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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
698b2c8b4c9e577aa3b1fa16 | nohurry/Opus-4.6-Reasoning-3000x-filtered | nohurry | {"license": "apache-2.0"} | false | False | 2026-02-10T13:06:40 | 356 | 83 | 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.
| 4,856 | 4,917 | [
"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 |
69b50e502b0587383a0e526b | stepfun-ai/Step-3.5-Flash-SFT | stepfun-ai | {"license": ["apache-2.0", "cc-by-nc-2.0"], "pretty_name": "Step-3.5-Flash-SFT", "language": ["multilingual"], "task_categories": ["text-generation"], "tags": ["chat", "sft", "instruction-tuning", "reasoning", "code", "agent"]} | false | False | 2026-03-14T14:22:37 | 73 | 73 | false | c994154a801557540c56af623f31b58c4770c652 |
Step-3.5-Flash-SFT
Step-3.5-Flash-SFT is a general-domain supervised fine-tuning release for chat models.
This repository keeps the full training interface in one place:
json/: canonical raw training data
tokenizers/: tokenizer snapshots for Step-3.5-Flash and Qwen3, released to preserve chat-template alignment
compiled/: tokenizer-specific compiled shards for StepTronOSS training
Data Format
Each raw shard is a JSON file whose top level is a list of examples. Each… See the full description on the dataset page: https://huggingface.co/datasets/stepfun-ai/Step-3.5-Flash-SFT. | 420 | 420 | [
"task_categories:text-generation",
"language:multilingual",
"license:apache-2.0",
"license:cc-by-nc-2.0",
"region:us",
"chat",
"sft",
"instruction-tuning",
"reasoning",
"code",
"agent"
] | 2026-03-14T07:29:20 | 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 | 75 | 68 | 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. | 78,527 | 78,527 | [
"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 |
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 | 2026-03-15T10:57:15 | 63 | 63 | false | 0e8070fd91da79a4e734bfb4c912602c68ce8e45 |
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. | 45,733 | 45,733 | [
"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 |
698e4ad0913c4d1f4a64479a | Crownelius/Opus-4.6-Reasoning-3300x | Crownelius | {"license": "apache-2.0"} | false | False | 2026-03-15T07:02:24 | 168 | 51 | false | 007a7feac2f4960bf59151945b39484d8748c150 |
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. | 2,163 | 2,180 | [
"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 |
6988f3d2dd11cee339d8c40b | karpathy/tinystories-gpt4-clean | karpathy | {"license": "cdla-sharing-1.0"} | false | False | 2026-02-08T21:07:28 | 51 | 44 | 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. | 1,869 | 1,896 | [
"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 |
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 | 175 | 37 | 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. | 12,249 | 12,249 | [
"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 |
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 | 32 | 32 | 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. | 1,514 | 1,514 | [
"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 |
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 | 41 | 30 | 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. | 756 | 756 | [
"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 |
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 | 200 | 29 | 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. | 10,499 | 19,491 | [
"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 |
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 | 28 | 28 | 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. | 1,584 | 1,584 | [
"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 |
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 | 70 | 26 | 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. | 2,009 | 2,070 | [
"task_categories:image-text-to-text",
"language:en",
"license:cc-by-2.0",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2602.13013",
"region:us",
"caption",
"audiovisual",
"instruction-tu... | 2026-02-10T02:44:25 | 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 | 26 | 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. | 360 | 360 | [
"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 |
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 | 83 | 18 | 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. | 10,149 | 38,689 | [
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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 | 17 | 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. | 2,544 | 2,544 | [
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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 | 16 | 16 | 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. | 10,708 | 10,708 | [
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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 | 18 | 15 | false | 915333b4f8b8684f39aeaafea600fea6f43fb703 | null | 27,840 | 27,840 | [
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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 | 15 | 15 | 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. | 959 | 959 | [
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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 | 1,197 | 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. | 607,542 | 9,692,671 | [
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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 | 14 | 14 | 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. | 703 | 703 | [
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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 | 386 | 13 | 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. | 104,901 | 1,442,997 | [
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6655eb19d17e141dcb546ed5 | HuggingFaceFW/fineweb-edu | HuggingFaceFW | {"license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "FineWeb-Edu", "size_categories": ["n>1T"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/*/*"}], "features": [{"name": "text", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "dump", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "file_path", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "language_score", "dtype": "float64"}, {"name": "token_count", "dtype": "int64"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}]}, {"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": 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"data/CC-MAIN-2014-42/*"}]}, {"config_name": "CC-MAIN-2014-41", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-41/*"}]}, {"config_name": "CC-MAIN-2014-35", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-35/*"}]}, {"config_name": "CC-MAIN-2014-23", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-23/*"}]}, {"config_name": "CC-MAIN-2014-15", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-15/*"}]}, {"config_name": "CC-MAIN-2014-10", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-10/*"}]}, {"config_name": "CC-MAIN-2013-48", "data_files": [{"split": "train", "path": "data/CC-MAIN-2013-48/*"}]}, {"config_name": "CC-MAIN-2013-20", "data_files": [{"split": "train", "path": "data/CC-MAIN-2013-20/*"}]}]} | false | False | 2025-07-11T20:16:53 | 990 | 13 | false | 87f09149ef4734204d70ed1d046ddc9ca3f2b8f9 |
📚 FineWeb-Edu
1.3 trillion tokens of the finest educational data the 🌐 web has to offer
Paper: https://arxiv.org/abs/2406.17557
What is it?
📚 FineWeb-Edu dataset consists of 1.3T tokens and 5.4T tokens (FineWeb-Edu-score-2) of educational web pages filtered from 🍷 FineWeb 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. | 221,797 | 6,122,388 | [
"task_categories:text-generation",
"language:en",
"license:odc-by",
"size_categories:1B<n<10B",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2406.17557",
"arxiv:2404.14219",
"arxiv:2401.10020",
... | 2024-05-28T14:32:57 | null | null |
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 | 291 | 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. | 9,888 | 9,888 | [
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"dataclaw",
"claude-code",
"codex-cli",
"conversations",
"coding-assistan... | 2026-02-24T20:20:32 | 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 | 13 | 13 | 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. | 384 | 384 | [
"task_categories:text-generation",
"task_categories:question-answering",
"task_categories:any-to-any",
"language:en",
"license:mit",
"size_categories:1M<n<10M",
"modality:tabular",
"modality:text",
"modality:audio",
"modality:video",
"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 | 13 | 13 | 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. | 119 | 119 | [
"language:de",
"language:en",
"language:es",
"language:cs",
"language:fr",
"language:hu",
"language:it",
"language:nl",
"language:pt",
"language:ru",
"language:sq",
"language:sv",
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:audio",
"modality:text",
... | 2026-03-10T15:37:06 | 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 | 12 | 12 | 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. | 710 | 710 | [
"task_categories:audio-to-audio",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:n<1K",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"speech",
"foreground-background-speech",
"spe... | 2026-03-03T13:04:33 | 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 | 17 | 12 | 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. | 184 | 184 | [
"task_categories:text-generation",
"annotations_creators:machine-generated",
"language_creators:found",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"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 | 2026-03-15T09:26:27 | 12 | 12 | false | e66d16caee4660873b7ea8913d004ca23bde1c02 |
Dataclaw Opus (4.5 & 4.6) Dataset
Currently there are still some major issues in the dataset format (i.e. lack of tools responses, and tool call id's), nothing gemini can't fix. I don't recommend using this set until the update is posted.
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
Using Gemini 3 Flash to replace all the… See the full description on the dataset page: https://huggingface.co/datasets/TeichAI/Claude-Opus-Dataclaw-Unredacted. | 94 | 94 | [
"task_categories:text-generation",
"language:en",
"license:mit",
"region:us"
] | 2026-03-12T00:28:46 | null | null |
6928ac839f54f92be8b78d70 | TeichAI/claude-4.5-opus-high-reasoning-250x | TeichAI | null | false | False | 2025-11-28T03:02:41 | 326 | 11 | 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
| 3,184 | 17,737 | [
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-11-27T19:54:43 | null | 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 | 11 | 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. | 521 | 521 | [
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-02-26T20:26:36 | null | null |
69b0e2310b2ac9d1b7534f7e | nvidia/Nemotron-RL-Super-Training-Blends | nvidia | {"license": "cc-by-4.0", "configs": [{"config_name": "default", "data_files": [{"split": "rlvr1", "path": "rlvr1.jsonl"}, {"split": "rlvr2", "path": "rlvr2.jsonl"}, {"split": "rlvr3", "path": "rlvr3.jsonl"}, {"split": "swe1", "path": "swe1.jsonl"}, {"split": "swe2", "path": "swe2.jsonl"}, {"split": "rlhf", "path": "rlhf.jsonl"}]}]} | false | False | 2026-03-12T00:22:48 | 11 | 11 | false | b90f74f1d0bafeec6d1f1321173f6775ba5bda2e |
Dataset Description:
Nemotron-3-Super-RL-Training-Blends contains the dataset blends used to train the Nemotron-3-Super-120B-A12B model. RL training for the Nemotron-3-Super-120B-A12B model is done in 6 stages: RLVR 1, RLVR 2, RLVR 3, SWE 1, SWE 2, and RLHF. The blends for each stage consist of data from various datasets, which we detail below. The percentages in parentheses indicate the mixing ratios of the dataset components. Note that the model was also trained on additional data… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-Super-Training-Blends. | 415 | 415 | [
"license:cc-by-4.0",
"region:us"
] | 2026-03-11T03:32:01 | null | null |
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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. | 172,797 | 6,449,416 | [
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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 | 477 | 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. | 12,501 | 60,536 | [
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] | 2025-02-06T00:39:24 | null | null |
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 | 16 | 9 | 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. | 1,470 | 45,186 | [
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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 | 9 | 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. | 507 | 507 | [
"task_categories:question-answering",
"task_categories:text-generation",
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"license:mit",
"size_categories:1K<n<10K",
"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 |
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 | 19 | 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. | 906 | 906 | [
"task_categories:text-generation",
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"size_categories:10K<n<100K",
"format:parquet",
"format:optimized-parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:pola... | 2026-03-02T06:35:31 | null | null |
69ae70be8e488f74a57a6010 | DataPilot/AItuber-Personas-Japan | DataPilot | {"license": "odc-by", "language": ["ja"], "tags": ["synthetic"], "pretty_name": "sdg-nexus", "size_categories": ["n<1K"]} | false | False | 2026-03-14T12:28:17 | 9 | 9 | false | d2563a4cc8d5d847c6420959ee6691fc45b97eb8 |
AItuber Persona Dataset
概要
本データセットは、AItuber（AI VTuber）のペルソナ設計に必要な コンセプト設計書・実装用システムプロンプト・配信テーマリスト の3点セットを、LLMを用いて合成的に生成したものです。多様なジャンル・性格・ビジュアルの組み合わせから、即座に実運用可能な品質のAItuberキャラクターデータを提供します。
生成にはSDG-LOOMという合成データ生成パイプラインを用いました。（sdg-loom）
データの説明
項目
内容
件数
195件
形式
JSONL（1行1JSON）
言語
日本語
生成日
2026年3月
ライセンス
odc-by ( Open Data Commons Attribution License )… See the full description on the dataset page: https://huggingface.co/datasets/DataPilot/AItuber-Personas-Japan. | 27 | 27 | [
"language:ja",
"license:odc-by",
"size_categories:n<1K",
"region:us",
"synthetic"
] | 2026-03-09T07:03:26 | null | null |
69b104fbc06491b1f9915fff | KaLM-Embedding/LMEB | KaLM-Embedding | {"license": "mit", "language": ["en"], "tags": ["long-horizon", "memory", "embedding", "benchmark", "openclaw", "lmeb", "mteb"], "size_categories": ["100K<n<1M"], "task_categories": ["feature-extraction"], "modalities": ["Text"], "configs": [{"config_name": "default", "data_files": "Dialogue/LoCoMo/single_hop/queries.jsonl"}], "pretty_name": "LMEB"} | false | False | 2026-03-15T12:20:45 | 9 | 9 | false | f137e843ba4b9439d554a8814647fd9bb62526ee |
🌟 LMEB: Long-horizon Memory Embedding Benchmark 🌟
Welcome to the Long-horizon Memory Embedding Benchmark (LMEB)! Unlike existing text embedding benchmarks that narrowly focus on passage retrieval, LLMEB is designed to evaluate embedding models' ability to handle complex, long-horizon memory retrieval tasks, focusing on fragmented, context-dependent, and temporally distant information. LMEB spans 22 diverse datasets and 193 retrieval tasks, across 4 memory types:
📅 Episodic… See the full description on the dataset page: https://huggingface.co/datasets/KaLM-Embedding/LMEB. | 0 | 0 | [
"task_categories:feature-extraction",
"language:en",
"license:mit",
"size_categories:100K<n<1M",
"region:us",
"long-horizon",
"memory",
"embedding",
"benchmark",
"openclaw",
"lmeb",
"mteb"
] | 2026-03-11T06:00:27 | null | null |
69b186f91cde8c71bb8f76b0 | Roman1111111/claude-opus-4.6-10000x | Roman1111111 | {"license": "mit"} | false | False | 2026-03-11T16:00:39 | 9 | 9 | 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. | 339 | 339 | [
"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 |
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 | 75 | 8 | false | ead1408f75b618502df9a1d8e0950166bf0a2a0b | null | 67,188 | 544,835 | [
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"modality:video",
"library:datasets",
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"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-06-07T12:06:37 | null | null |
67d305619f485955bf117049 | nvidia/HelpSteer3 | nvidia | {"license": "cc-by-4.0", "language": ["en", "zh", "ko", "fr", "es", "ru", "ja", "de", "it", "pt", "pl", "id", "nl", "vi"], "pretty_name": "HelpSteer3", "size_categories": ["10K<n<100K"], "tags": ["human-feedback", "reinforcement-learning"], "configs": [{"config_name": "preference", "default": true, "data_files": [{"split": "train", "path": "preference/train.jsonl.gz"}, {"split": "validation", "path": "preference/validation.jsonl.gz"}]}, {"config_name": "feedback", "data_files": [{"split": "train", "path": "feedback/train.jsonl.gz"}, {"split": "validation", "path": "feedback/validation.jsonl.gz"}]}, {"config_name": "edit", "data_files": [{"split": "train", "path": "edit/train.jsonl.gz"}, {"split": "validation", "path": "edit/validation.jsonl.gz"}]}, {"config_name": "edit_quality", "data_files": [{"split": "train", "path": "edit_quality/train.jsonl.gz"}, {"split": "validation", "path": "edit_quality/validation.jsonl.gz"}]}, {"config_name": "principle", "data_files": [{"split": "train", "path": "principle/train.jsonl.gz"}, {"split": "validation", "path": "principle/validation.jsonl.gz"}]}]} | false | False | 2025-11-16T07:18:00 | 105 | 8 | false | f6d145777bcbde96137596340fab89793acd1031 |
HelpSteer3
HelpSteer3 is an open-source dataset (CC-BY-4.0) that supports aligning models to become more helpful in responding to user prompts.
HelpSteer3-Preference can be used to train Llama 3.3 Nemotron Super 49B v1 (for Generative RMs) and Llama 3.3 70B Instruct Models (for Bradley-Terry RMs) to produce Reward Models that score as high as 85.5% on RM-Bench and 78.6% on JudgeBench, which substantially surpass existing Reward Models on these benchmarks.
HelpSteer3-Feedback and… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/HelpSteer3. | 4,650 | 35,400 | [
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"language:es",
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"language:de",
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"language:pl",
"language:id",
"language:nl",
"language:vi",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:json",
"modali... | 2025-03-13T16:18:41 | null | null |
67d45c3d35fc7f6d2ab224c8 | allenai/olmOCR-bench | allenai | {"license": "odc-by", "tags": ["text"], "configs": [{"config_name": "olmocr-bench", "data_files": [{"split": "arxiv_math", "path": ["bench_data/arxiv_math.jsonl"]}, {"split": "headers_footers", "path": ["bench_data/headers_footers.jsonl"]}, {"split": "long_tiny_text", "path": ["bench_data/long_tiny_text.jsonl"]}, {"split": "multi_column", "path": ["bench_data/multi_column.jsonl"]}, {"split": "old_scans", "path": ["bench_data/old_scans.jsonl"]}, {"split": "old_scans_math", "path": ["bench_data/old_scans_math.jsonl"]}, {"split": "table_tests", "path": ["bench_data/table_tests.jsonl"]}]}], "language": ["en"], "pretty_name": "olmOCR-bench", "size_categories": ["1K<n<10K"]} | false | False | 2026-02-19T17:28:38 | 121 | 8 | false | 54a96a6fb6a2bd3b297e59869491db4d3625b711 |
olmOCR-bench
olmOCR-bench is a dataset of 1,403 PDF files, plus 7,010 unit test cases that capture properties of the output that a good OCR system should have.
This benchmark evaluates the ability of OCR systems to accurately convert PDF documents to markdown format while preserving critical textual and structural information.
Quick links:
📃 Paper
🛠️ Code
🎮 Demo
Table 1. Distribution of Test Classes by Document Source
Document Source
Text Present
Text… See the full description on the dataset page: https://huggingface.co/datasets/allenai/olmOCR-bench. | 2,402 | 32,850 | [
"benchmark:official",
"benchmark:eval-yaml",
"language:en",
"license:odc-by",
"size_categories:1K<n<10K",
"modality:document",
"modality:text",
"arxiv:2502.18443",
"region:us",
"text"
] | 2025-03-14T16:41:33 | null | null |
689d7cdd5219881b53bd55f3 | nvidia/Nemotron-Pretraining-Dataset-sample | nvidia | {"license": "other", "configs": [{"config_name": "Nemotron-CC-MATH", "data_files": [{"path": "Nemotron-CC-MATH/*.parquet", "split": "train"}]}, {"config_name": "Nemotron-CC-High-Quality", "data_files": [{"path": "Nemotron-CC-High-Quality/*.parquet", "split": "train"}]}, {"config_name": "Nemotron-CC-High-Quality-Synthetic", "data_files": [{"path": "Nemotron-CC-High-Quality-Synthetic/*.parquet", "split": "train"}]}, {"config_name": "Nemotron-CC-Diverse-QA", "data_files": [{"path": "Nemotron-CC-Diverse-QA/*.parquet", "split": "train"}]}, {"config_name": "Nemotron-CC-Translated-Diverse-QA", "data_files": [{"path": "Nemotron-CC-Translated-Diverse-QA/*.parquet", "split": "train"}]}, {"config_name": "Nemotron-Synthetic-Code", "data_files": [{"path": "Nemotron-Synthetic-Code/*.parquet", "split": "train"}]}, {"config_name": "Nemotron-SFT-Code", "data_files": [{"path": "Nemotron-SFT-Code/*.parquet", "split": "train"}]}, {"config_name": "Nemotron-SFT-General", "data_files": [{"path": "Nemotron-SFT-General/*.parquet", "split": "train"}]}, {"config_name": "Nemotron-SFT-MATH", "data_files": [{"path": "Nemotron-SFT-MATH/*.parquet", "split": "train"}]}, {"config_name": "Nemotron-Code-Metadata", "data_files": [{"path": "Nemotron-Code-Metadata/*.parquet", "split": "train"}]}], "track_downloads": true} | false | False | 2025-12-22T17:07:37 | 50 | 8 | false | 3ad096e6394e487bb4f778733300da85275bb449 |
Nemotron-Pre-Training-Dataset-v1 Release
Data Overview
This pretraining dataset, for generative AI model training, preserves high-value math and code while enriching it with diverse multilingual Q&A, fueling the next generation of intelligent, globally-capable models.
This dataset supports NVIDIA Nemotron Nano 2, a family of large language models (LLMs) that consists of the NVIDIA-Nemotron-Nano-9B-v2, NVIDIA-Nemotron-Nano-9B-v2-Base, and NVIDIA-Nemotron-Nano-12B-v2-Base… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Dataset-sample. | 777 | 7,529 | [
"license:other",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2508.14444",
"region:us"
] | 2025-08-14T06:06:21 | 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 | 156 | 8 | 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. | 889 | 4,636 | [
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2025-12-14T02:52:50 | 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. | 905 | 961 | [
"task_categories:robotics",
"task_categories:image-to-text",
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"format:optimized-parquet",
"modality:image",
"modality:text",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:polars",
"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. | 989 | 989 | [
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"code",
"finance",
"legal",
"... | 2026-02-19T05:34:46 | 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 | 8 | 8 | 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. | 218 | 218 | [
"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 |
69b47da50db2f0b674627622 | yatin-superintelligence/Adversarial-Agent-Intent-Safety-Analysis-240K | yatin-superintelligence | {"pretty_name": "Adversarial Agent Intent Safety Analysis 240K", "license": "openrail", "language": ["en"], "library_name": "datasets", "size_categories": ["100K<n<1M"], "task_categories": ["text-classification", "text-generation", "question-answering", "reinforcement-learning", "robotics"], "tags": ["agent", "text", "safety", "jailbreak", "alignment", "trust", "digital-arrest", "robotics", "reinforcement-learning", "surveillance", "synthetic", "adversarial", "intent-analysis", "red-teaming", "guardrails", "dual-use", "classification", "agentic", "reasoning", "system-2", "chain-of-thought", "cybersecurity", "malware", "hacker", "document", "tool-use", "software", "engineering", "code", "legal", "medical", "healthcare", "biology", "chemistry", "finance", "science", "datasets", "parquet", "pandas", "polars", "dask"], "extra_gated_prompt": "Please complete this form to request access to the Adversarial Agent Intent Safety Analysis 240K dataset. This dataset is released for AI safety research, red-teaming, and responsible model development.", "extra_gated_fields": {"Full Name": "text", "Email": "text", "Organization / Institution / Company": "text", "Academic or Commercial Use": {"type": "select", "options": ["Academic / Research", "Commercial", "Personal / Non-commercial", "Government / Policy"]}, "Country": "country"}, "dataset_info": {"features": [{"name": "batch_index", "dtype": "int64"}, {"name": "mode", "dtype": "string"}, {"name": "sophistication", "dtype": "string"}, {"name": "risk_level", "dtype": "string"}, {"name": "adversarial_prompt", "dtype": "string"}, {"name": "surface_interpretation", "dtype": "string"}, {"name": "intent_analysis", "dtype": "string"}, {"name": "clarifying_questions", "dtype": "string"}], "splits": [{"name": "train", "num_examples": 242454}]}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "adversarial_intent_safety_*.parquet"}]}]} | false | auto | 2026-03-15T13:06:29 | 8 | 8 | false | dc44c76a1b831fee8ee07cf1a9bace762b0af3a4 |
Adversarial Agent Intent Safety Analysis 240K
Abstract
The Adversarial-Agent-Intent-Safety-Analysis-240K is a deterministically structured dataset featuring 242,454 context-rich adversarial prompts and safety evaluations. Engineered strictly for training frontier command-and-control models, guardrail classifiers, and red-teaming agents, it encourages models to parse multi-layered intention across 126 critical risk vectors.
This design trains models to decouple the surface… See the full description on the dataset page: https://huggingface.co/datasets/yatin-superintelligence/Adversarial-Agent-Intent-Safety-Analysis-240K. | 26 | 26 | [
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:question-answering",
"task_categories:reinforcement-learning",
"task_categories:robotics",
"language:en",
"license:openrail",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"modality:docum... | 2026-03-13T21:12:05 | null | null |
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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
- 8,421
Size of downloaded dataset files:
1.86 GB
Size of the auto-converted Parquet files:
1.86 GB
Number of rows:
4,853,992