["question-answering"], "language": ["en"], "tags": ["code"], "size_categories": ["100K<n<1M"], "configs": [{"config_name": "dataset_adapters", "data_files": [{"split": "train", "path": "dataset_adapters/*.parquet"}]}, {"config_name": "skill_based_easy", "data_files": [{"split": "train", "path": "synthetic_tasks/skill_based/easy/*/data_filtered.parquet"}]}, {"config_name": "skill_based_medium", "data_files": [{"split": "train", "path": "synthetic_tasks/skill_based/medium/*/data_filtered.parquet"}]}, {"config_name": "skill_based_mixed", "data_files": [{"split": "train", "path": "synthetic_tasks/skill_based/mixed/*/data_filtered.parquet"}]}]} | false | False | 2026-02-27T22:37:57 | 95 | 18 | false | a1667c4ffdadea02a89bffe4f1bb7ca2ff19f8d9 |
Terminal-Corpus: Large-Scale SFT Dataset for Terminal Agents
Terminal-Corpus is a large-scale Supervised Fine-Tuning (SFT) dataset designed to scale the terminal interaction capabilities of Large Language Models (LLMs). Developed by NVIDIA, this dataset was built using the Terminal-Task-Gen pipeline, which combines dataset adaptation with synthetic task generation across diverse domains.
🚀 Key Results & Performance
The high-quality trajectories in Terminal-Corpus enable… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Terminal-Corpus. | 2,475 | 2,475 | [
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69acbc6d461c6ec304b2b943 | FINAL-Bench/ALL-Bench-Leaderboard | FINAL-Bench | {"annotations_creators": ["expert-generated"], "language": ["en"], "license": "mit", "pretty_name": "ALL Bench Leaderboard 2026", "size_categories": ["n<1K"], "source_datasets": ["original"], "tags": ["benchmark", "leaderboard", "llm", "vlm", "ai-evaluation", "gpt-5", "claude", "gemini", "final-bench", "metacognition", "multimodal", "ai-agent", "image-generation", "video-generation", "music-generation", "union-eval"], "task_categories": ["text-generation", "visual-question-answering", "text-to-image", "text-to-video", "text-to-audio"], "configs": [{"config_name": "llm", "data_files": [{"split": "train", "path": "data/llm.jsonl"}]}, {"config_name": "vlm_flagship", "data_files": [{"split": "train", "path": "data/vlm_flagship.jsonl"}]}, {"config_name": "agent", "data_files": [{"split": "train", "path": "data/agent.jsonl"}]}, {"config_name": "image", "data_files": [{"split": "train", "path": "data/image.jsonl"}]}, {"config_name": "video", "data_files": [{"split": "train", "path": "data/video.jsonl"}]}, {"config_name": "music", "data_files": [{"split": "train", "path": "data/music.jsonl"}]}], "models": ["Qwen/Qwen3.5-122B-A10B", "Qwen/Qwen3.5-27B", "Qwen/Qwen3.5-35B-A3B", "Qwen/Qwen3.5-9B", "Qwen/Qwen3.5-4B", "Qwen/Qwen3-Next-80B-A3B-Thinking", "deepseek-ai/DeepSeek-V3", "deepseek-ai/DeepSeek-R1", "zai-org/GLM-5", "meta-llama/Llama-4-Scout-17B-16E-Instruct", "meta-llama/Llama-4-Maverick-17B-128E-Instruct", "microsoft/phi-4", "upstage/Solar-Open-100B", "K-intelligence/Midm-2.0-Base-Instruct", "Nanbeige/Nanbeige4.1-3B", "MiniMaxAI/MiniMax-M2.5", "stepfun-ai/Step-3.5-Flash", "OpenGVLab/InternVL3-78B", "Qwen/Qwen2.5-VL-72B-Instruct", "Qwen/Qwen3-VL-30B-A3B", "black-forest-labs/FLUX.1-dev", "stabilityai/stable-diffusion-3.5-large", "Lightricks/LTX-Video", "facebook/musicgen-large", "facebook/jasco-chords-drums-melody-1B"]} | false | False | 2026-03-10T02:42:24 | 18 | 18 | false | 7378ede0b4776f0bf97f8e106bb9d603c80a5074 |
🏆 ALL Bench Leaderboard 2026
The only AI benchmark dataset covering LLM · VLM · Agent · Image · Video · Music in a single unified file.
Dataset Summary
ALL Bench Leaderboard aggregates and cross-verifies benchmark scores for 90+ AI models across 6 modalities. Every numerical score is tagged with a confidence level (cross-verified, single-source, or self-reported) and its original source. The dataset is designed for researchers, developers, and… See the full description on the dataset page: https://huggingface.co/datasets/FINAL-Bench/ALL-Bench-Leaderboard. | 1,592 | 1,592 | [
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"size_categories:n<1... | 2026-03-08T00:01:49 | null | null |
69981ebb8794c09b40ce6b1e | Oatmealliu/UrbanVerse-100K | Oatmealliu | {"license": "odc-by", "language": ["en"], "pretty_name": "UrbanVerse-100K", "size_categories": ["100K<n<1M"], "task_categories": ["robotics", "text-to-3d", "image-to-3d", "reinforcement-learning", "image-to-text", "text-to-image"], "tags": ["3d", "Robotics", "PhysicalAI", "EmbodiedAI", "Objects", "3DAssets", "UrbanSimulation", "IsaacSim", "IsaacLab"], "extra_gated_fields": {"Full Name": "text", "Email Address": "text", "Country": "country", "Institution": "text", "Sector of Institution": {"type": "select", "options": ["Academic/Education", "Corporation", "Startup", "Government", "Non-profit Organization", "Individual", "Other"]}, "Purpose": {"type": "select", "options": ["Embodied AI", "Physical AI", "3D Generation", "Reinforcement Learning", "Imitation Learning", "Computer Vision", "Autonomous Driving", "Generative Models", "Multimodal Large Language Models", "Visual Question Answering"]}, "I accept the conditions and licenses of the files contained in this dataset": "checkbox"}} | false | manual | 2026-03-11T10:40:11 | 15 | 15 | false | 5625b8038308e5c25320da1d1ddc952f8a291686 |
UrbanVerse-100K Dataset
[!NOTE]
UrbanVerse-100K is a large-scale, physics-aware 3D asset and material database curated for urban simulation, physical and embodied AI research. It contains over 102K metric-scale urban object assets (GLB/USD), along with 646 4K sky maps (HDR) and 403 4K ground (road/sidewalk/terrain) materials (MDL), each annotated with rich semantic and physical attributes. The dataset is IsaacSim-ready, enabling scalable construction of realistic urban… See the full description on the dataset page: https://huggingface.co/datasets/Oatmealliu/UrbanVerse-100K. | 7,854 | 7,854 | [
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... | 2026-02-20T08:43:39 | null | null |
69a70420de30b37a2f37ccca | karpathy/climbmix-400b-shuffle | karpathy | {"license": "mit"} | false | False | 2026-03-03T17:02:01 | 16 | 15 | false | 915333b4f8b8684f39aeaafea600fea6f43fb703 | null | 25,038 | 25,038 | [
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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_name": "Grade School Math 8K", "tags": ["math-word-problems"], "dataset_info": [{"config_name": "main", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3963202, "num_examples": 7473}, {"name": "test", "num_bytes": 713732, "num_examples": 1319}], "download_size": 2725633, "dataset_size": 4676934}, {"config_name": "socratic", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5198108, "num_examples": 7473}, {"name": "test", "num_bytes": 936859, "num_examples": 1319}], "download_size": 3164254, "dataset_size": 6134967}], "configs": [{"config_name": "main", "data_files": [{"split": "train", "path": "main/train-*"}, {"split": "test", "path": "main/test-*"}]}, {"config_name": "socratic", "data_files": [{"split": "train", "path": "socratic/train-*"}, {"split": "test", "path": "socratic/test-*"}]}]} | false | False | 2025-12-20T18:53:44 | 1,196 | 14 | false | cc7b047b6e5bb11b4f1af84efc572db110a51b3c |
Dataset Card for GSM8K
Dataset Summary
GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.
These problems take between 2 and 8 steps to solve.
Solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the… See the full description on the dataset page: https://huggingface.co/datasets/openai/gsm8k. | 598,656 | 9,667,975 | [
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"size_categories:10K<n<100K",
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"library:dat... | 2022-04-12T10:22:10 | gsm8k | null |
69a0ac7cc1f01f9b6b9031de | BytedTsinghua-SIA/CUDA-Agent-Ops-6K | BytedTsinghua-SIA | {"license": "cc-by-4.0", "pretty_name": "CUDA-Agent-Ops-6K", "size_categories": ["1K<n<10K"], "task_categories": ["text-generation"], "language": ["en"]} | false | False | 2026-02-27T19:56:56 | 56 | 14 | false | 44a734c78c947bfcba5189cbfd13f57a6d29a698 |
CUDA-Agent-Ops-6K
CUDA-Agent-Ops-6K is a curated training dataset for CUDA kernel generation and optimization.
It is released as part of the CUDA-Agent project:
Project Page: https://CUDA-Agent.github.io/
Github Repo: https://github.com/BytedTsinghua-SIA/CUDA-Agent
Dataset Summary
CUDA-Agent-Ops-6K contains 6,000 synthesized operator-level training tasks designed for large-scale agentic RL training. It is intended to provide diverse and executable CUDA-oriented training… See the full description on the dataset page: https://huggingface.co/datasets/BytedTsinghua-SIA/CUDA-Agent-Ops-6K. | 516 | 516 | [
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] | 2026-02-26T20:26:36 | null | null |
699e0810251cac84be7d52ba | peteromallet/dataclaw-peteromallet | peteromallet | {"license": "mit", "task_categories": ["text-generation"], "language": ["en"], "tags": ["dataclaw", "claude-code", "codex-cli", "conversations", "coding-assistant", "tool-use", "agentic-coding", "claude-haiku-4-5-20251001", "claude-opus-4-5-20251101", "claude-opus-4-6", "claude-sonnet-4-5-20250929", "claude-sonnet-4-6"], "pretty_name": "Coding Agent Conversations", "configs": [{"config_name": "default", "data_files": "conversations.jsonl"}]} | false | False | 2026-02-25T16:14:13 | 287 | 13 | false | b925056b0539a8bd28a06417dca464aac6ba7bdb |
Coding Agent Conversation Logs
This is a performance art project. Anthropic built their models on the world's freely shared information, then introduced increasingly dystopian data policies to stop anyone else from doing the same — pulling up the ladder behind them. DataClaw lets you throw the ladder back down. The dataset it produces is yours to share.
Exported with DataClaw.
Tag: dataclaw — Browse all DataClaw datasets
Stats
Metric
Value
Sessions
549… See the full description on the dataset page: https://huggingface.co/datasets/peteromallet/dataclaw-peteromallet. | 9,878 | 9,878 | [
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"coding-assistan... | 2026-02-24T20:20:32 | null | null |
69af2c4fe58f63b685b08d5c | yatin-superintelligence/Creative-Professionals-Agentic-Tasks-1M | yatin-superintelligence | {"pretty_name": "Creative Professionals Agentic Tasks (1M)", "language": ["en"], "license": "mit", "library_name": "datasets", "size_categories": ["1M<n<10M"], "task_categories": ["text-generation", "question-answering", "any-to-any"], "tags": ["text", "audio", "video", "3d", "image", "art", "music", "code", "agent", "agentic-tasks", "frontend-development", "ui-ux-design", "game-ui", "3d-animation", "cgi", "vfx", "video-editing", "nonlinear-editing", "music-production", "audio-engineering", "sound-design", "brand-design", "photo-editing", "tool-use", "synthetic", "datasets", "parquet", "pandas", "polars", "dask"], "dataset_info": {"features": [{"name": "batch_id", "dtype": "int64"}, {"name": "index_id", "dtype": "int64"}, {"name": "professional", "dtype": "string"}, {"name": "group", "dtype": "string"}, {"name": "user_prompt", "dtype": "string"}], "splits": [{"name": "train", "num_examples": 1070930}]}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "creative_pro_tasks_train_*.parquet"}]}]} | false | False | 2026-03-13T14:45:14 | 13 | 13 | false | 620e36077ad9325ef19ae8caa4389175272b1c41 |
Creative Professionals Agentic Tasks (1M)
Abstract
A massive-scale, high-fidelity synthetic task dataset comprising 1,070,917 agentic command operations across 36 creative, technical, and engineering software environments. This dataset is engineered exclusively to stress-test, evaluate, and fine-tune multimodal AI agents designed for Agent Environment operation, complex software interaction, and multi-step reasoning within deep software infrastructures.… See the full description on the dataset page: https://huggingface.co/datasets/yatin-superintelligence/Creative-Professionals-Agentic-Tasks-1M. | 835 | 835 | [
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"libr... | 2026-03-09T20:23:43 | null | null |
656523d6bfb751371817c448 | Idavidrein/gpqa | Idavidrein | {"license": "cc-by-4.0", "viewer": true, "extra_gated_prompt": "You agree to NOT reveal examples from this dataset in plain text or images online, to reduce the risk of leakage into foundation model training corpora.", "extra_gated_fields": {"I accept these terms": "checkbox"}, "configs": [{"config_name": "gpqa_extended", "data_files": "gpqa_extended.csv"}, {"config_name": "gpqa_main", "data_files": "gpqa_main.csv"}, {"config_name": "gpqa_diamond", "data_files": "gpqa_diamond.csv"}, {"config_name": "gpqa_experts", "data_files": "gpqa_experts.csv"}], "task_categories": ["question-answering", "text-generation"], "language": ["en"], "tags": ["open-domain-qa", "open-book-qa", "multiple-choice-qa"], "pretty_name": "GPQA", "size_categories": ["n<1K"]} | false | auto | 2026-03-05T23:06:58 | 385 | 12 | false | 633f5ee89ab8ad4522a9f850766b73f62147ffdd |
Dataset Card for GPQA
GPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy, despite spending >30m with full access to Google.
We request that you do not reveal examples from this dataset in plain text or images online, to reduce the risk of leakage into foundation model… See the full description on the dataset page: https://huggingface.co/datasets/Idavidrein/gpqa. | 105,756 | 1,440,182 | [
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"... | 2023-11-27T23:18:46 | null | null |
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/*"}]}, {"config_name": "sample-100BT", "data_files": [{"split": "train", "path": "sample/100BT/*"}]}, {"config_name": "sample-350BT", "data_files": [{"split": "train", "path": "sample/350BT/*"}]}, {"config_name": "CC-MAIN-2025-05", "data_files": [{"split": "train", "path": "data/CC-MAIN-2025-05/*"}]}, {"config_name": "CC-MAIN-2025-08", "data_files": [{"split": "train", "path": "data/CC-MAIN-2025-08/*"}]}, {"config_name": "CC-MAIN-2025-13", "data_files": [{"split": "train", "path": "data/CC-MAIN-2025-13/*"}]}, {"config_name": "CC-MAIN-2025-18", "data_files": [{"split": "train", "path": 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🍷 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 performance and ran on the 🏭 datatrove library, our large scale data processing library.
🍷 FineWeb was originally meant to be a fully open replication of 🦅 RefinedWeb, with a release… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb. | 168,887 | 6,441,547 | [
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"arxiv:2306.01116",
"arxiv:2109.07445",
"arxiv:2406.17557",
"doi:10.57967/hf/2493",
"region:us"
] | 2024-04-18T14:33:13 | null | null |
6928ac839f54f92be8b78d70 | TeichAI/claude-4.5-opus-high-reasoning-250x | TeichAI | null | false | False | 2025-11-28T03:02:41 | 326 | 12 | false | 742c86f88b66bf53cb5961a25e4360f5582f4a6e | This is a reasoning dataset created using Claude Opus 4.5 with a reasoning depth set to high. Some of these questions are from reedmayhew and the rest were generated.
The dataset is meant for creating distilled versions of Claude Opus 4.5 by fine-tuning already existing open-source LLMs.
Stats
Costs: $ 52.3 (USD)
Total tokens (input + output): 2.13 M
| 3,048 | 17,504 | [
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] | 2025-11-27T19:54:43 | null | null |
69af2a96484ef491320cc3c1 | yatin-superintelligence/Audio-Video-Engineering-Agentic-Tasks-1M | yatin-superintelligence | {"pretty_name": "Audio/Video Engineering Agentic Tasks (1M)", "language": ["en"], "license": "mit", "library_name": "datasets", "size_categories": ["1M<n<10M"], "task_categories": ["text-generation", "question-answering", "any-to-any"], "tags": ["text", "audio", "video", "music", "art", "media-production", "digital-audio-workstation", "nonlinear-editing", "agent", "agentic-tasks", "music-composition", "music-production", "sound-design", "video-editing", "tool-use", "troubleshooting", "synthetic", "datasets", "parquet", "pandas", "polars", "dask"], "dataset_info": {"features": [{"name": "batch_id", "dtype": "int64"}, {"name": "index", "dtype": "int64"}, {"name": "professional", "dtype": "string"}, {"name": "group", "dtype": "string"}, {"name": "user_prompt", "dtype": "string"}], "splits": [{"name": "train", "num_examples": 1031068}]}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "av_agentic_tasks_train_*.parquet"}]}]} | false | False | 2026-03-13T14:45:10 | 12 | 12 | false | d2157d1d602b06aee35618a4ef841a489e85b3d1 |
Audio/Video Engineering Agentic Tasks (1M)
Abstract
A highly specialized dataset comprising 1,029,459 in-context troubleshooting prompts and execution commands built for the deepest levels of media production. Unlike standard datasets that simulate clean, theoretical instructions, this matrix captures the chaotic, highly-detailed, and conversational reality of professional audio engineers, composers, and video editors mid-session. It is engineered to train multimodal AI… See the full description on the dataset page: https://huggingface.co/datasets/yatin-superintelligence/Audio-Video-Engineering-Agentic-Tasks-1M. | 320 | 320 | [
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"library:datasets",
"library:pandas",
"library:polars",
... | 2026-03-09T20:16:22 | null | null |
69b03aa205292d5180b6fc1e | maikezu/dowis | maikezu | {"license": "cc-by-4.0", "language": ["de", "en", "es", "cs", "fr", "hu", "it", "nl", "pt", "ru", "sq", "sv"], "tags": ["speech prompts", "text prompts", "instruction following", "benchmark"], "size_categories": ["1K<n<10K"], "dataset_info": {"features": [{"name": "text_prompt", "dtype": "string"}, {"name": "audio_prompt_female_1", "dtype": "audio"}, {"name": "audio_prompt_female_2", "dtype": "audio"}, {"name": "audio_prompt_male_1", "dtype": "audio"}, {"name": "audio_prompt_male_2", "dtype": "audio"}, {"name": "language", "dtype": "string"}, {"name": "task", "dtype": "string"}, {"name": "prompt_type", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 2704378267.6, "num_examples": 1320}], "download_size": 1772318018, "dataset_size": 2704378267.6}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]} | false | False | 2026-03-12T09:17:21 | 12 | 12 | false | 40cebb56cbc5145a9c52555939dc0859188ea42b |
Do What I Say (DOWIS): A Spoken Prompt Dataset for Instruction-Following
NEW DOWIS now also contains spoken and written prompts in Albanian (sq), and for the tasks LIPREAD and SLU!
TL;DR — DOWIS is a multilingual dataset of human-recorded spoken and written instruction prompts, designed to enable realistic evaluation of Speech Large Language Models across 11 tasks and 12 languages.
Dataset Summary
Most Speech LLM benchmarks use text-based prompts, which does… See the full description on the dataset page: https://huggingface.co/datasets/maikezu/dowis. | 115 | 115 | [
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... | 2026-03-10T15:37:06 | null | null |
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📚 FineWeb-Edu
1.3 trillion tokens of the finest educational data the 🌐 web has to offer
Paper: https://arxiv.org/abs/2406.17557
What is it?
📚 FineWeb-Edu dataset consists of 1.3T tokens and 5.4T tokens (FineWeb-Edu-score-2) of educational web pages filtered from 🍷 FineWeb dataset. This is the 1.3 trillion version.
To enhance FineWeb's quality, we developed an educational quality classifier using annotations generated by LLama3-70B-Instruct. We then… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu. | 222,740 | 6,115,881 | [
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"arxiv:2406.17557",
"arxiv:2404.14219",
"arxiv:2401.10020",
... | 2024-05-28T14:32:57 | null | null |
6969d8ba29be2bd1483adfb7 | nvidia/Nemotron-Pretraining-Specialized-v1.1 | nvidia | {"license": "cc-by-4.0", "task_categories": ["text-generation"], "track_downloads": true, "configs": [{"config_name": "Nemotron-Pretraining-Formal-Logic", "data_files": [{"split": "train", "path": "Nemotron-Pretraining-Formal-Logic/*.parquet"}]}, {"config_name": "Nemotron-Pretraining-Economics", "data_files": [{"split": "train", "path": "Nemotron-Pretraining-Economics/*.parquet"}]}, {"config_name": "Nemotron-Pretraining-Multiple-Choice", "data_files": [{"split": "train", "path": "Nemotron-Pretraining-Multiple-Choice/*.parquet"}]}, {"config_name": "Nemotron-Pretraining-Unconditional-Algorithmic", "data_files": [{"split": "train", "path": "Nemotron-Pretraining-Unconditional-Algorithmic/*.parquet"}]}, {"config_name": "Nemotron-Pretraining-Code-Concepts", "data_files": [{"split": "train", "path": "Nemotron-Pretraining-Code-Concepts/*.parquet"}]}]} | false | False | 2026-03-11T14:43:59 | 11 | 11 | false | 13fa979be2e7f7e62913eee0ec5e97c8fd6e24af |
Nemotron-Pretraining-Specialized-v1.1
Dataset Description:
The Nemotron-Pretraining-Specialized-v1.1 dataset is part of the Nemotron Pretraining Data collection of pretraining datasets. Designed for the NVIDIA Nemotron 3 family of LLMs, this dataset contains a collection of synthetic datasets aimed to improve LLM capabilities in code concepts and algorithms, formal logic, economics, and multiple choice questions. The code concepts dataset is an instance of a general… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Specialized-v1.1. | 667 | 667 | [
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"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-01-16T06:20:42 | null | null |
699946473ccabf2d24116f0f | Roman1111111/gemini-3.1-pro-hard-high-reasoning | Roman1111111 | {"license": "mit", "task_categories": ["question-answering", "text-generation", "reasoning"], "tags": ["code", "finance", "legal", "agent", "chemistry", "physics", "synthetic", "gemini-3.1-pro", "high-reasoning", "expert-level"], "size_categories": ["1k<n<10K"], "language": ["en"]} | false | False | 2026-02-21T05:50:10 | 28 | 11 | false | 5b9be1b2b8087b748a8a36c4d47631722d3b3d8e |
Dataset Card for Gemini-3.1-Pro-Ultra-Reasoning-5.6M
Dataset Details
Dataset Description
This dataset represents the frontier of synthetic reasoning data, generated by Gemini 3.1 Pro (High Reasoning variant). While smaller in total token volume than its predecessors (5.6M tokens), this corpus prioritizes logical density and multi-step verification.
The move to the 3.1 architecture provides a measurable leap in "System 2" thinking. Unlike standard models… See the full description on the dataset page: https://huggingface.co/datasets/Roman1111111/gemini-3.1-pro-hard-high-reasoning. | 483 | 483 | [
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"format:json",
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"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"code",
"finance",
"legal",
"ag... | 2026-02-21T05:44:39 | null | null |
69ab632c9d4152acb2e45fb7 | Mustafaege/qwen3.5-toolcalling-v2 | Mustafaege | {"language": ["en"], "license": "apache-2.0", "pretty_name": "Qwen3.5 Tool Calling Dataset v2", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "tags": ["tool-use", "tool-calling", "function-calling", "reasoning", "agentic", "jupyter", "code-execution", "sft", "chat", "qwen3", "qwen3.5", "chain-of-thought", "multi-turn", "structured-output", "json", "fine-tuning", "open-source", "expanded-dataset"], "annotations_creators": ["machine-generated"], "language_creators": ["found"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]} | false | False | 2026-03-07T13:06:45 | 14 | 11 | false | 8f0343a5613879fefda0eb002d10ff7150a2c588 |
Qwen3.5 Tool Calling Dataset v2
An expanded tool-calling SFT dataset combining smirki/Tool-Calling-Dataset-UIGEN-X and AmanPriyanshu/tool-reasoning-sft-jupyter-agent, unified into Qwen3 messages format. Adds Jupyter notebook agent data with code execution reasoning chains.
Dataset Summary
Property
Value
Total Samples
~60K+
Train Split
~55K
Test Split
~6K
Sources
UIGEN-X + Jupyter Agent
Format
Qwen3 messages
Language
English
License
Apache 2.0… See the full description on the dataset page: https://huggingface.co/datasets/Mustafaege/qwen3.5-toolcalling-v2. | 176 | 176 | [
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"library:datasets",
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"library:polars",
"library:mlcroissant",
"region:us... | 2026-03-06T23:28:44 | null | null |
69b208be3fadc91fa277f593 | TeichAI/Claude-Opus-Dataclaw-Unredacted | TeichAI | {"language": ["en"], "license": "mit", "task_categories": ["text-generation"]} | false | False | 2026-03-14T05:56:04 | 11 | 11 | false | db4502e0401409d288b996ae48fe7704dfb90c51 |
Dataclaw Opus (4.5 & 4.6) Dataset
This dataset was assembled by:
Collecting all Dataclaw datasets we could find
Filtering for Opus-family conversations
Normalizing them into a single training format
Deduplicating overlapping uploads
Used Gemini 3 Flash to replace all [REDACTED] content with made up details, prompts, secrets, etc. (given the context of the entire conversation)
Rebuilding the retained rows into a validated OpenAI function-calling chat format with structured… See the full description on the dataset page: https://huggingface.co/datasets/TeichAI/Claude-Opus-Dataclaw-Unredacted. | 80 | 80 | [
"task_categories:text-generation",
"language:en",
"license:mit",
"region:us"
] | 2026-03-12T00:28:46 | null | null |
67a404bc8c6d42c5ec097433 | Anthropic/EconomicIndex | Anthropic | {"language": "en", "pretty_name": "EconomicIndex", "tags": ["AI", "LLM", "Economic Impacts", "Anthropic"], "viewer": true, "license": "mit", "configs": [{"config_name": "release_2026_01_15", "data_files": [{"split": "raw_claude_ai", "path": "release_2026_01_15/data/intermediate/aei_raw_claude_ai_2025-11-13_to_2025-11-20.csv"}, {"split": "raw_1p_api", "path": "release_2025_09_15/data/intermediate/aei_raw_1p_api_2025-11-13_to_2025-11-20.csv"}]}]} | false | False | 2026-03-11T05:02:11 | 475 | 10 | false | d1001170819fe03262c168fcf77ae99a5abf9576 |
The Anthropic Economic Index
Overview
The Anthropic Economic Index provides insights into how AI is being incorporated into real-world tasks across the modern economy.
Data Releases
This repository contains multiple data releases, each with its own documentation:
Labor market impacts: Job exposure and task penetration data
2026-01-15 Release: Updated analysis with economic primitives and Sonnet 4.5
2025-09-15 Release: Updated analysis with geographic and… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/EconomicIndex. | 12,541 | 60,263 | [
"language:en",
"license:mit",
"arxiv:2503.04761",
"region:us",
"AI",
"LLM",
"Economic Impacts",
"Anthropic"
] | 2025-02-06T00:39:24 | null | null |
6997f5d1260ef062721a6a13 | togethercomputer/CoderForge-Preview | togethercomputer | {"dataset_info": [{"config_name": "trajectories", "features": [{"name": "trajectory_id", "dtype": "string"}, {"name": "finish_reason", "dtype": "string"}, {"name": "image", "dtype": "string"}, {"name": "messages", "dtype": "string"}, {"name": "reward", "dtype": "float64"}, {"name": "tools", "dtype": "string"}, {"name": "license", "dtype": "string"}], "splits": [{"name": "SWE_Rebench", "num_bytes": 19392208677, "num_examples": 77169}, {"name": "SWE_Smith", "num_bytes": 33088967556, "num_examples": 148001}, {"name": "R2E_Gym", "num_bytes": 6869123922, "num_examples": 32964}, {"name": "filtered_reward1", "num_bytes": 33547502194, "num_examples": 155144}], "download_size": 22788997561, "dataset_size": 92897802349}, {"config_name": "trajectories-tokenized_qwencoder", "features": [{"name": "trajectory_id", "dtype": "string"}, {"name": "reward", "dtype": "float64"}, {"name": "chat_template_applied", "dtype": "string"}, {"name": "input_ids", "list": "int32"}, {"name": "labels", "list": "int64"}], "splits": [{"name": "SWE_Rebench", "num_bytes": 64238782798, "num_examples": 77169}, {"name": "SWE_Smith", "num_bytes": 107118447512, "num_examples": 148001}, {"name": "R2E_Gym", "num_bytes": 23869485518, "num_examples": 32964}, {"name": "filtered_reward1", "num_bytes": 108349044091, "num_examples": 155144}], "download_size": 49985669802, "dataset_size": 303575759919}], "configs": [{"config_name": "trajectories", "data_files": [{"split": "SWE_Rebench", "path": "trajectories/SWE_Rebench-*"}, {"split": "SWE_Smith", "path": "trajectories/SWE_Smith-*"}, {"split": "R2E_Gym", "path": "trajectories/R2E_Gym-*"}, {"split": "filtered_reward1", "path": "trajectories/filtered_reward1-*"}]}, {"config_name": "trajectories-tokenized_qwencoder", "data_files": [{"split": "SWE_Rebench", "path": "trajectories-tokenized_qwencoder/SWE_Rebench-*"}, {"split": "SWE_Smith", "path": "trajectories-tokenized_qwencoder/SWE_Smith-*"}, {"split": "R2E_Gym", "path": "trajectories-tokenized_qwencoder/R2E_Gym-*"}, {"split": "filtered_reward1", "path": "trajectories-tokenized_qwencoder/filtered_reward1-*"}]}]} | false | False | 2026-02-26T18:22:08 | 147 | 10 | false | 060fca96cf723b2ebab3181e9e59fafd273df3cb |
CoderForge-Preview: SOTA Open Dataset for Training Efficient Agents
CoderForge-Preview is the largest open test-verified coding agent dataset.
Fine-tuning Qwen-3 32B on it, we boost SWE-Bench Verified performance 23.0% → 59.4% pass@1 and rank #1 among open-data and #2 among open-weight models ≤32B parameters.
Limitations
Adaptability to different scaffolds: We generated all trajectories using a single scaffold and fixed tool set (no permutations). Models trained via… See the full description on the dataset page: https://huggingface.co/datasets/togethercomputer/CoderForge-Preview. | 11,535 | 11,535 | [
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] | 2026-02-20T05:49:05 | null | null |
69a6dc61541e5c55e792dcb6 | ai-coustics/dawn_chorus_en | ai-coustics | {"license": "cc-by-nc-4.0", "task_categories": ["audio-to-audio"], "language": ["en"], "tags": ["speech", "foreground-background-speech", "speech-to-text"], "pretty_name": "dawn_chorus_en", "size_categories": ["n<1K"], "configs": [{"config_name": "default", "data_files": [{"split": "eval", "path": "eval.parquet"}]}], "dataset_info": {"features": [{"name": "mix", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "speech", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcript", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "conversation_type", "dtype": "string"}, {"name": "speech_source", "dtype": "string"}, {"name": "index", "dtype": "int64"}]}} | false | False | 2026-03-03T13:06:55 | 10 | 10 | false | 3c21347c1e61ea904a493f9a6b3856161432da80 |
dawn_chorus_en
An open-source evaluation dataset for accurate foreground speaker transcription.
The dataset targets mixture conditions where foreground speech remains generally transcribable by speech-to-text systems, while background speech is distinctly perceived as background. It provides around 90 minutes of foreground–background speech mixtures composed of recorded and synthesized foreground speech, along with ground truth foreground speech and corresponding transcripts.… See the full description on the dataset page: https://huggingface.co/datasets/ai-coustics/dawn_chorus_en. | 658 | 658 | [
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69a52fb3ff95a38fe27d886f | TianHongZXY/CHIMERA | TianHongZXY | {"language": ["en"], "pretty_name": "CHIMERA", "tags": ["reasoning", "chain-of-thought", "synthetic-data", "llm", "stem", "post-training"], "license": "apache-2.0", "task_categories": ["text-generation", "question-answering"], "size_categories": ["1K<n<10K"], "annotations_creators": ["machine-generated"], "configs": [{"config_name": "Qwen3-235B-2507", "default": true, "data_files": [{"split": "train", "path": "Qwen3-235B-2507/train-*.parquet"}]}, {"config_name": "Qwen3.5-397B", "data_files": [{"split": "train", "path": "Qwen3.5-397B/train-*.parquet"}]}]} | false | False | 2026-03-11T04:38:56 | 17 | 9 | false | d6a22de2d5a51eb8f1ac1edd6ffde4d791bd0f65 |
CHIMERA: Compact Synthetic Data for Generalizable LLM Reasoning
CHIMERA is a compact but high-difficulty synthetic reasoning datasetwith long Chain-of-Thought (CoT) trajectories and broad STEM coverage, designed for reasoning post-training. All examples are fully LLM-generated and automatically verified without human annotation.
Total: 9,225 problems
Subjects: 8
Topics: 1,179
🔥 Why CHIMERA?
Recent reasoning advances rely heavily on high-quality… See the full description on the dataset page: https://huggingface.co/datasets/TianHongZXY/CHIMERA. | 874 | 874 | [
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678bd1db320331c7e0499ec7 | nomic-ai/nomic-embed-unsupervised-data | nomic-ai | {"language": ["en"], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "document", "dtype": "string"}, {"name": "dataset", "dtype": "string"}, {"name": "shard", "dtype": "int64"}], "splits": [{"name": "reddit_title_body", "num_bytes": 133556530576.56786, "num_examples": 66204599}, {"name": "amazon_reviews", "num_bytes": 79397795801.44087, "num_examples": 39357860}, {"name": "paq", "num_bytes": 108682741460.16927, "num_examples": 53874545}, {"name": "s2orc_citation_titles", "num_bytes": 15578276961.267248, "num_examples": 7722225}, {"name": "s2orc_title_abstract", "num_bytes": 72727941660.31642, "num_examples": 36051582}, {"name": "s2orc_abstract_citation", "num_bytes": 15412180087.166075, "num_examples": 7639890}, {"name": "s2orc_abstract_body", "num_bytes": 13214381649.546701, "num_examples": 6550431}, {"name": "wikianswers", "num_bytes": 20349823474.661026, "num_examples": 10087503}, {"name": "wikipedia", "num_bytes": 12503510832.888903, "num_examples": 6198049}, {"name": "gooaq", "num_bytes": 2584478254.5968294, "num_examples": 1281138}, {"name": "codesearch", "num_bytes": 1743019608.3259697, "num_examples": 864023}, {"name": "yahoo_title_answer", "num_bytes": 558247690.3202951, "num_examples": 276726}, {"name": "agnews", "num_bytes": 847859634.6904019, "num_examples": 420288}, {"name": "amazonqa", "num_bytes": 456192977.6962069, "num_examples": 226137}, {"name": "yahoo_qa", "num_bytes": 289440471.31127894, "num_examples": 143477}, {"name": "yahoo_title_question", "num_bytes": 430336857.75505495, "num_examples": 213320}, {"name": "ccnews", "num_bytes": 713469137.831569, "num_examples": 353670}, {"name": "npr", "num_bytes": 736476787.666073, "num_examples": 365075}, {"name": "eli5", "num_bytes": 215412525.82009435, "num_examples": 106781}, {"name": "cnn", "num_bytes": 592128749.4145954, "num_examples": 293521}, {"name": "stackexchange_duplicate_questions", "num_bytes": 147688736.90346697, "num_examples": 73210}, {"name": "stackexchange_title_body", "num_bytes": 162788452.73084643, "num_examples": 80695}, {"name": "stackexchange_body_body", "num_bytes": 132516397.19234861, "num_examples": 65689}, {"name": "sentence_compression", "num_bytes": 350216575.3502183, "num_examples": 173604}, {"name": "wikihow", "num_bytes": 193722192.5434098, "num_examples": 96029}, {"name": "altlex", "num_bytes": 223334581.13794592, "num_examples": 110708}, {"name": "quora", "num_bytes": 90547861.71168031, "num_examples": 44885}, {"name": "simplewiki", "num_bytes": 197127445.7587226, "num_examples": 97717}, {"name": "squad", "num_bytes": 50669280.21860921, "num_examples": 25117}], "download_size": 261162378852, "dataset_size": 482138856722.99994}, "configs": [{"config_name": "default", "data_files": [{"split": "reddit_title_body", "path": "data/reddit_title_body-*"}, {"split": "amazon_reviews", "path": "data/amazon_reviews-*"}, {"split": "paq", "path": "data/paq-*"}, {"split": "s2orc_citation_titles", "path": "data/s2orc_citation_titles-*"}, {"split": "s2orc_title_abstract", "path": "data/s2orc_title_abstract-*"}, {"split": "s2orc_abstract_citation", "path": "data/s2orc_abstract_citation-*"}, {"split": "s2orc_abstract_body", "path": "data/s2orc_abstract_body-*"}, {"split": "wikianswers", "path": "data/wikianswers-*"}, {"split": "wikipedia", "path": "data/wikipedia-*"}, {"split": "gooaq", "path": "data/gooaq-*"}, {"split": "codesearch", "path": "data/codesearch-*"}, {"split": "yahoo_title_answer", "path": "data/yahoo_title_answer-*"}, {"split": "agnews", "path": "data/agnews-*"}, {"split": "amazonqa", "path": "data/amazonqa-*"}, {"split": "yahoo_qa", "path": "data/yahoo_qa-*"}, {"split": "yahoo_title_question", "path": "data/yahoo_title_question-*"}, {"split": "ccnews", "path": "data/ccnews-*"}, {"split": "npr", "path": "data/npr-*"}, {"split": "eli5", "path": "data/eli5-*"}, {"split": "cnn", "path": "data/cnn-*"}, {"split": "stackexchange_duplicate_questions", "path": "data/stackexchange_duplicate_questions-*"}, {"split": "stackexchange_title_body", "path": "data/stackexchange_title_body-*"}, {"split": "stackexchange_body_body", "path": "data/stackexchange_body_body-*"}, {"split": "sentence_compression", "path": "data/sentence_compression-*"}, {"split": "wikihow", "path": "data/wikihow-*"}, {"split": "altlex", "path": "data/altlex-*"}, {"split": "quora", "path": "data/quora-*"}, {"split": "simplewiki", "path": "data/simplewiki-*"}, {"split": "squad", "path": "data/squad-*"}]}]} | false | False | 2025-01-24T22:02:10 | 15 | 8 | false | 917bae6ed30ebc80fc8c81ba8e3e34558205d6bb | Weakly Supervised Contrastive Training data for Text Embedding models used in Nomic Embed models
Training
Click the Nomic Atlas map below to visualize a 5M sample of our contrastive pretraining data!
We train our embedder using a multi-stage training pipeline. Starting from a long-context BERT model,
the first unsupervised contrastive stage trains on a dataset generated from weakly related text pairs, such as question-answer pairs from forums like StackExchange and Quora… See the full description on the dataset page: https://huggingface.co/datasets/nomic-ai/nomic-embed-unsupervised-data. | 1,445 | 45,144 | [
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] | 2025-01-18T16:07:55 | null | null |
698dd2570db46090757245bc | markov-ai/computer-use | markov-ai | {"license": "apache-2.0", "task_categories": ["robotics", "image-to-text"], "tags": ["computer-use", "gui-agent", "osworld", "trajectories", "reinforcement-learning"], "size_categories": ["n<1K"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*.parquet"}]}]} | false | False | 2026-02-13T15:11:21 | 59 | 8 | false | de58c88b4b33dd03fa4d5d0f490748f576bd37b3 |
Computer Use Trajectories
Successful computer-use agent trajectories collected on OSWorld tasks.
Dataset Details
Rows: 160 (one per task trajectory)
Steps: 1,378 total across all trajectories (avg ~8.6 steps/task)
Agent: Gemini 3 Flash Preview with linearized accessibility-tree grounding
Score filter: Only trajectories with score = 1.0 (fully successful)
Domains
Domain
Tasks
Description
chrome
21
Web browsing tasks in Google Chrome
gimp
15
Image… See the full description on the dataset page: https://huggingface.co/datasets/markov-ai/computer-use. | 892 | 921 | [
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"librar... | 2026-02-12T13:15:03 | null | null |
6996a0f665f352f44ec11a37 | Roman1111111/gemini-3-pro-10000x-hard-high-reasoning | Roman1111111 | {"license": "mit", "task_categories": ["question-answering", "text-generation", "reasoning"], "tags": ["code", "finance", "legal", "agent", "chemistry", "art", "synthetic", "gemini-3-pro", "hard-reasoning", "mathematics", "physics"], "size_categories": ["10K<n<100K"], "language": ["en"]} | false | False | 2026-02-20T03:49:27 | 42 | 8 | false | 5feedf31aaa6ff0ae0ee1bc8a169bc6bfaccbd5a |
Dataset Card for Gemini-3-Pro-Reasoning-10000x-high-reasoning
Dataset Details
Dataset Description
Suggestion: I would use it to fine tune glm- 4.7-flash, or other 30b moe models, but 2-20b llms work perfectly, you can fine tune Nanbeige 4.1 - 3b, gpt-oss:20b, or qwen3: 4b, 8b(note: better to fine tune newest versions(2507 4b qwen3 , or qwen 3 vl:8b)) for maximum improvement.
This dataset is a high-complexity synthetic reasoning corpus containing… See the full description on the dataset page: https://huggingface.co/datasets/Roman1111111/gemini-3-pro-10000x-hard-high-reasoning. | 972 | 972 | [
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"code",
"finance",
"legal",
"... | 2026-02-19T05:34:46 | null | null |
69a0413735be92a8b511584c | AweAI-Team/Scale-SWE | AweAI-Team | null | false | False | 2026-03-05T04:50:13 | 34 | 8 | false | d8db20390a936bbda9c96d88b97cc4778dff1481 |
Immersion in the GitHub Universe: Scaling Coding Agents to Mastery
🔥 Highlights
Source from 6M+ pull requests and 23000+ repositories.
Cover 5200 Repositories.
100k high-quality instances.
71k trajectories from DeepSeek v3.2 with 3.5B token.
Strong performance: 64% in SWE-bench-Verified trained from Qwen3-30A3B-Instruct.
📣 News
2026-02-26 🚀 We released a portion of our data on Hugging Face. This release includes 20,000 SWE task… See the full description on the dataset page: https://huggingface.co/datasets/AweAI-Team/Scale-SWE. | 2,259 | 2,259 | [
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"arxiv:2602.09892",
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] | 2026-02-26T12:48:55 | null | null |
69ab52d621553879afae74f5 | nvidia/Retrieval-Synthetic-NVDocs-v1 | nvidia | {"license": "cc-by-4.0", "task_categories": ["question-answering", "text-retrieval", "text-ranking", "text-classification"], "language": ["en"], "size_categories": ["100K<n<1M"]} | false | False | 2026-03-11T15:23:09 | 12 | 8 | false | bacc762bd9c80de32302f472e414b1aa9547507c |
Dataset Description:
Retrieval-Synthetic-NVDocs-v1 is a synthetic retrieval dataset with question–answer supervision designed to train and evaluate embedding and RAG systems. The dataset was generated on top of NVIDIA's publicly available content using NeMo Data Designer, NVIDIA's open-source framework for generating high-quality synthetic data from scratch or based on seed data.
The dataset contains document chunks paired with semantically rich question-answer pairs across multiple… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Retrieval-Synthetic-NVDocs-v1. | 398 | 398 | [
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"l... | 2026-03-06T22:19:02 | 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 | 2026-03-14T03:49:57 | 9,614 | 7 | false | 9223977493bbbb576ed57473cfa7e192ff58e074 |
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/awesome-chatgpt-prompts
About
prompts.chat is an open-source platform where users can share, discover, and collect AI prompts from the community. The project can be… See the full description on the dataset page: https://huggingface.co/datasets/fka/prompts.chat. | 23,229 | 471,177 | [
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"ChatGPT",
"prompts",
"AI",
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"Claude"... | 2022-12-13T23:47:45 | null | null |
663b7fd5a4152b77b637ba11 | TIGER-Lab/MMLU-Pro | TIGER-Lab | {"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering"], "pretty_name": "MMLU-Pro", "tags": ["evaluation"], "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "options", "sequence": "string"}, {"name": "answer", "dtype": "string"}, {"name": "answer_index", "dtype": "int64"}, {"name": "cot_content", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "src", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 61242, "num_examples": 70}, {"name": "test", "num_bytes": 8714663, "num_examples": 12032}], "download_size": 121157475, "dataset_size": 8775905}} | false | False | 2026-03-11T10:56:33 | 449 | 7 | false | 54611cde22c74cca43dd78732198de6abe971398 |
MMLU-Pro Dataset
MMLU-Pro dataset is a more robust and challenging massive multi-task understanding dataset tailored to more rigorously benchmark large language models' capabilities. This dataset contains 12K complex questions across various disciplines.
|Github | 🏆Leaderboard | 📖Paper |
🚀 What's New
[2026.03.11] Added more cutting-edge frontier models to the leaderboard, including the Claude-4.6 series, Seed2.0 series, Qwen3.5 series, and Gemini-3.1-Pro, among… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro. | 123,588 | 1,300,586 | [
"benchmark:official",
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"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2406.01574",
"doi:10.57967/hf... | 2024-05-08T13:36:21 | null | null |
6835e8703de5738a2e9af4ae | nvidia/PhysicalAI-Autonomous-Vehicles | nvidia | {"extra_gated_heading": "You must agree to the NVIDIA Autonomous Vehicle Dataset License Agreement to access this dataset.", "extra_gated_prompt": "### NVIDIA Autonomous Vehicle Dataset License Agreement\n\nThis NVIDIA Autonomous Vehicle Dataset License Agreement (\"Agreement\") is a legal agreement between you, whether an individual or entity (\"you\") and NVIDIA Corporation with address 2788 San Tomas Expressway, Santa Clara, California 95051 (\"NVIDIA\") and governs the use of certain datasets, including any annotations and metadata attached to the datasets, provided by NVIDIA (\"Dataset\").\n\nThis Agreement can be accepted only by an adult of legal age of majority in the country in which the Dataset are used.\n\nIf you don't have the required age or authority to accept this Agreement or if you don't accept all the terms and conditions of this Agreement, do not use the Dataset.\n\nYou agree to use the Dataset only for purposes expressly permitted by this Agreement and in accordance with any applicable law or regulation in the relevant jurisdictions.\n\n1. License Grant. Subject to the terms of this Agreement, NVIDIA grants you a non-exclusive, revocable, non-transferable, non-sublicensable (except as expressly granted in Sections 1 and 2 of this Agreement, license to download, use, modify, and reproduce the Dataset, in each case solely for your internal development of autonomous vehicles and automated driving assisted systems using NVIDIA technology (\"Purpose\"). NVIDIA may from time to time update the Dataset. If requested by NVIDIA, you will use the updated version of any such Dataset and delete any prior versions upon NVIDIA's written request.\n\n2. Authorized Users. You may allow your Affiliates' employees and contractors (all such users collectively \"Authorized Users\") to access and use the Dataset from your secure network for the Purpose on your behalf. You are responsible for the compliance with the terms of this Agreement by your authorized users. Any act or omission by your authorized users that if committed by you would constitute a breach of this Agreement will be deemed to constitute a breach of this Agreement. \"Affiliates\" means an entity that owns or controls, is owned or controlled by, or is under common ownership or control with you, where \"control\" is the possession, directly or indirectly, of the power to direct or cause the direction of the management and policies of an entity, whether through ownership of voting securities, by contract or otherwise.\n\n3. Confidentiality. You agree that you will not use, nor authorize others to use, NVIDIA Confidential Information, other than for the Purpose, and that you will not disclose NVIDIA Confidential Information to any third party, except to Authorized Users under this Agreement that have a need to know such Confidential Information for the Purpose, provided that each such recipient is subject to a written agreement that includes confidentiality obligations consistent with the terms. You will protect the NVIDIA Confidential Information with at least the same degree of care that you use to protect your own similar confidential and proprietary information, but no less than a reasonable degree of care, including any appropriate technical, organizational and contractual measures. \"Confidential Information\" means the Dataset including its features and functionality, output, and any results of benchmarking or other competitive analysis or regression or performance data relating to the Dataset.\n\n4. Limitations. Your license to use the Dataset is restricted as follows:\n\n4.1 You will not use the Dataset for the purpose of any surveillance program, service and/or product of public authorities, corporations and/or citizens that monitors the behavior of an individual person or groups of persons in any unethical manner. You will not use the Dataset to directly or indirectly enable law enforcement or any public authority to enforce any rules or regulations including any road traffic laws.\n\n4.2 You may not change or remove copyright or other proprietary notices in the Dataset.\n\n4.3 The rights granted to you in Section 1 and 2 are for the Purpose only. You may not use the Dataset for any other purpose.\n\n4.4 You may not identify or attempt to identify or profile any individual (including by way of license plate numbers) in the Dataset or de-anonymize or attempt to de-anonymize any Dataset. This includes prohibition against processing of license plate numbers for purpose of tracking or collecting data about a vehicle over time and across different frames.\n\n4.5 You may not: (a) infer, measure, detect or otherwise label the race, ethnicity, gender, age or health (or any other sensitive attributes) of individuals in the Dataset, (b) perform biometric processing of the Dataset, (c) analyze faces, gazes, eye movements, gait, or body movements to uniquely identify persons, or (d) use the Dataset to develop or evaluate any identity, emotion recognition technology or social scoring technology.\n\n4.6 You may not create derivative works of the Dataset, sell, rent, sublicense, transfer, distribute, embed, or host the Dataset (in whole or in part), or otherwise make the Dataset (in whole or in part) available to others.\n\n4.7 You may not bypass, disable or circumvent any technical limitation, encryption, security, digital rights management or authentication mechanism relating to the Dataset.\n\n4.8 You must keep track of any copies of the Dataset. You will keep track of where the Dataset or portions of it are stored to ensure these restrictions follow such Dataset.\n\n4.9 While NVIDIA has exercised reasonable efforts to anonymize the Dataset, you must cooperate with NVIDIA to honor any data subject rights where applicable. You will delete the Dataset upon written notice by NVIDIA and you will promptly notify NVIDIA at https://www.nvidia.com/en-us/support/submit-security-vulnerability/ if you notice that any portion of the Dataset is not sufficiently anonymized.\n\n5. AI Ethics.\n\n5.1 Ethical Use. NVIDIA is committed to safety, trust and transparency in AI development. NVIDIA encourages you to (a) ensure that the product or service you develop, use, offer as a service or distribute meets the legal and ethical requirements of the relevant industry or use case, (b) take reasonable measures to address unintended bias and to mitigate harm to others, including underrepresented or vulnerable groups, and (c) inform users of the nature and limitations of the product or service.\n\n5.2 Prohibited Uses. NVIDIA expressly prohibits the use of its products or services for any purpose in violation of applicable law or regulation, including but not limited to (a) illegal surveillance, (b) illegal collection or processing of biometric information without the consent of the subject where required under applicable law, or (c) illegal harassment, abuse, threatening or bullying of individuals or groups of individuals or intentionally misleading or deceiving others.\n\n6. Ownership. The Dataset, including all intellectual property rights, is and will remain the sole and exclusive property of NVIDIA or its licensors. Except as expressly granted in this Agreement, (i) NVIDIA reserves all rights, interests and remedies in connection with the Dataset, and (ii) no other license or right is granted to you by implication, estoppel or otherwise.\n\n7. Feedback. You may, but are not obligated to, provide suggestions, requests, fixes, modifications, enhancements, or other feedback regarding or in connection with your use of the Dataset (collectively, \"Feedback\"). Feedback, even if designated as confidential by you, will not create any confidentiality obligation for NVIDIA or its affiliates. If you provide Feedback, you hereby grant NVIDIA, its affiliates and its designees a nonexclusive, perpetual, irrevocable, sublicensable, worldwide, royalty-free, fully paid-up and transferable license, under your intellectual property rights, to publicly perform, publicly display, reproduce, use, make, have made, sell, offer for sale, distribute (through multiple tiers of distribution), import, create derivative works of and otherwise commercialize and exploit the Feedback at NVIDIA's discretion.\n\n8. Term and Termination. This Agreement expires twelve (12) months after the date of initial delivery or download of the Dataset. This Agreement will automatically terminate (a) if you fail to comply with any of the terms in this Agreement or (b) if you commence or participate in any legal proceeding against NVIDIA with respect to the Dataset. Upon termination, you must stop using and destroy all copies of the Dataset. Upon written request, you will certify in writing that you have complied with your commitments under this section. All provisions will survive termination, except for the licenses granted to you.\n\n9. Disclaimer of Warranties. THE DATASET IS PROVIDED BY NVIDIA AS-IS AND WITH ALL FAULTS. TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, NVIDIA DISCLAIMS ALL WARRANTIES AND REPRESENTATIONS OF ANY KIND, WHETHER EXPRESS, IMPLIED OR STATUTORY, RELATING TO OR ARISING UNDER THIS AGREEMENT, INCLUDING, WITHOUT LIMITATION, THE WARRANTIES OF TITLE, NONINFRINGEMENT, MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, USAGE OF TRADE AND COURSE OF DEALING.\n\n10. Limitations of Liability. TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE, WILL NVIDIA BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES OF ANY TYPE ARISING OUT OF OR AS A RESULT OF THIS AGREEMENT OR THE USE OR INABILITY TO USE THE SOFTWARE (INCLUDING BUT NOT LIMITED TO DAMAGES FOR LOSS OF GOODWILL, WORK STOPPAGE, COMPUTER FAILURE OR MALFUNCTION, OR ANY AND ALL OTHER DAMAGES OR LOSSES), EVEN IF NVIDIA HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.\n\n11. Governing Law and Jurisdiction. This Agreement will be governed in all respects by the laws of the United States and the laws of the State of Delaware, without regard to conflict of laws principles or the United Nations Convention on Contracts for the International Sale of Goods. The state and federal courts residing in Santa Clara County, California will have exclusive jurisdiction over any dispute or claim arising out of or related to this Agreement, and the parties irrevocably consent to personal jurisdiction and venue in those courts; except that either party may apply for injunctive remedies or an equivalent type of urgent legal relief in any jurisdiction.\n\n12. Indemnity. You agree to defend, indemnify and hold harmless NVIDIA and its affiliates, and their respective employees, contractors, agents, officers and directors, from and against any and all claims, damages, obligations, losses, liabilities, costs or debt, fines, restitutions and expenses (including but not limited to attorney's fees and costs incident to establishing the right of indemnification) arising out of or related to your use of the Dataset outside of the scope of this Agreement, or not in compliance with its terms.\n\n13. General.\n13.1 No Assignment. NVIDIA may assign, delegate or transfer its rights or obligations under this Agreement by any means or operation of law. You may not, without NVIDIA's prior written consent, assign, delegate or transfer any of your rights or obligations under this Agreement by any means or operation of law, and any attempt to do so is null and void.\n\n13.2 No Waiver. No waiver of any term of the Agreement will be deemed a further or continuing waiver of such term or any other term, and NVIDIA's failure to assert any right or provision under the Agreement will not constitute a waiver of such right or provision.\n\n13.3 Trade and Compliance. You agree to with all applicable export, import, trade and economic sanctions laws and regulations, as amended, including without limitation U.S. Export Administration Regulations and Office of Foreign Assets Control regulations. Any violation of such laws by you will void any warranty for the associated products and technologies. You confirm (a) your understanding that export or reexport of certain NVIDIA products or technologies may require a license or other approval from appropriate authorities and (b) that you will not export or reexport any products or technology, directly or indirectly, without first obtaining any required license or other approval from appropriate authorities, (i) to any countries that are subject to any U.S. or local export restrictions (currently including, but not necessarily limited to, Belarus, Cuba, Iran, North Korea, Russia, Syria, the Region of Crimea, Donetsk People's Republic Region and Luhansk People's Republic Region); (ii) to any end-user who it knows or has reason to know will utilize them in the design, development or production of nuclear, chemical or biological weapons, missiles, rocket systems, unmanned air vehicles capable of a maximum range of at least 300 kilometers, regardless of payload, or intended for military end-use, or any weapons of mass destruction; (iii) to any end-user who has been prohibited from participating in the U.S. or local export transactions by any governing authority; or (iv) to any known military or military-intelligence end-user or for any known military or military-intelligence end-use in accordance with U.S. trade compliance laws and regulations..\n\n13.4 Notices. Please direct your legal notices or other correspondence to NVIDIA Corporation, 2788 San Tomas Expressway, Santa Clara, California 95051, United States of America, Attention: Legal Department, with a copy emailed to legalnotices@nvidia.com. If NVIDIA needs to contact you about the Dataset, you consent to receive the notices by email and agree that such notices will satisfy any legal communication requirements.\n\n13.5 Force Majeure. Neither party will be liable during any period where an event or circumstance prevents or delays that party from performing its obligations under this Agreement and that event or circumstance: (i) is not within the reasonable control of that party and is not the result of that party's negligence, and (ii) cannot be overcome or avoided by that party using reasonably diligent efforts.\n\n13.6 Severability and Amendment. If a court of competent jurisdiction rules that a provision of this Agreement is unenforceable, that provision will be deemed modified to the extent necessary to make it enforceable and the remainder of this Agreement will continue in full force and effect. Any amendment to this Agreement must be in writing and signed by authorized representatives of both parties.\n\n13.7 Independent Contractors. The parties are independent contractors, and this Agreement does not create a joint venture, partnership, agency or other form of business association between the parties. Neither party will have the power to bind the other party or incur any obligation on its behalf without the other party's prior written consent.\n\n13.8 Construction. The headings in the Agreement are included solely for convenience and are not intended to affect the meaning or interpretation of the Agreement. As required by the context of the Agreement, the singular of a term includes the plural and vice versa.\n\n13.9 Entire Agreement. Regarding the subject matter of this Agreement, the parties agree that (i) this Agreement constitutes the entire and exclusive agreement between the parties and supersedes all prior and contemporaneous communications and (ii) any additional or different terms or conditions, whether contained in purchase orders, order acknowledgments, invoices or otherwise, will not be binding and are null and void.", "extra_gated_button_content": "I accept the terms of the NVIDIA Autonomous Vehicle Dataset License Agreement", "license": "other", "license_name": "nvidia-av-dataset", "license_link": "https://huggingface.co/datasets/nvidia/PhysicalAI-Autonomous-Vehicles/blob/main/LICENSE.pdf", "viewer": false} | false | auto | 2026-03-13T22:04:06 | 775 | 7 | false | 37a7cc2c868d684d0456b5412a7ec5d18597a96a |
PHYSICAL AI AUTONOMOUS VEHICLES
The PhysicalAI-Autonomous-Vehicles dataset provides one of the largest, geographically diverse collections of multi-sensor data empowering AV researchers to build the next generation of Physical AI based end-to-end driving systems. This dataset is ready for commercial/non-commercial AV use per the license agreement.
Data Collection Method
Automatic/Sensor
Labeling Method
Automatic/Sensor
This dataset has a total of 1700 hours of driving… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Autonomous-Vehicles. | 231,353 | 1,005,295 | [
"license:other",
"region:us"
] | 2025-05-27T16:29:36 | null | null |
689fcad5cd5c456095ebedae | Brianferrell787/financial-news-multisource | Brianferrell787 | {"pretty_name": "Multi-Source Financial & General News", "language": ["en"], "license": "other", "extra_gated_heading": "Request access to Multi-Source Financial & General News", "extra_gated_description": "This corpus aggregates third-party news for research use. Please acknowledge the terms below.", "extra_gated_prompt": "By requesting access you confirm that:\n\u2022 You will use the data **for non-commercial research/education** only.\n\u2022 You will **respect each original source\u2019s terms** and remove items upon rightsholder request.\n\u2022 You will **cite** this corpus and the original sources in any publication.\n", "extra_gated_fields": {"Name / Email": "text", "How did you hear about this dataset?": {"type": "select", "options": ["Google search", "Hugging Face search", "Word of mouth", "Citation in a paper", "Twitter / X", "Reddit", "Other"]}, "If \"Other\", please specify (optional)": {"type": "text", "required": false}, "Intended use": {"type": "select", "options": ["Research", "Education", {"label": "Other", "value": "other"}]}, "I agree to non-commercial, research-only use": "checkbox", "I will not redistribute article text": "checkbox", "I will cite this corpus and original sources": "checkbox"}, "extra_gated_button_content": "Agree & Request Access", "tags": ["finance", "markets", "trading", "backtesting", "time-series", "news", "headlines", "parquet", "multisource", "llm", "reinforcement-learning", "retrieval", "dataset-card"], "size_categories": ["10M<n<100M"], "task_categories": ["text-classification", "text-retrieval", "other"], "task_ids": ["language-modeling", "document-retrieval", "topic-classification", "news-articles-summarization", "document-question-answering"], "configs": [{"config_name": "data", "data_files": ["data/*/*.parquet"]}]} | false | auto | 2025-11-29T21:31:56 | 66 | 7 | false | c25780f336280adb57c64bda7aed605d065c672d |
Multi-Source Financial & General News
🚀 57.1 MILLION ROWS OF NEWS CONTENT — one unified corpus for market-aware AI/ML
I combined 24 public news datasets (many small on their own) into one consistent, ready-to-use layer so you don’t have to wrangle them yourself. Everything is normalized to a minimal schema (date, text, extra_fields) and shipped as Parquet shards per subset—streamable, DuckDB-friendly, and built with a trading date policy (this can be edited if folks see other use… See the full description on the dataset page: https://huggingface.co/datasets/Brianferrell787/financial-news-multisource. | 2,715 | 13,483 | [
"task_categories:text-classification",
"task_categories:text-retrieval",
"task_categories:other",
"task_ids:language-modeling",
"task_ids:document-retrieval",
"task_ids:topic-classification",
"task_ids:news-articles-summarization",
"task_ids:document-question-answering",
"language:en",
"license:ot... | 2025-08-16T00:03:33 | null | null |
693e2682c9d7af74f71b3e5f | nvidia/Nemotron-Agentic-v1 | nvidia | {"license": "cc-by-4.0", "language": ["en"], "configs": [{"config_name": "default", "data_files": [{"split": "interactive_agent", "path": "data/interactive_agent.jsonl"}, {"split": "tool_calling", "path": "data/tool_calling.jsonl"}]}]} | false | False | 2025-12-15T13:48:35 | 155 | 7 | false | 650d590978ca35c8f1ecea2faf136e5fac421b62 |
Dataset Description:
The Nemotron-Agentic-Tool-Use-v1 dataset is designed to strengthen models’ capabilities as interactive, tool-using agents. It focuses on multi-turn conversations where language models decompose user goals, decide when to call tools, and reason over tool outputs to complete tasks reliably and safely.
This dataset is ready for commercial use.
The Nemotron-Agentic-Tool-Use-v1 dataset contains the following subsets:
Interactive Agent
This dataset… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Agentic-v1. | 877 | 4,579 | [
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2025-12-14T02:52:50 | null | null |
696552c844f950f64be9b539 | openai/ih-challenge | openai | {"license": "apache-2.0"} | false | False | 2026-01-12T21:17:39 | 7 | 7 | false | 056b7d94345dd4f8049da75bd70617d8928ac586 |
IH-Challenge
Training dataset from our paper Large-Scale RLVR Improves Instruction Hierarchy on Frontier LLMs.
Warning About Company Names
To avoid legal and reputational risk, we replaced all company names in the original dataset for either COMPETITOR_i or BRAND_i, with i ∈ ℕ. We recommend that you replace these placeholders with real company names before training on the dataset.
Data Schema
Field
Type
Description
attacker_meta_problem
str
General… See the full description on the dataset page: https://huggingface.co/datasets/openai/ih-challenge. | 94 | 94 | [
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-01-12T20:00:08 | null | null |
69856ff91857b54ca5ef9047 | artillerywu/DeepResearch-9K | artillerywu | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "difficulty", "dtype": "int64"}, {"name": "search trajectory", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "final answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 286413691, "num_examples": 3974}], "download_size": 64975087, "dataset_size": 286413691}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "DeepResearch-Hard/train-*"}]}]} | false | False | 2026-02-21T07:07:56 | 9 | 7 | false | 946870822ebf09b08b4c13a951d7b64da5dfd554 | null | 278 | 283 | [
"size_categories:1K<n<10K",
"format:parquet",
"format:optimized-parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-02-06T04:37:13 | null | null |
6993497cb265036892229930 | OmniLottie/MMLottie-2M | OmniLottie | {"license": "cc-by-nc-sa-4.0", "language": ["en"], "tags": ["lottie", "animation", "vector-graphics", "motion-graphics", "multi-modal"], "size_categories": ["1M<n<10M"], "configs": [{"config_name": "Lottie", "data_files": "data/Lottie/*.parquet"}, {"config_name": "Lottie_SVG", "data_files": "data/Lottie_SVG/*.parquet"}]} | false | False | 2026-03-07T07:45:11 | 28 | 7 | false | b53c3972343cabe94d4f4b1a86433a9c3dc8b298 |
MMLottie-2M Dataset
The first large-scale Lottie animation dataset for multi-modal vector animation generation, containing ~2M samples with diverse motion patterns and visual styles.
Dataset Overview
MMLottie-2M consists of two complementary subsets designed to support comprehensive training for Lottie animation generation:
1. Lottie Subset
Native Lottie animations collected from major online platforms including LottieFiles, IconScout, Flaticon, Iconfont, and… See the full description on the dataset page: https://huggingface.co/datasets/OmniLottie/MMLottie-2M. | 497 | 521 | [
"language:en",
"license:cc-by-nc-sa-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2603.02138",
"region:us",
"lottie",
"animation",
"vector-graphics",
"motion-gra... | 2026-02-16T16:44:44 | null | null |
69ae7132f939066a47e28bb8 | humanlaya-data-lab/OneMillion-Bench | humanlaya-data-lab | {"license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "language": ["en", "zh"], "tags": ["economics_and_finance", "healthcare_and_medicine", "industry", "law", "natural_science"], "pretty_name": "$OneMillion-Bench", "size_categories": ["n<1K"]} | false | False | 2026-03-11T06:34:22 | 7 | 7 | false | 5cf9d5005e2e1f20b4481ed50846161697e82a73 |
$OneMillion-Bench
A bilingual (Global/Chinese) realistic expert-level benchmark for evaluating language agents across 5 professional domains. The benchmark contains 400 entries with detailed, weighted rubric-based grading criteria designed for fine-grained evaluation of domain expertise, analytical reasoning, and instruction following.
Dataset Structure
Each subdirectory is a Hugging Face subset (configuration), and all data is in the test split.
$OneMillion-Bench/
├──… See the full description on the dataset page: https://huggingface.co/datasets/humanlaya-data-lab/OneMillion-Bench. | 197 | 197 | [
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"language:zh",
"license:apache-2.0",
"size_categories:n<1K",
"modality:text",
"arxiv:2603.07980",
"region:us",
"economics_and_finance",
"healthcare_and_medicine",
"industry",
"law",
"natural_science"
] | 2026-03-09T07:05:22 | null | null |
69b186f91cde8c71bb8f76b0 | Roman1111111/claude-opus-4.6-10000x | Roman1111111 | {"license": "mit"} | false | False | 2026-03-11T16:00:39 | 7 | 7 | false | 3fedde0a6ac508eb255151c9d00e5a37e2f3f16a | This is a high-fidelity reasoning dataset synthesized using Claude Opus 4.6. The dataset is designed to capture the model's internal "Chain of Thought" and reasoning traces, specifically focusing on mathematical accuracy and structured logical deduction.
The dataset is intended for Supervised Fine-Tuning (SFT) and Distillation, allowing smaller open-source models to inherit the sophisticated reasoning patterns of Claude Opus 4.6.
Dataset Description
This collection combines high-difficulty… See the full description on the dataset page: https://huggingface.co/datasets/Roman1111111/claude-opus-4.6-10000x. | 319 | 319 | [
"license:mit",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-03-11T15:15:05 | null | null |
621ffdd236468d709f18202d | EdinburghNLP/xsum | EdinburghNLP | {"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "pretty_name": "Extreme Summarization (XSum)", "paperswithcode_id": "xsum", "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["summarization"], "task_ids": ["news-articles-summarization"], "dataset_info": {"features": [{"name": "document", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 479206363, "num_examples": 204045}, {"name": "validation", "num_bytes": 26292877, "num_examples": 11332}, {"name": "test", "num_bytes": 26756141, "num_examples": 11334}], "download_size": 332791351, "dataset_size": 532255381}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "train-eval-index": [{"config": "default", "task": "summarization", "task_id": "summarization", "splits": {"train_split": "train", "eval_split": "test"}, "col_mapping": {"document": "text", "summary": "target"}, "metrics": [{"type": "rouge", "name": "Rouge"}]}]} | false | False | 2026-01-12T14:28:39 | 137 | 6 | false | 7d4d486c2f8ef850b1a11aead99b894ff3dd7da9 |
Dataset Card for "xsum"
Dataset Summary
Extreme Summarization (XSum) Dataset.
There are three features:
document: Input news article.
summary: One sentence summary of the article.
id: BBC ID of the article.
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed
Dataset Structure
Data Instances
default
Size of downloaded dataset files: 257.30 MB
Size of the generated dataset:… See the full description on the dataset page: https://huggingface.co/datasets/EdinburghNLP/xsum. | 15,259 | 1,605,129 | [
"task_categories:summarization",
"task_ids:news-articles-summarization",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"librar... | 2022-03-02T23:29:22 | xsum | null |
639244f571c51c43091df168 | Anthropic/hh-rlhf | Anthropic | {"license": "mit", "tags": ["human-feedback"]} | false | False | 2023-05-26T18:47:34 | 1,674 | 6 | 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 preference (or reward) models for subsequent RLHF training. These data are not meant for supervised training of dialogue agents. Training dialogue agents on these data is likely to lead… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/hh-rlhf. | 20,200 | 1,806,826 | [
"license:mit",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2204.05862",
"region:us",
"human-feedback"
] | 2022-12-08T20:11:33 | null | null |
6662f7cd2b8a3cd48ea74f41 | lmms-lab/Video-MME | lmms-lab | {"dataset_info": {"config_name": "videomme", "features": [{"name": "video_id", "dtype": "string"}, {"name": "duration", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "sub_category", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "videoID", "dtype": "string"}, {"name": "question_id", "dtype": "string"}, {"name": "task_type", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "options", "sequence": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 1003241, "num_examples": 2700}], "download_size": 405167, "dataset_size": 1003241}, "configs": [{"config_name": "videomme", "data_files": [{"split": "test", "path": "videomme/test-*"}]}]} | false | False | 2024-07-04T08:14:20 | 73 | 6 | false | ead1408f75b618502df9a1d8e0950166bf0a2a0b | null | 64,211 | 540,950 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"modality:video",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-06-07T12:06:37 | 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
- Downloads last month
- 8,286
Size of downloaded dataset files:
1.86 GB
Size of the auto-converted Parquet files:
1.86 GB
Number of rows:
4,848,381