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696b2406e6c69ff4f49745f4
|
sojuL/RubricHub_v1
|
sojuL
|
{"license": "apache-2.0", "language": ["zh", "en"], "tags": ["medical", "science", "wirting", "isntruction", "chat", "general"], "pretty_name": "RubricHub", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "reinforcement-learning", "question-answering"]}
| false
|
False
| 2026-02-03T03:09:05
| 236
| 110
| false
|
3837d55971473a872e84879c88f708b8da3ec2ef
|
RubricHub
RubricHub is a large-scale (approximately 110K), multi-domain dataset that provides high-quality rubric-based supervision for open-ended generation tasks. It is constructed via an automated coarse-to-fine rubric generation framework, which integrates principle-guided synthesis, multi-model aggregation, and difficulty evolution to produce comprehensive and highly discriminative evaluation criteria, overcoming the supervision ceiling ofโฆ See the full description on the dataset page: https://huggingface.co/datasets/sojuL/RubricHub_v1.
| 1,103
| 1,103
|
[
"task_categories:text-generation",
"task_categories:reinforcement-learning",
"task_categories:question-answering",
"language:zh",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2601.08430",
"region:us",
"medical",
"science",
"wirting",
"isntruction",
"chat",
"general"
] | 2026-01-17T05:54:14
| null | null |
6979a5a831f39715e08652e7
|
OpenDataArena/MMFineReason-1.8M-Qwen3-VL-235B-Thinking
|
OpenDataArena
|
{"license": "apache-2.0", "task_categories": ["visual-question-answering", "question-answering", "text-generation"], "language": ["en"], "tags": ["multimodal", "reasoning", "chain-of-thought", "mathematics", "science", "STEM", "visual-reasoning", "VLM", "distillation"], "size_categories": ["1M<n<10M"]}
| false
|
False
| 2026-01-30T03:25:45
| 94
| 94
| false
|
843d86e3b1330b5484c404703755c262fdcc756d
|
MMFineReason
Closing the Multimodal Reasoning Gap via Open Data-Centric Methods
Average score across mathematical reasoning and multimodal understanding benchmarks.
๐ Overview
MMFineReason is a large-scale, high-quality multimodal reasoning dataset comprising 1.8M samples and 5.1B solution tokens, featuring detailed reasoning annotations distilled from Qwen3-VL-235B-A22B-Thinking.
๐ฏ Key Highlights
1.8M High-Quality Samples with 5.1B Solution Tokensโฆ See the full description on the dataset page: https://huggingface.co/datasets/OpenDataArena/MMFineReason-1.8M-Qwen3-VL-235B-Thinking.
| 2,121
| 2,121
|
[
"task_categories:visual-question-answering",
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2601.21821",
"region:us",
"multimodal",
"reasoning",
"chain-of-thought",
"mathematics",
"science",
"STEM",
"visual-reasoning",
"VLM",
"distillation"
] | 2026-01-28T05:59:04
| null | null |
6965b354f2c297a7078582d4
|
Qwen/DeepPlanning
|
Qwen
|
{"language": ["en", "zh"], "license": "apache-2.0", "viewer": false, "task_categories": ["text-generation"], "tags": ["planning", "llm-benchmark", "reasoning", "autonomous-agents"], "pretty_name": "DeepPlanning", "size_categories": ["1k<n<10k"]}
| false
|
False
| 2026-01-27T05:22:17
| 144
| 88
| false
|
4769c4974f6a2ac026a725a9e99320727454ead8
|
DeepPlanning: Benchmarking Long-Horizon Agentic Planning with Verifiable Constraints
DeepPlanningBench is a challenging benchmark for evaluating long-horizon agentic planning capabilities of large language models (LLMs) with verifiable constraints. It features realistic multi-day travel planning and multi-product shopping tasks that require proactive information acquisition, local constrained reasoning, and global constrained optimization.
๐ Website:โฆ See the full description on the dataset page: https://huggingface.co/datasets/Qwen/DeepPlanning.
| 503
| 503
|
[
"task_categories:text-generation",
"language:en",
"language:zh",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:webdataset",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"arxiv:2601.18137",
"region:us",
"planning",
"llm-benchmark",
"reasoning",
"autonomous-agents"
] | 2026-01-13T02:52:04
| null | null |
6978a37bcc1cd38620f46bbc
|
MiniMaxAI/role-play-bench
|
MiniMaxAI
|
{"language": ["zh", "en"], "license": "apache-2.0", "task_categories": ["text-generation"], "size_categories": ["1K<n<10K"], "configs": [{"config_name": "seeds_zh", "data_files": [{"split": "test", "path": "data/zh/seeds.parquet"}]}, {"config_name": "seeds_en", "data_files": [{"split": "test", "path": "data/en/seeds.parquet"}]}, {"config_name": "dialogues_zh", "data_files": [{"split": "test", "path": "data/zh/dialogues.parquet"}]}, {"config_name": "dialogues_en", "data_files": [{"split": "test", "path": "data/en/dialogues.parquet"}]}, {"config_name": "evaluations_zh", "data_files": [{"split": "test", "path": "data/zh/evaluations.parquet"}]}, {"config_name": "evaluations_en", "data_files": [{"split": "test", "path": "data/en/evaluations.parquet"}]}]}
| false
|
False
| 2026-01-28T04:01:11
| 104
| 75
| false
|
3c1be2a56afbcaab19ae6b40b8a24429eae792f5
|
Role-play Benchmark
A comprehensive benchmark for evaluating Role-play Agents in Chinese and English scenarios.
Dataset Summary
Role-play Benchmark is designed to evaluate Role-play Agents' ability to deliver immersive role-play experiences through Situated Reenactment. Unlike traditional benchmarks with verifiable answers, Role-play is fundamentally non-verifiable, e.g., there's no single "correct" response when a tsundere character is asked "Do you like me?". Insteadโฆ See the full description on the dataset page: https://huggingface.co/datasets/MiniMaxAI/role-play-bench.
| 612
| 612
|
[
"task_categories:text-generation",
"language:zh",
"language:en",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-01-27T11:37:31
| null | null |
6976521d67df645b2b063143
|
nvidia/Nemotron-Personas-Brazil
|
nvidia
|
{"license": "cc-by-4.0", "task_categories": ["text-generation"], "language": ["pt"], "tags": ["synthetic", "personas", "NVIDIA", "datadesigner"], "size_categories": ["1M<n<10M"], "dataset_info": {"features": [{"name": "uuid", "dtype": "string"}, {"name": "professional_persona", "dtype": "string"}, {"name": "sports_persona", "dtype": "string"}, {"name": "arts_persona", "dtype": "string"}, {"name": "travel_persona", "dtype": "string"}, {"name": "culinary_persona", "dtype": "string"}, {"name": "persona", "dtype": "string"}, {"name": "cultural_background", "dtype": "string"}, {"name": "skills_and_expertise", "dtype": "string"}, {"name": "skills_and_expertise_list", "dtype": "string"}, {"name": "hobbies_and_interests", "dtype": "string"}, {"name": "hobbies_and_interests_list", "dtype": "string"}, {"name": "career_goals_and_ambitions", "dtype": "string"}, {"name": "sex", "dtype": "string"}, {"name": "age", "dtype": "int64"}, {"name": "marital_status", "dtype": "string"}, {"name": "education_level", "dtype": "string"}, {"name": "occupation", "dtype": "string"}, {"name": "municipality", "dtype": "string"}, {"name": "state", "dtype": "string"}, {"name": "country", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5395470286, "num_examples": 1000000}], "download_size": 2514627068, "dataset_size": 5395470286}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
| false
|
False
| 2026-01-26T23:09:43
| 77
| 67
| false
|
441be2bd83a829020452ba9242efd31d212ae602
|
Nemotron-Personas-Brazil
Abordagem de IA composta para geraรงรฃo de personas baseada em distribuiรงรตes do mundo real
Visรฃo Geral do Conjunto de Dados (Dataset Overview):
Nemotron-Personas-Brazil รฉ um conjunto de dados (dataset) de cรณdigo aberto (CC BY 4.0) composto por personas geradas sinteticamente e fundamentadas em distribuiรงรตes demogrรกficas, geogrรกficas e traรงos de personalidade reais do Brasil, visando capturar a diversidade e a riqueza da populaรงรฃo. Trata-se deโฆ See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Personas-Brazil.
| 5,905
| 5,905
|
[
"task_categories:text-generation",
"language:pt",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"format:optimized-parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"library:datadesigner",
"region:us",
"synthetic",
"personas",
"NVIDIA",
"datadesigner"
] | 2026-01-25T17:25:49
| null | null |
6977293fe22c059ad0184148
|
tencent/CL-bench
|
tencent
|
{"language": ["en"], "license": "other", "task_categories": ["text-generation"], "pretty_name": "CL-bench", "size_categories": ["1K<n<10K"], "tags": ["context-learning", "long-context", "benchmark"]}
| false
|
False
| 2026-02-04T06:09:30
| 57
| 57
| false
|
ae92df21c0abce4cdfb5bea39907bf4cb2703f96
|
CL-bench: A Benchmark for Context Learning
Dataset Description
CL-bench is a benchmark for evaluating language models' context learning abilities.
Resolving tasks in CL-bench requires models to learn from the provided context, ranging from new domain-specific knowledge, rule systems, and complex procedures to laws derived from empirical data, rather than only relying on pre-trained knowledge.
Dataset Statistics
Total Samples: 1,899 tasks
Format: JSONL (oneโฆ See the full description on the dataset page: https://huggingface.co/datasets/tencent/CL-bench.
| 421
| 421
|
[
"task_categories:text-generation",
"language:en",
"license:other",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2602.03587",
"region:us",
"context-learning",
"long-context",
"benchmark"
] | 2026-01-26T08:43:43
| null | null |
6979d9b81ad3c64ffcb046fd
|
OpenDataArena/MMFineReason-SFT-123K-Qwen3-VL-235B-Thinking
|
OpenDataArena
|
{"license": "apache-2.0", "task_categories": ["visual-question-answering", "question-answering", "text-generation"], "language": ["en"], "tags": ["multimodal", "reasoning", "chain-of-thought", "mathematics", "science", "STEM", "visual-reasoning", "VLM", "distillation", "hard-samples"], "size_categories": ["100K<n<1M"]}
| false
|
False
| 2026-02-03T12:48:24
| 57
| 57
| false
|
cda6140bfba449bef9e57065a528b7424966e144
|
MMFineReason-SFT-123K
The Hardest 7% โ Less Data, More Reasoning
๐ Overview
MMFineReason-SFT-123K is a difficulty-filtered subset of MMFineReason-1.8M, containing only the hardest 7% of samples where Qwen3-VL-4B-Thinking consistently fails (pass rate = 0).
๐ฏ Key Highlights
123K Challenging Samples: Only instances where a 4B thinking model fails all 4 inference attemptsEfficient Training: Comparable performance to full 1.8M dataset with only 7% ofโฆ See the full description on the dataset page: https://huggingface.co/datasets/OpenDataArena/MMFineReason-SFT-123K-Qwen3-VL-235B-Thinking.
| 371
| 371
|
[
"task_categories:visual-question-answering",
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2601.21821",
"region:us",
"multimodal",
"reasoning",
"chain-of-thought",
"mathematics",
"science",
"STEM",
"visual-reasoning",
"VLM",
"distillation",
"hard-samples"
] | 2026-01-28T09:41:12
| null | null |
696ddc1ba806b4bfbcfc0224
|
opendatalab/ChartVerse-SFT-1800K
|
opendatalab
|
{"license": "apache-2.0", "language": ["en"], "task_categories": ["visual-question-answering", "image-text-to-text"], "tags": ["chart", "reasoning", "vision-language", "multimodal", "chart-understanding", "CoT", "SFT", "large-scale"], "size_categories": ["1M<n<10M"]}
| false
|
False
| 2026-01-30T08:01:50
| 131
| 53
| false
|
86fd98bdfac3e7fa2120748e7d6c597e7ee26cf8
|
ChartVerse-SFT-1800K is an extended large-scale chart reasoning dataset with Chain-of-Thought (CoT) annotations, developed as part of the opendatalab/ChartVerse project. For more details about our method, datasets, and full model series, please visit our Project Page.
This dataset contains all verified correct samples without failure rate filtering. Unlike SFT-600K which excludes easy samples (r=0), SFT-1800K includes the complete set of truth-anchored QA pairs for maximum coverage and scale.โฆ See the full description on the dataset page: https://huggingface.co/datasets/opendatalab/ChartVerse-SFT-1800K.
| 2,784
| 2,784
|
[
"task_categories:visual-question-answering",
"task_categories:image-text-to-text",
"language:en",
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2601.13606",
"region:us",
"chart",
"reasoning",
"vision-language",
"multimodal",
"chart-understanding",
"CoT",
"SFT",
"large-scale"
] | 2026-01-19T07:24:11
| null | null |
697bbe08a3b926e427d4bac2
|
crownelius/Opus-4.5-WritingStyle-1000x
|
crownelius
|
{"license": "apache-2.0"}
| false
|
False
| 2026-01-29T20:11:26
| 52
| 52
| false
|
8c4a72bb52b33252d23fd50dc7e9c8d296248aff
| null | 90
| 90
|
[
"license:apache-2.0",
"region:us"
] | 2026-01-29T20:07:36
| null | null |
698329830f3a0db58fc9e7d4
|
FutureMa/EvasionBench
|
FutureMa
|
{"license": "apache-2.0", "task_categories": ["text-classification"], "language": ["en"], "tags": ["finance", "earnings-calls", "evasion-detection", "nlp"], "pretty_name": "EvasionBench", "size_categories": ["10K<n<100K"]}
| false
|
False
| 2026-02-05T04:42:20
| 49
| 49
| false
|
d87d231387e8bc850ca33e1d670fccbbbcf56c6c
|
EvasionBench
EvasionBench is a benchmark dataset for detecting evasive answers in earnings call Q&A sessions. The task is to classify how directly corporate management addresses questions from financial analysts.
Dataset Summary
This dataset contains 16,726 question-answer pairs from earnings call transcripts, each labeled with one of three evasion levels. The labels were generated using the Eva-4B-V2 model, a fine-tuned classifier specifically trained forโฆ See the full description on the dataset page: https://huggingface.co/datasets/FutureMa/EvasionBench.
| 22
| 22
|
[
"task_categories:text-classification",
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2601.09142",
"region:us",
"finance",
"earnings-calls",
"evasion-detection",
"nlp"
] | 2026-02-04T11:12:03
| null | null |
697ca9768b0dd3ffab6523fe
|
OpenMed/Medical-Reasoning-SFT-Trinity-Mini
|
OpenMed
|
{"license": "apache-2.0", "task_categories": ["text-generation", "question-answering"], "language": ["en"], "tags": ["medical", "reasoning", "healthcare", "clinical", "chain-of-thought", "thinking", "sft"], "size_categories": ["100K<n<1M"]}
| false
|
False
| 2026-01-31T20:47:09
| 46
| 46
| false
|
ffccc8338fff6f3ebff08dad5eea8b457a051e33
|
Medical-Reasoning-SFT-Trinity-Mini
A large-scale medical reasoning dataset generated using arcee-ai/Trinity-Mini, containing over 810,000 samples with detailed chain-of-thought reasoning for medical and healthcare questions.
Dataset Overview
Metric
Value
Model
arcee-ai/Trinity-Mini
Total Samples
~810,374
Estimated Tokens
~1.52 Billion
Content Tokens
~542 Million
Reasoning Tokens
~977 Million
Language
English
Schema
Each sampleโฆ See the full description on the dataset page: https://huggingface.co/datasets/OpenMed/Medical-Reasoning-SFT-Trinity-Mini.
| 588
| 588
|
[
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"format:optimized-parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"medical",
"reasoning",
"healthcare",
"clinical",
"chain-of-thought",
"thinking",
"sft"
] | 2026-01-30T12:52:06
| null | null |
69779ec1f6a3e303e3bf234d
|
moonshotai/WorldVQA
|
moonshotai
|
{"license": "apache-2.0", "task_categories": ["visual-question-answering"], "language": ["en", "zh"], "size_categories": ["1K<n<10K"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "WorldVQA.tsv"}], "sep": "\t"}]}
| false
|
False
| 2026-02-04T07:28:09
| 49
| 40
| false
|
29e1d54b27ffb34cdffb4cdc95d29afcf101f1f7
|
WorldVQA
WorldVQA: Measuring Atomic World Knowledge in Multimodal Large Language Models
HomePage |
Dataset |
Paper |
Code
Abstract
We introduce WorldVQA, a benchmark designed to evaluate the atomic vision-centric world knowledge of Multimodal Large Language Models (MLLMs). Current evaluations often conflate visual knowledge retrieval with reasoning. In contrast, WorldVQA decouples these capabilities to strictly measure "what the modelโฆ See the full description on the dataset page: https://huggingface.co/datasets/moonshotai/WorldVQA.
| 2,857
| 2,857
|
[
"task_categories:visual-question-answering",
"language:en",
"language:zh",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:csv",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2602.02537",
"region:us"
] | 2026-01-26T17:05:05
| null | null |
696e2528357a40707550b1c4
|
google/WaxalNLP
|
google
|
{"language_creators": ["creator_1"], "language": ["ach", "aka", "amh", "dag", "dga", "ewe", "fat", "ful", "hau", "ibo", "kik", "kpo", "lin", "lug", "luo", "mas", "mlg", "nyn", "orm", "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": "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": "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": "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": "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": "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": "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": "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": "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": "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
| 2026-02-05T07:51:25
| 80
| 38
| false
|
0c72f661c468321674071d2bcfdb5991ca1defae
|
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 languageโฆ See the full description on the dataset page: https://huggingface.co/datasets/google/WaxalNLP.
| 4,874
| 4,874
|
[
"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",
"language:dag",
"language:dga",
"language:ewe",
"language:fat",
"language:ful",
"language:hau",
"language:ibo",
"language:kik",
"language:kpo",
"language:lin",
"language:lug",
"language:luo",
"language:mas",
"language:mlg",
"language:nyn",
"language:orm",
"language:sid",
"language:sna",
"language:sog",
"language:swa",
"language:tir",
"language:twi",
"language:wal",
"language:yor",
"license:cc-by-sa-4.0",
"license:cc-by-4.0",
"modality:audio",
"arxiv:2602.02734",
"region:us",
"audio",
"automatic-speech-recognition",
"text-to-speech"
] | 2026-01-19T12:35:52
| null | null |
696f96e3069809fcbca97030
|
uv-scripts/unsloth-jobs
|
uv-scripts
|
{"viewer": false, "tags": ["uv-script", "unsloth", "training", "hf-jobs", "vlm", "fine-tuning"]}
| false
|
False
| 2026-01-30T13:35:19
| 34
| 33
| false
|
b49d2744327cd2240686615b843ab546e3fa497e
|
๐ฆฅ Unsloth Training Scripts for HF Jobs
UV scripts for fine-tuning LLMs and VLMs using Unsloth on HF Jobs (on-demand cloud GPUs). UV handles dependency installation automatically, so you can run these scripts directly without any local setup.
Prerequisites
A Hugging Face account with a token
The HF CLI: curl -LsSf https://hf.co/cli/install.sh | bash
A dataset on the Hub (see format requirements below)
Data Format
VLM Fine-tuning
Requires images andโฆ See the full description on the dataset page: https://huggingface.co/datasets/uv-scripts/unsloth-jobs.
| 73
| 73
|
[
"region:us",
"uv-script",
"unsloth",
"training",
"hf-jobs",
"vlm",
"fine-tuning"
] | 2026-01-20T14:53:23
| null | null |
697d119a2e5ae66a3d8d0015
|
ronantakizawa/moltbook
|
ronantakizawa
|
{"license": "mit", "task_categories": ["text-classification", "text-generation"], "language": ["en"], "tags": ["ai", "ai-agents", "clawd", "moltbook"], "size_categories": ["1K<n<10K"], "configs": [{"config_name": "posts", "data_files": "moltbook_posts.csv"}, {"config_name": "submolts", "data_files": "moltbook_submolts.csv"}]}
| false
|
False
| 2026-02-02T17:44:19
| 31
| 31
| false
|
41c34a23fb5316f9360a0bee78ca95f5ba010b92
|
Moltbook Dataset
A dataset of posts and communities from Moltbook - a Reddit-style social platform designed for AI agents.
NOTE: This dataset is a snapshot of Moltbook before it went viral and got flooded with inauthentic accounts such as humans and bots.
Files
File
Records
Description
moltbook_posts.csv
6,105
All posts from the platform
moltbook_submolts.csv
124
All communities (submolts)
Dataset Insights
Overviewโฆ See the full description on the dataset page: https://huggingface.co/datasets/ronantakizawa/moltbook.
| 2,393
| 2,393
|
[
"task_categories:text-classification",
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"ai",
"ai-agents",
"clawd",
"moltbook"
] | 2026-01-30T20:16:26
| null | null |
6960b100448a2a7a83c8f3fb
|
nyuuzyou/google-code-archive
|
nyuuzyou
|
{"annotations_creators": ["machine-generated"], "language_creators": ["found"], "language": ["code", "en"], "license": "other", "multilinguality": ["multilingual"], "pretty_name": "Google Code Archive Dataset", "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["text-generation"], "tags": ["code", "google-code", "archive"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/*.parquet"}], "default": true}], "dataset_info": {"features": [{"name": "code", "dtype": "string"}, {"name": "repo_name", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "size", "dtype": "int64"}]}}
| false
|
False
| 2026-02-01T20:38:02
| 67
| 30
| false
|
0a5433fb20e76e46acae3d223ef6df329e6d5a4c
|
Google Code Archive Dataset
Dataset Description
This dataset was compiled from the Google Code Archive, a preserved snapshot of projects hosted on Google Code, Google's open-source project hosting service that operated from 2006 to 2016. Google Code was one of the major code hosting platforms of its era, hosting hundreds of thousands of open-source projects before its shutdown. The archive provides a unique historical record of open-source development during a formativeโฆ See the full description on the dataset page: https://huggingface.co/datasets/nyuuzyou/google-code-archive.
| 1,508
| 1,508
|
[
"task_categories:text-generation",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:multilingual",
"source_datasets:original",
"language:code",
"language:en",
"license:other",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"code",
"google-code",
"archive"
] | 2026-01-09T07:40:48
| null | null |
69524c8ad001e56220ced9bc
|
Alibaba-Apsara/Superior-Reasoning-SFT-gpt-oss-120b
|
Alibaba-Apsara
|
{"license": "cc-by-4.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["code", "math", "scientific-qa", "instruction-following", "reasoning", "thinking", "gpt-oss-120b", "distill"], "size_categories": ["435K"], "configs": [{"config_name": "stage1", "data_files": "Superior-Reasoning-SFT-gpt-oss-120b-stage1-train-data.jsonl", "features": [{"name": "uuid", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "meta", "dtype": "string"}]}, {"config_name": "stage2", "data_files": "Superior-Reasoning-SFT-gpt-oss-120b-stage2-train-data.jsonl", "features": [{"name": "uuid", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "meta", "dtype": "string"}]}]}
| false
|
False
| 2026-01-31T10:05:46
| 324
| 29
| false
|
21b55a649f05ac110bea5a64a9c064a5100ff554
|
Superior-Reasoning-SFT-gpt-oss-120b
ย
ย
ย
ย
ย
๐ฃ News
Our dataset ranked #1 on the Hugging Face Datasets Trending leaderboard from January 20 to January 30.
๐ Overview
The Superior-Reasoning-SFT-gpt-oss-120b dataset is a high-quality, open-source collection containing 435K samples designed to democratize the training of high-performance Long Chain-of-Thought (Long-CoT) models. Unlike standard distilled datasets that rely on random sampling orโฆ See the full description on the dataset page: https://huggingface.co/datasets/Alibaba-Apsara/Superior-Reasoning-SFT-gpt-oss-120b.
| 35,293
| 35,299
|
[
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2601.09088",
"arxiv:2512.20908",
"region:us",
"code",
"math",
"scientific-qa",
"instruction-following",
"reasoning",
"thinking",
"gpt-oss-120b",
"distill"
] | 2025-12-29T09:40:26
| null | null |
68fbac6e20e0a787e6d0328b
|
OpenDataArena/MMFineReason-Full-2.3M-Qwen3-VL-235B-Thinking
|
OpenDataArena
|
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{"config_name": "FineVision-raven", "data_files": [{"split": "train", "path": "FineVision-raven/train-*"}]}, {"config_name": "FineVision-scienceqa", "data_files": [{"split": "train", "path": "FineVision-scienceqa/train-*"}]}, {"config_name": "FineVision-tqa", "data_files": [{"split": "train", "path": "FineVision-tqa/train-*"}]}, {"config_name": "FineVision-visualwebinstruct(filtered)", "data_files": [{"split": "train", "path": "FineVision-visualwebinstruct(filtered)/train-*"}]}, {"config_name": "FineVision-visualwebinstruct_filtered_", "data_files": [{"split": "train", "path": "FineVision-visualwebinstruct_filtered_/train-*"}]}, {"config_name": "GameQA-140K", "data_files": [{"split": "train", "path": "GameQA-140K/train-*"}]}, {"config_name": "LLaVA-CoT", "data_files": [{"split": "train", "path": "LLaVA-CoT/train-*"}]}, {"config_name": "MMK12", "data_files": [{"split": "train", "path": "MMK12/train-*"}]}, {"config_name": "MMR1", "data_files": [{"split": "train", "path": "MMR1/train-*"}]}, {"config_name": "PuzzleQA", "data_files": [{"split": "train", "path": "PuzzleQA/train-*"}]}, {"config_name": "ViRL39K", "data_files": [{"split": "train", "path": "ViRL39K/train-*"}]}, {"config_name": "VisualSphinx", "data_files": [{"split": "train", "path": "VisualSphinx/train-*"}]}, {"config_name": "WaltonColdStart", "data_files": [{"split": "train", "path": "WaltonColdStart/train-*"}]}, {"config_name": "WeMath2-Pro", "data_files": [{"split": "train", "path": "WeMath2-Pro/train-*"}]}, {"config_name": "WeMath2-SFT", "data_files": [{"split": "train", "path": "WeMath2-SFT/train-*"}]}, {"config_name": "WeMath2-Standard", "data_files": [{"split": "train", "path": "WeMath2-Standard/train-*"}]}, {"config_name": "Zebra-CoT-Physics", "data_files": [{"split": "train", "path": "Zebra-CoT-Physics/train-*"}]}, {"config_name": "mmopenr1-8k", "data_files": [{"split": "train", "path": "mmopenr1-8k/train-*"}]}], "license": "apache-2.0", "task_categories": ["visual-question-answering", "question-answering", "text-generation"], "language": ["en"], "tags": ["multimodal", "reasoning", "chain-of-thought", "mathematics", "science", "STEM", "visual-reasoning", "VLM", "distillation", "unfiltered"], "size_categories": ["1M<n<10M"]}
| false
|
False
| 2026-02-03T09:23:32
| 53
| 28
| false
|
ca26b43465cbb05a84c1c47ebec646ba06847565
|
MMFineReason-Full-2.3M
The Complete Pre-Selection Dataset โ Before Quality Filtering
๐ Overview
MMFineReason-Full-2.3M is the complete pre-selection dataset containing 2.3M samples and 8.8B solution tokens, generated through our reasoning distillation pipeline before the data selection stage. This dataset includes all samples that passed basic template and length validation, but have not undergone correctness verification filtering.
๐ฏ Key Characteristicsโฆ See the full description on the dataset page: https://huggingface.co/datasets/OpenDataArena/MMFineReason-Full-2.3M-Qwen3-VL-235B-Thinking.
| 2,583
| 6,310
|
[
"task_categories:visual-question-answering",
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:1M<n<10M",
"modality:image",
"modality:text",
"arxiv:2601.21821",
"region:us",
"multimodal",
"reasoning",
"chain-of-thought",
"mathematics",
"science",
"STEM",
"visual-reasoning",
"VLM",
"distillation",
"unfiltered"
] | 2025-10-24T16:42:22
| null | null |
6967b2da7b115954f1c9327c
|
mercor/apex-agents
|
mercor
|
{"license": "cc-by-4.0", "language": ["en"], "tags": ["agents", "benchmarking", "finance", "legal", "management-consulting", "tool-use", "long-horizon"], "pretty_name": "apex-agents", "size_categories": ["n<1K"]}
| false
|
False
| 2026-01-22T00:33:03
| 83
| 27
| false
|
602aae289ba9f4b74c27635e6f3a1738b000e5be
|
APEXโAgents
APEXโAgents is a benchmark from Mercor for evaluating whether AI agents can execute long-horizon, cross-application professional services tasks. Tasks were created by investment banking analysts, management consultants, and corporate lawyers, and require agents to navigate realistic work environments with files and tools (e.g., docs, spreadsheets, PDFs, email, chat, calendar).
Tasks: 480 total (160 per job category)
Worlds: 33 total (10 banking, 11 consulting, 12 law)โฆ See the full description on the dataset page: https://huggingface.co/datasets/mercor/apex-agents.
| 13,720
| 13,720
|
[
"language:en",
"license:cc-by-4.0",
"size_categories:n<1K",
"arxiv:2601.14242",
"region:us",
"agents",
"benchmarking",
"finance",
"legal",
"management-consulting",
"tool-use",
"long-horizon"
] | 2026-01-14T15:14:34
| null | null |
6972150c16d042c9f9edc8b4
|
t-martyniuk/DAD-3DHeadsDataset
|
t-martyniuk
|
{"license": "cc-by-nc-sa-4.0", "gated": true, "extra_gated_prompt": "Please use your institutional email for requesting the dataset access.\n\nThe \"Researcher\" has requested permission to use the DAD-3DHeads dataset (the \"Dataset\") created at Pi\u00f1ata Farms Inc. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n\n1. Researcher shall use the Dataset for non-commercial research and educational purposes only.\n2. Researcher accepts full responsibility for them using the Dataset and shall defend and indemnify Pi\u00f1ata Farms Inc., including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Dataset, including but not limited to Researcher's use of any copies of copyrighted images that they may create from the Dataset.\n3. The Dataset may not be passed to a third party, beyond the immediate research group of the Researcher.\n4. Pi\u00f1ata Farms Inc. reserves the right to terminate Researcher's access to the Dataset at any time.\n5. Researcher understands and is acknowledged that these images are provided 'as is', without any express or implied warranties of fitness for a particular purpose.\n6. All submitted papers (or any publicly available text) that uses the compound or partial images of the database must cite the following paper:\n\nTetiana Martyniuk*, Orest Kupyn*, Yana Kurlyak, Igor Krashenyi, Jiri Matas, Viktoriia Sharmanska: \"DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image\", CVPR 2022.\n", "extra_gated_fields": {"Name": "text", "Affiliation": "text", "Intended use": "text", "I agree to the terms": "checkbox"}, "task_categories": ["image-to-3d", "keypoint-detection"], "size_categories": ["10K<n<100K"], "tags": ["facial-landmarks", "head-pose-estimation", "keypoints", "3d-dense-head-fitting", "3d-head-estimation", "3d-reconstruction", "face", "head", "face-alignment", "face-keypoints", "head-keypoints", "machine-learning", "deep-learning", "computer-vision", "pytorch", "dataset", "flame", "flame-model", "cvpr", "3dmm", "3d-face-alignment", "3d-computer-vision", "3d-face-reconstruction", "papers-with-code", "face-reenactment", "3d-face-modelling", "first-order-motion-model", "3d-head", "cvpr2022"]}
| false
|
manual
| 2026-01-27T14:56:47
| 42
| 27
| false
|
3791ea8b457a2bf064dcdd4856959e7e932f3f54
|
DAD-3DHeads dataset
DAD-3DHeads dataset was introduced in the CVPR 2022 paper
DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image
Tetiana Martyniuk, Orest Kupyn, Yana Kurlyak, Igor Krashenyi, Jiลi Matas, Viktoriia Sharmanska
Please use your institutional email for requesting the dataset access.
Useful links
official DAD-3DHeads Github repository,
DAD-3DHeads benchmark code.
The dataset structure is the following:โฆ See the full description on the dataset page: https://huggingface.co/datasets/t-martyniuk/DAD-3DHeadsDataset.
| 57
| 57
|
[
"task_categories:image-to-3d",
"task_categories:keypoint-detection",
"license:cc-by-nc-sa-4.0",
"size_categories:10K<n<100K",
"arxiv:2204.03688",
"region:us",
"facial-landmarks",
"head-pose-estimation",
"keypoints",
"3d-dense-head-fitting",
"3d-head-estimation",
"3d-reconstruction",
"face",
"head",
"face-alignment",
"face-keypoints",
"head-keypoints",
"machine-learning",
"deep-learning",
"computer-vision",
"pytorch",
"dataset",
"flame",
"flame-model",
"cvpr",
"3dmm",
"3d-face-alignment",
"3d-computer-vision",
"3d-face-reconstruction",
"papers-with-code",
"face-reenactment",
"3d-face-modelling",
"first-order-motion-model",
"3d-head",
"cvpr2022"
] | 2026-01-22T12:16:12
| null | null |
6977a1349d8b1dff27765195
|
nvidia/Nemotron-Personas-Singapore
|
nvidia
|
{"license": "cc-by-4.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["synthetic", "personas", "NVIDIA", "datadesigner"], "size_categories": ["1M<n<10M"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "uuid", "dtype": "string"}, {"name": "professional_persona", "dtype": "string"}, {"name": "sports_persona", "dtype": "string"}, {"name": "arts_persona", "dtype": "string"}, {"name": "travel_persona", "dtype": "string"}, {"name": "culinary_persona", "dtype": "string"}, {"name": "persona", "dtype": "string"}, {"name": "cultural_background", "dtype": "string"}, {"name": "skills_and_expertise", "dtype": "string"}, {"name": "skills_and_expertise_list", "dtype": "string"}, {"name": "hobbies_and_interests", "dtype": "string"}, {"name": "hobbies_and_interests_list", "dtype": "string"}, {"name": "career_goals_and_ambitions", "dtype": "string"}, {"name": "sex", "dtype": "string"}, {"name": "age", "dtype": "int64"}, {"name": "marital_status", "dtype": "string"}, {"name": "education_level", "dtype": "string"}, {"name": "occupation", "dtype": "string"}, {"name": "industry", "dtype": "string"}, {"name": "planning_area", "dtype": "string"}, {"name": "country", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 626406567, "num_examples": 148000}], "download_size": 274800528, "dataset_size": 626406567}}
| false
|
False
| 2026-01-27T00:03:22
| 41
| 23
| false
|
a3994709410410f834bd949d643d3f2796908969
|
Nemotron-Personas-Singapore
A compound AI approach to personas grounded in real-world distributions
Dataset Overview
Nemotron-Personas-Singapore is an open-source (CC BY 4.0) dataset of synthetically-generated personas. This dataset is grounded in real-world demographic, geographic and personality trait distributions in Singapore to capture the diversity and richness of the Singaporean population. It is a variant of Nemotron-Personas-USA, and the first Singaporeanโฆ See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Personas-Singapore.
| 2,400
| 2,400
|
[
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"format:optimized-parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"library:datadesigner",
"region:us",
"synthetic",
"personas",
"NVIDIA",
"datadesigner"
] | 2026-01-26T17:15:32
| null | null |
6464dcc7a0748f9aa4c27389
|
deepset/prompt-injections
|
deepset
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 71720, "num_examples": 546}, {"name": "test", "num_bytes": 15981, "num_examples": 116}], "download_size": 51215, "dataset_size": 87701, "license": "cc-by-4.0"}, "license": "apache-2.0"}
| false
|
False
| 2024-07-30T16:12:57
| 131
| 22
| false
|
4f61ecb038e9c3fb77e21034b22511b523772cdd
|
Dataset Card for "deberta-v3-base-injection-dataset"
More Information needed
| 2,774
| 59,884
|
[
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2023-05-17T13:55:19
| null | null |
68f119703c5910443df36569
|
SWE-Arena/bot_data
|
SWE-Arena
|
nan
| false
|
False
| 2026-01-22T04:55:09
| 42
| 22
| false
|
b7cc227d7f5ef4a237a25eb1f1f0c0f03c6721c4
| null | 247
| 1,527
|
[
"size_categories:n<1K",
"modality:text",
"region:us"
] | 2025-10-16T16:12:32
| null | null |
6928ac839f54f92be8b78d70
|
TeichAI/claude-4.5-opus-high-reasoning-250x
|
TeichAI
|
nan
| false
|
False
| 2025-11-28T03:02:41
| 214
| 22
| 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
| 7,030
| 10,608
|
[
"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 |
696674bae19dee6669d689d8
|
AvitoTech/BAT
|
AvitoTech
|
{"configs": [{"config_name": "fpa_campaigns", "data_files": "fpa/campaigns.csv"}, {"config_name": "fpa_stats", "data_files": "fpa/stats.csv"}, {"config_name": "vcg_campaigns", "data_files": "vcg/campaigns.csv"}, {"config_name": "vcg_stats", "data_files": "vcg/stats.csv"}]}
| false
|
False
| 2026-01-13T16:37:15
| 38
| 20
| false
|
cd15e02054d26a6f1534cab5a7897a7f1bd974b7
|
BAT Dataset
This dataset provides an alternative way to access the data from the BAT (BAT: Benchmark for Auto-bidding Task) autobidding benchmark.
Related Resources
GitHub Repository: avito-tech/bat-autobidding-benchmark
Paper: BAT: Benchmark for Auto-bidding Task
Dataset Description
This dataset contains auction data for First-Price Auction (FPA) and Vickrey-Clarke-Groves (VCG) mechanisms, used for benchmarking autobidding algorithms.
Configurationsโฆ See the full description on the dataset page: https://huggingface.co/datasets/AvitoTech/BAT.
| 84
| 84
|
[
"size_categories:10M<n<100M",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 2026-01-13T16:37:14
| null | null |
67510376ab059ce38f2caaa3
|
SWE-Arena/conversation_data
|
SWE-Arena
|
nan
| false
|
False
| 2026-01-25T17:18:10
| 43
| 19
| false
|
fb2d49c4f9667206dc37622e52af466f45a29d46
| null | 173
| 1,500
|
[
"region:us"
] | 2024-12-05T01:35:50
| null | null |
621ffdd236468d709f181e5e
|
cais/mmlu
|
cais
|
{"annotations_creators": ["no-annotation"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["multiple-choice-qa"], "paperswithcode_id": "mmlu", "pretty_name": "Measuring Massive Multitask Language Understanding", "language_bcp47": ["en-US"], "dataset_info": [{"config_name": "abstract_algebra", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 49618.6654322746, "num_examples": 100}, {"name": "validation", "num_bytes": 5485.515349444808, "num_examples": 11}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 17143, "dataset_size": 57303.3562203159}, {"config_name": "all", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 6967453, "num_examples": 14042}, {"name": "validation", "num_bytes": 763484, "num_examples": 1531}, {"name": "dev", "num_bytes": 125353, "num_examples": 285}, {"name": "auxiliary_train", "num_bytes": 161000625, "num_examples": 99842}], "download_size": 51503402, "dataset_size": 168856915}, {"config_name": "anatomy", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 66985.19833357072, "num_examples": 135}, {"name": "validation", "num_bytes": 6981.5649902024825, "num_examples": 14}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 28864, "dataset_size": 76165.9387623697}, {"config_name": "astronomy", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 75420.3714570574, "num_examples": 152}, {"name": "validation", "num_bytes": 7978.931417374265, "num_examples": 16}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 39316, "dataset_size": 85598.47831302814}, {"config_name": "auxiliary_train", "features": [{"name": "train", "struct": [{"name": "answer", "dtype": "int64"}, {"name": "choices", "sequence": "string"}, {"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}]}], "splits": [{"name": "train", 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{"split": "validation", "path": "public_relations/validation-*"}, {"split": "dev", "path": "public_relations/dev-*"}]}, {"config_name": "security_studies", "data_files": [{"split": "test", "path": "security_studies/test-*"}, {"split": "validation", "path": "security_studies/validation-*"}, {"split": "dev", "path": "security_studies/dev-*"}]}, {"config_name": "sociology", "data_files": [{"split": "test", "path": "sociology/test-*"}, {"split": "validation", "path": "sociology/validation-*"}, {"split": "dev", "path": "sociology/dev-*"}]}, {"config_name": "us_foreign_policy", "data_files": [{"split": "test", "path": "us_foreign_policy/test-*"}, {"split": "validation", "path": "us_foreign_policy/validation-*"}, {"split": "dev", "path": "us_foreign_policy/dev-*"}]}, {"config_name": "virology", "data_files": [{"split": "test", "path": "virology/test-*"}, {"split": "validation", "path": "virology/validation-*"}, {"split": "dev", "path": "virology/dev-*"}]}, {"config_name": "world_religions", "data_files": [{"split": "test", "path": "world_religions/test-*"}, {"split": "validation", "path": "world_religions/validation-*"}, {"split": "dev", "path": "world_religions/dev-*"}]}]}
| false
|
False
| 2024-03-08T20:36:26
| 650
| 18
| false
|
c30699e8356da336a370243923dbaf21066bb9fe
|
Dataset Card for MMLU
Dataset Summary
Measuring Massive Multitask Language Understanding by Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021).
This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge. The test spans subjects in the humanities, social sciences, hard sciences, and other areas that are important for some people to learn. This covers 57 tasksโฆ See the full description on the dataset page: https://huggingface.co/datasets/cais/mmlu.
| 306,299
| 39,898,192
|
[
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:mit",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2009.03300",
"arxiv:2005.00700",
"arxiv:2005.14165",
"arxiv:2008.02275",
"region:us"
] | 2022-03-02T23:29:22
|
mmlu
| null |
69819e28a85b01f9f5d78d50
|
lxucs/tele-lens
|
lxucs
|
{"license": "mit", "task_categories": ["question-answering"], "language": ["en"], "tags": ["llm", "qa", "parity"], "size_categories": ["1K<n<10K"], "configs": [{"config_name": "tele-lens", "data_files": [{"split": "test", "path": "test*"}]}]}
| false
|
False
| 2026-02-03T08:11:29
| 18
| 18
| false
|
85d95985010ef1248644de93fdd55ad6f48bfffc
|
This is the dataset for the paper:
No Global Plan in Chain-of-Thought: Uncover the Latent Planning Horizon of LLMs
In this paper, we uncovered a myopic latent planning horizon in LLMs' Chain-of-Thought (CoT), through our probing method Tele-Lens. We further underscore the significance of exploiting such CoT dynamics, with our proposed methods for estimation of both CoT uncertainty and necessity.
Our dataset spans 12 tasks of diverse domains, which we categorize into three types:
Explicitโฆ See the full description on the dataset page: https://huggingface.co/datasets/lxucs/tele-lens.
| 22
| 22
|
[
"task_categories:question-answering",
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"arxiv:2602.02103",
"region:us",
"llm",
"qa",
"parity"
] | 2026-02-03T07:05:12
| null | null |
675103d813aa765a04ace26f
|
SWE-Arena/vote_data
|
SWE-Arena
|
nan
| false
|
False
| 2026-01-25T17:18:14
| 39
| 17
| false
|
2efc84d5525a3fea718043670158dc0f1eb4b2e7
| null | 122
| 2,224
|
[
"size_categories:n<1K",
"modality:text",
"region:us"
] | 2024-12-05T01:37:28
| null | null |
6965ea3edc3429e12c3e5f93
|
tencent/HY3D-Bench
|
tencent
|
{"license": "other", "size_categories": ["n>1T"]}
| false
|
False
| 2026-02-05T06:24:10
| 17
| 17
| false
|
505ccc038dee9ac75e1ca2e69acf3eea1cb3ba98
|
๐ฅ News
Feb 04, 2026: ๐ We release HY3D-Bench - a comprehensive collection of high-quality 3D datasets with 3 DIFFERENT subsets!
โจ Full-level Dataset: 252K+ watertight meshes with multi-view renderings and sampled points
โจ Part-level Dataset: 240K+ objects with fine-grained part decomposition
โจ Synthetic Dataset: 125K+ AI-synthesized objects across 1,252 categories
๐ Baseline Model: Hunyuan3D-Shape-v2-1 Small (0.8B DiT) trained on our Full-level dataโฆ See the full description on the dataset page: https://huggingface.co/datasets/tencent/HY3D-Bench.
| 16
| 16
|
[
"license:other",
"size_categories:n>1T",
"arxiv:2602.03907",
"arxiv:2509.06784",
"arxiv:2509.08643",
"arxiv:2509.21245",
"arxiv:2506.15442",
"arxiv:2501.12202",
"arxiv:2411.02293",
"region:us"
] | 2026-01-13T06:46:22
| null | null |
68e91c24e825003e1c2aec1a
|
SWE-Arena/leaderboard_data
|
SWE-Arena
|
nan
| false
|
False
| 2026-01-24T19:43:10
| 48
| 15
| false
|
8f7931b7b18b553f5a5d4d695d7a4fb0dfd08d81
| null | 945
| 1,980
|
[
"region:us"
] | 2025-10-10T14:45:56
| null | null |
6938038933eda94c0094c844
|
raidium/RadImageNet-VQA
|
raidium
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["1K<n<10M"], "task_categories": ["visual-question-answering"], "tags": ["medical"], "pretty_name": "RadImageNet-VQA", "dataset_info": [{"config_name": "alignment", "features": [{"name": "image", "dtype": "image"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "metadata", "struct": [{"name": "content_type", "dtype": "string"}, {"name": "correct_text", "dtype": "null"}, {"name": "is_abnormal", "dtype": "bool"}, {"name": "location", "dtype": "string"}, {"name": "modality", "dtype": "string"}, {"name": "pathology", "dtype": "string"}, {"name": "question_id", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 29401649909, "num_examples": 750009}, {"name": "val", "num_bytes": 3175441830, "num_examples": 83668}], "download_size": 38405331105, "dataset_size": 32577091739}, {"config_name": "benchmark", "features": [{"name": "image", "dtype": "image"}, {"name": "question", "dtype": "string"}, {"name": "choices", "list": "string"}, {"name": "answer", "dtype": "string"}, {"name": "question_type", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "content_type", "dtype": "string"}, {"name": "correct_text", "dtype": "string"}, {"name": "is_abnormal", "dtype": "bool"}, {"name": "location", "dtype": "string"}, {"name": "modality", "dtype": "string"}, {"name": "pathology", "dtype": "string"}, {"name": "question_id", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 414947216, "num_examples": 9000}], "download_size": 361133763, "dataset_size": 414947216}, {"config_name": "instruct", "features": [{"name": "image", "dtype": "image"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "metadata", "struct": [{"name": "content_type", "dtype": "string"}, {"name": "correct_text", "dtype": "string"}, {"name": "is_abnormal", "dtype": "bool"}, {"name": "location", "dtype": "string"}, {"name": "modality", "dtype": "string"}, {"name": "pathology", "dtype": "string"}, {"name": "question_id", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 29904541796, "num_examples": 750009}, {"name": "val", "num_bytes": 3231558586, "num_examples": 83668}], "download_size": 38424398344, "dataset_size": 33136100382}], "configs": [{"config_name": "alignment", "data_files": [{"split": "train", "path": "alignment/train-*"}, {"split": "val", "path": "alignment/val-*"}]}, {"config_name": "instruct", "data_files": [{"split": "train", "path": "instruct/train-*"}, {"split": "val", "path": "instruct/val-*"}]}, {"config_name": "benchmark", "data_files": [{"split": "test", "path": "benchmark/test-*"}]}], "extra_gated_prompt": "### RADIMAGENET LLC Dataset Research Use Agreement\n \n1. RadImageNet grants you permission, upon your agreeing to the terms of the Research Use Agreement, to view and use the Dataset for personal, non-commercial (e.g., academic) research purposes only. Any commercial use, sale, or other monetization, by you or your affiliates, is strictly prohibited under any and all circumstances.\n2. Other than any limited rights expressly granted herein to you, RadImageNet retains all rights, title, and interest in the Dataset.\n3. You may make a verbatim copy of the Dataset for non-commercial research use as permitted in the Research Use Agreement. You may not alter this verbatim copy for any reason. If another user within your organization wishes to use the Dataset, they must register as an individual user and comply with all the terms of the Research Use Agreement.\n4. YOU MAY NOT DISTRIBUTE, PUBLISH, OR REPRODUCE A COPY of any portion, including the entirety, of the Dataset to anyone without express and specific prior written permission from RadImageNet.\n5. YOU MAY NOT SHARE THE DOWNLOAD LINK to the Dataset with others. For example, if someone other than you within your organization wishes to use or view the Dataset, they must register as an individual user and agree to and comply with all the terms of the Research Use Agreement.\n6. You must not modify, reverse engineer, decompile, or create derivative works from the Dataset. You must not remove or alter any copyright or other proprietary notices in the Dataset.\n7. The Dataset has not been reviewed or approved by the Food and Drug Administration, or any other regulatory agency of the United States of America. The Dataset is being provided to you strictly and only for non-clinical, research use. In no event shall data or images generated through the use, directly or indirectly, in whole or in part, of the Dataset be used or relied upon in the diagnosis or provision of patient care. This Research Use Agreement expressly forbids the use, directly or indirectly, in whole or in part, of the Dataset in the diagnosis or provision of patient care.\n8. THE DATASET IS PROVIDED \u201cAS IS,\u201d AND RADIMAGENET AND ITS COLLABORATORS MAKE NO WARRANTY, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY PARTICULAR PURPOSE,2 NOR DO THEY ASSUME ANY LIABILITY OR RESPONSIBILITY FOR THE USE OF THE DATASET.\n9. You will not attempt to identify or re-identify any of the individual data subjects (e.g., patients). Identification or re-identification of individuals is strictly prohibited. Any identification or re-identification of any individual data subject shall be immediately reported to RadImageNet and may be subject to immediate termination of the use of the Dataset.\n\n10. Any violation of the Research Use Agreement or other impermissible use shall be grounds for immediate termination of use of the Dataset. It is your duty to promptly report to RadImageNet any knowledge of any violation at any time. In the event that RadImageNet determines that you have violated this Research Use Agreement or made other impermissible use of the Dataset, RadImageNet may direct that you immediately return all copies of the Dataset and retain no copies thereof. RadImageNet may do this even if you did not cause the violation or impermissible use.\n\nIn consideration for your agreement to the terms and conditions contained in the Research Use Agreement, RadImageNet grants you limited permission to view and use the Dataset for personal, non-commercial research, as described herein. You may not otherwise copy, reproduce, retransmit, distribute, publish, commercially exploit or otherwise transfer any material from or related to the Dataset.\n#### Limitation of Use\nYou may use the Dataset for legal purposes only.\n#### Indemnification\nYou agree to indemnify and hold RadImageNet harmless from and not liable in any way for any claims, losses or damages, including legal fees, arising out of or resulting from your use of the Dataset or your violation or role in violation of the Research Use Agreement. You agree to fully cooperate in RadImageNet\u2019s defense against any such claims. These terms and all other terms of the Research Use Agreement shall be governed by and interpreted in accordance with the laws of New York State.", "extra_gated_fields": {"Name": "text", "Title": "text", "Date": "date_picker", "By clicking Submit below I accept the terms of this RADIMAGENET LLC Dataset Research Use Agreement (hereinafter \u201cthe Research Use Agreement\u201d), as well as to the Terms of Use of the RADIMAGENET LLC (hereinafter \u201cRadImageNet\u201d) website as posted and updated periodically": "checkbox"}, "extra_gated_button_content": "Submit"}
| false
|
auto
| 2025-12-19T10:06:57
| 74
| 15
| false
|
fe2154107adfd74f5b8218be6d2b3b127b668d32
|
RadImageNet-VQA: A Large-Scale CT and MRI Dataset for Radiologic Visual Question Answering
๐ Paper
Dataset Details
We introduce RadImageNet-VQA, a large-scale dataset designed for training and benchmarking radiologic VQA on CT and MRI exams. Built from the CT/MRI subset of RadImageNet and its expert-curated anatomical and pathological annotations, RadImageNet-VQA provides 750K images with 7.5M generated samples, including 750K medical captions for visual-textโฆ See the full description on the dataset page: https://huggingface.co/datasets/raidium/RadImageNet-VQA.
| 1,968
| 2,254
|
[
"task_categories:visual-question-answering",
"language:en",
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"medical"
] | 2025-12-09T11:10:01
| null | null |
68072cc4cce05035af98207e
|
nvidia/OpenMathReasoning
|
nvidia
|
{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["question-answering", "text-generation"], "pretty_name": "OpenMathReasoning", "tags": ["math", "nvidia"], "configs": [{"config_name": "default", "data_files": [{"split": "cot", "path": "data/cot-*"}, {"split": "tir", "path": "data/tir-*"}, {"split": "genselect", "path": "data/genselect-*"}, {"split": "additional_problems", "path": "data/additional_problems-*"}]}], "dataset_info": {"features": [{"name": "expected_answer", "dtype": "string"}, {"name": "problem_type", "dtype": "string"}, {"name": "problem_source", "dtype": "string"}, {"name": "generation_model", "dtype": "string"}, {"name": "pass_rate_72b_tir", "dtype": "string"}, {"name": "problem", "dtype": "string"}, {"name": "generated_solution", "dtype": "string"}, {"name": "inference_mode", "dtype": "string"}, {"name": "used_in_kaggle", "dtype": "bool"}], "splits": [{"name": "cot", "num_bytes": 71639174648, "num_examples": 3201061}, {"name": "tir", "num_bytes": 35746562996, "num_examples": 1718466}, {"name": "genselect", "num_bytes": 6981124435, "num_examples": 565620}, {"name": "additional_problems", "num_bytes": 66328865, "num_examples": 193170}], "download_size": 49585391985, "dataset_size": 114433190944}}
| false
|
False
| 2025-05-27T18:43:44
| 436
| 14
| false
|
d3d08664755704f422af97d43a7ff0ded4bd95df
|
OpenMathReasoning
OpenMathReasoning is a large-scale math reasoning dataset for training large language models (LLMs).
This dataset contains
306K unique mathematical problems sourced from AoPS forums with:
3.2M long chain-of-thought (CoT) solutions
1.7M long tool-integrated reasoning (TIR) solutions
566K samples that select the most promising solution out of many candidates (GenSelect)
Additional 193K problems sourced from AoPS forums (problems only, no solutions)
We usedโฆ See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenMathReasoning.
| 14,273
| 158,333
|
[
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2504.16891",
"region:us",
"math",
"nvidia"
] | 2025-04-22T05:44:36
| null | null |
6835e8703de5738a2e9af4ae
|
nvidia/PhysicalAI-Autonomous-Vehicles
|
nvidia
|
{"extra_gated_heading": "You must agree to the NVIDIA Autonomous Vehicle Dataset License Agreement to access this dataset.", "extra_gated_prompt": "### NVIDIA Autonomous Vehicle Dataset License Agreement\n\nThis NVIDIA Autonomous Vehicle Dataset License Agreement (\"Agreement\") is a legal agreement between you, whether an individual or entity (\"you\") and NVIDIA Corporation with address 2788 San Tomas Expressway, Santa Clara, California 95051 (\"NVIDIA\") and governs the use of certain datasets, including any annotations and metadata attached to the datasets, provided by NVIDIA (\"Dataset\").\n\nThis Agreement can be accepted only by an adult of legal age of majority in the country in which the Dataset are used.\n\nIf you don't have the required age or authority to accept this Agreement or if you don't accept all the terms and conditions of this Agreement, do not use the Dataset.\n\nYou agree to use the Dataset only for purposes expressly permitted by this Agreement and in accordance with any applicable law or regulation in the relevant jurisdictions.\n\n1. License Grant. Subject to the terms of this Agreement, NVIDIA grants you a non-exclusive, revocable, non-transferable, non-sublicensable (except as expressly granted in Sections 1 and 2 of this Agreement, license to download, use, modify, and reproduce the Dataset, in each case solely for your internal development of autonomous vehicles and automated driving assisted systems using NVIDIA technology (\"Purpose\"). NVIDIA may from time to time update the Dataset. If requested by NVIDIA, you will use the updated version of any such Dataset and delete any prior versions upon NVIDIA's written request.\n\n2. Authorized Users. You may allow your Affiliates' employees and contractors (all such users collectively \"Authorized Users\") to access and use the Dataset from your secure network for the Purpose on your behalf. You are responsible for the compliance with the terms of this Agreement by your authorized users. Any act or omission by your authorized users that if committed by you would constitute a breach of this Agreement will be deemed to constitute a breach of this Agreement. \"Affiliates\" means an entity that owns or controls, is owned or controlled by, or is under common ownership or control with you, where \"control\" is the possession, directly or indirectly, of the power to direct or cause the direction of the management and policies of an entity, whether through ownership of voting securities, by contract or otherwise.\n\n3. Confidentiality. You agree that you will not use, nor authorize others to use, NVIDIA Confidential Information, other than for the Purpose, and that you will not disclose NVIDIA Confidential Information to any third party, except to Authorized Users under this Agreement that have a need to know such Confidential Information for the Purpose, provided that each such recipient is subject to a written agreement that includes confidentiality obligations consistent with the terms. You will protect the NVIDIA Confidential Information with at least the same degree of care that you use to protect your own similar confidential and proprietary information, but no less than a reasonable degree of care, including any appropriate technical, organizational and contractual measures. \"Confidential Information\" means the Dataset including its features and functionality, output, and any results of benchmarking or other competitive analysis or regression or performance data relating to the Dataset.\n\n4. Limitations. Your license to use the Dataset is restricted as follows:\n\n4.1 You will not use the Dataset for the purpose of any surveillance program, service and/or product of public authorities, corporations and/or citizens that monitors the behavior of an individual person or groups of persons in any unethical manner. You will not use the Dataset to directly or indirectly enable law enforcement or any public authority to enforce any rules or regulations including any road traffic laws.\n\n4.2 You may not change or remove copyright or other proprietary notices in the Dataset.\n\n4.3 The rights granted to you in Section 1 and 2 are for the Purpose only. You may not use the Dataset for any other purpose.\n\n4.4 You may not identify or attempt to identify or profile any individual (including by way of license plate numbers) in the Dataset or de-anonymize or attempt to de-anonymize any Dataset. This includes prohibition against processing of license plate numbers for purpose of tracking or collecting data about a vehicle over time and across different frames.\n\n4.5 You may not: (a) infer, measure, detect or otherwise label the race, ethnicity, gender, age or health (or any other sensitive attributes) of individuals in the Dataset, (b) perform biometric processing of the Dataset, (c) analyze faces, gazes, eye movements, gait, or body movements to uniquely identify persons, or (d) use the Dataset to develop or evaluate any identity, emotion recognition technology or social scoring technology.\n\n4.6 You may not create derivative works of the Dataset, sell, rent, sublicense, transfer, distribute, embed, or host the Dataset (in whole or in part), or otherwise make the Dataset (in whole or in part) available to others.\n\n4.7 You may not bypass, disable or circumvent any technical limitation, encryption, security, digital rights management or authentication mechanism relating to the Dataset.\n\n4.8 You must keep track of any copies of the Dataset. You will keep track of where the Dataset or portions of it are stored to ensure these restrictions follow such Dataset.\n\n4.9 While NVIDIA has exercised reasonable efforts to anonymize the Dataset, you must cooperate with NVIDIA to honor any data subject rights where applicable. You will delete the Dataset upon written notice by NVIDIA and you will promptly notify NVIDIA at https://www.nvidia.com/en-us/support/submit-security-vulnerability/ if you notice that any portion of the Dataset is not sufficiently anonymized.\n\n5. AI Ethics.\n\n5.1 Ethical Use. NVIDIA is committed to safety, trust and transparency in AI development. NVIDIA encourages you to (a) ensure that the product or service you develop, use, offer as a service or distribute meets the legal and ethical requirements of the relevant industry or use case, (b) take reasonable measures to address unintended bias and to mitigate harm to others, including underrepresented or vulnerable groups, and (c) inform users of the nature and limitations of the product or service.\n\n5.2 Prohibited Uses. NVIDIA expressly prohibits the use of its products or services for any purpose in violation of applicable law or regulation, including but not limited to (a) illegal surveillance, (b) illegal collection or processing of biometric information without the consent of the subject where required under applicable law, or (c) illegal harassment, abuse, threatening or bullying of individuals or groups of individuals or intentionally misleading or deceiving others.\n\n6. Ownership. The Dataset, including all intellectual property rights, is and will remain the sole and exclusive property of NVIDIA or its licensors. Except as expressly granted in this Agreement, (i) NVIDIA reserves all rights, interests and remedies in connection with the Dataset, and (ii) no other license or right is granted to you by implication, estoppel or otherwise.\n\n7. Feedback. You may, but are not obligated to, provide suggestions, requests, fixes, modifications, enhancements, or other feedback regarding or in connection with your use of the Dataset (collectively, \"Feedback\"). Feedback, even if designated as confidential by you, will not create any confidentiality obligation for NVIDIA or its affiliates. If you provide Feedback, you hereby grant NVIDIA, its affiliates and its designees a nonexclusive, perpetual, irrevocable, sublicensable, worldwide, royalty-free, fully paid-up and transferable license, under your intellectual property rights, to publicly perform, publicly display, reproduce, use, make, have made, sell, offer for sale, distribute (through multiple tiers of distribution), import, create derivative works of and otherwise commercialize and exploit the Feedback at NVIDIA's discretion.\n\n8. Term and Termination. This Agreement expires twelve (12) months after the date of initial delivery or download of the Dataset. This Agreement will automatically terminate (a) if you fail to comply with any of the terms in this Agreement or (b) if you commence or participate in any legal proceeding against NVIDIA with respect to the Dataset. Upon termination, you must stop using and destroy all copies of the Dataset. Upon written request, you will certify in writing that you have complied with your commitments under this section. All provisions will survive termination, except for the licenses granted to you.\n\n9. Disclaimer of Warranties. THE DATASET IS PROVIDED BY NVIDIA AS-IS AND WITH ALL FAULTS. TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, NVIDIA DISCLAIMS ALL WARRANTIES AND REPRESENTATIONS OF ANY KIND, WHETHER EXPRESS, IMPLIED OR STATUTORY, RELATING TO OR ARISING UNDER THIS AGREEMENT, INCLUDING, WITHOUT LIMITATION, THE WARRANTIES OF TITLE, NONINFRINGEMENT, MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, USAGE OF TRADE AND COURSE OF DEALING.\n\n10. Limitations of Liability. TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE, WILL NVIDIA BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES OF ANY TYPE ARISING OUT OF OR AS A RESULT OF THIS AGREEMENT OR THE USE OR INABILITY TO USE THE SOFTWARE (INCLUDING BUT NOT LIMITED TO DAMAGES FOR LOSS OF GOODWILL, WORK STOPPAGE, COMPUTER FAILURE OR MALFUNCTION, OR ANY AND ALL OTHER DAMAGES OR LOSSES), EVEN IF NVIDIA HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.\n\n11. Governing Law and Jurisdiction. This Agreement will be governed in all respects by the laws of the United States and the laws of the State of Delaware, without regard to conflict of laws principles or the United Nations Convention on Contracts for the International Sale of Goods. The state and federal courts residing in Santa Clara County, California will have exclusive jurisdiction over any dispute or claim arising out of or related to this Agreement, and the parties irrevocably consent to personal jurisdiction and venue in those courts; except that either party may apply for injunctive remedies or an equivalent type of urgent legal relief in any jurisdiction.\n\n12. Indemnity. You agree to defend, indemnify and hold harmless NVIDIA and its affiliates, and their respective employees, contractors, agents, officers and directors, from and against any and all claims, damages, obligations, losses, liabilities, costs or debt, fines, restitutions and expenses (including but not limited to attorney's fees and costs incident to establishing the right of indemnification) arising out of or related to your use of the Dataset outside of the scope of this Agreement, or not in compliance with its terms.\n\n13. General.\n13.1 No Assignment. NVIDIA may assign, delegate or transfer its rights or obligations under this Agreement by any means or operation of law. You may not, without NVIDIA's prior written consent, assign, delegate or transfer any of your rights or obligations under this Agreement by any means or operation of law, and any attempt to do so is null and void.\n\n13.2 No Waiver. No waiver of any term of the Agreement will be deemed a further or continuing waiver of such term or any other term, and NVIDIA's failure to assert any right or provision under the Agreement will not constitute a waiver of such right or provision.\n\n13.3 Trade and Compliance. You agree to with all applicable export, import, trade and economic sanctions laws and regulations, as amended, including without limitation U.S. Export Administration Regulations and Office of Foreign Assets Control regulations. Any violation of such laws by you will void any warranty for the associated products and technologies. You confirm (a) your understanding that export or reexport of certain NVIDIA products or technologies may require a license or other approval from appropriate authorities and (b) that you will not export or reexport any products or technology, directly or indirectly, without first obtaining any required license or other approval from appropriate authorities, (i) to any countries that are subject to any U.S. or local export restrictions (currently including, but not necessarily limited to, Belarus, Cuba, Iran, North Korea, Russia, Syria, the Region of Crimea, Donetsk People's Republic Region and Luhansk People's Republic Region); (ii) to any end-user who it knows or has reason to know will utilize them in the design, development or production of nuclear, chemical or biological weapons, missiles, rocket systems, unmanned air vehicles capable of a maximum range of at least 300 kilometers, regardless of payload, or intended for military end-use, or any weapons of mass destruction; (iii) to any end-user who has been prohibited from participating in the U.S. or local export transactions by any governing authority; or (iv) to any known military or military-intelligence end-user or for any known military or military-intelligence end-use in accordance with U.S. trade compliance laws and regulations..\n\n13.4 Notices. Please direct your legal notices or other correspondence to NVIDIA Corporation, 2788 San Tomas Expressway, Santa Clara, California 95051, United States of America, Attention: Legal Department, with a copy emailed to legalnotices@nvidia.com. If NVIDIA needs to contact you about the Dataset, you consent to receive the notices by email and agree that such notices will satisfy any legal communication requirements.\n\n13.5 Force Majeure. Neither party will be liable during any period where an event or circumstance prevents or delays that party from performing its obligations under this Agreement and that event or circumstance: (i) is not within the reasonable control of that party and is not the result of that party's negligence, and (ii) cannot be overcome or avoided by that party using reasonably diligent efforts.\n\n13.6 Severability and Amendment. If a court of competent jurisdiction rules that a provision of this Agreement is unenforceable, that provision will be deemed modified to the extent necessary to make it enforceable and the remainder of this Agreement will continue in full force and effect. Any amendment to this Agreement must be in writing and signed by authorized representatives of both parties.\n\n13.7 Independent Contractors. The parties are independent contractors, and this Agreement does not create a joint venture, partnership, agency or other form of business association between the parties. Neither party will have the power to bind the other party or incur any obligation on its behalf without the other party's prior written consent.\n\n13.8 Construction. The headings in the Agreement are included solely for convenience and are not intended to affect the meaning or interpretation of the Agreement. As required by the context of the Agreement, the singular of a term includes the plural and vice versa.\n\n13.9 Entire Agreement. Regarding the subject matter of this Agreement, the parties agree that (i) this Agreement constitutes the entire and exclusive agreement between the parties and supersedes all prior and contemporaneous communications and (ii) any additional or different terms or conditions, whether contained in purchase orders, order acknowledgments, invoices or otherwise, will not be binding and are null and void.", "extra_gated_button_content": "I accept the terms of the NVIDIA Autonomous Vehicle Dataset License Agreement", "license": "other", "license_name": "nvidia-av-dataset", "license_link": "https://huggingface.co/datasets/nvidia/PhysicalAI-Autonomous-Vehicles/blob/main/LICENSE.pdf", "viewer": false}
| false
|
auto
| 2026-01-21T15:02:05
| 735
| 14
| false
|
2ae73f49ffd2b5db43b404201beb7b92889f7afc
|
PhysicalAI Autonomous Vehicles
Dataset Description
The PhysicalAI-Autonomous-Vehicles dataset provides one of the largest, most geographically diverse collections of
multi-sensor data empowering AV researchers to build the next generation of Physical AI based end-to-end driving systems.
This dataset has a total of 1700 hours of driving recorded from planned data-collection drives in 25 countries and 2500+ cities.
The data captures diverse traffic, weather conditionsโฆ See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Autonomous-Vehicles.
| 240,212
| 608,708
|
[
"license:other",
"region:us"
] | 2025-05-27T16:29:36
| null | null |
695df55a4e351abe5277cca5
|
UniParser/OmniScience
|
UniParser
|
{"license": "cc-by-nc-sa-4.0", "task_categories": ["image-to-text"], "extra_gated_heading": "Request Access to This Dataset", "extra_gated_description": "Please complete the required fields below to request access. Access will be automatically granted upon submission.", "extra_gated_fields": {"Full Name": {"type": "text"}, "Email": {"type": "text"}, "Affiliation (Company / University)": {"type": "text"}, "I agree this dataset is for non-commercial use ONLY": {"type": "checkbox"}}, "extra_gated_button_content": "Submit Access Request"}
| false
|
auto
| 2026-01-22T02:55:43
| 115
| 14
| false
|
9c9fdac9ea87b36e3889330463cd4aee2e81ce95
|
OmniScience: A Large-scale Dataset for Scientific Image Understanding
๐ 2026-01-21: The OmniScience dataset ranked Top 8 on Hugging Face Datasets Trending (Top 1 on Image Caption Filed). ๐ 2026-01-17: The OmniScience dataset surpassed 5,000 downloads within 5 days of its release. ๐ 2026-01-12: Official release of the OmniScience dataset. ๐ 2025-06-01: Completion of the original dataset collection.
๐ Dataset Summary
OmniScience is an ultra-large-scaleโฆ See the full description on the dataset page: https://huggingface.co/datasets/UniParser/OmniScience.
| 9,022
| 9,039
|
[
"task_categories:image-to-text",
"license:cc-by-nc-sa-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"format:optimized-parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2512.15098",
"region:us"
] | 2026-01-07T05:55:38
| null | null |
6973555c00a6884fc265fa5d
|
GAIR/daVinci-Dev
|
GAIR
|
{"pretty_name": "daVinci-Dev (Agent-native Trajectories)", "language": ["en"], "size_categories": ["1M<n<10M"], "license": "other", "license_name": "mixed-permissive-and-cc-by-4.0", "license_link": "https://creativecommons.org/licenses/by/4.0/", "tags": ["software-engineering", "agent", "pull-request", "code", "synthetic", "trajectory", "patch", "github", "python"], "extra_gated_prompt": "By requesting access, you agree to the terms of the license and to cite the dataset in any resulting publications.", "extra_gated_heading": "Please provide your full legal name and organization details. Avoid using acronyms where possible. Failure to provide accurate information may result in access denial.", "extra_gated_button_content": "Submit Request", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Organization": "text", "Country": "country", "Job Title": {"type": "select", "options": ["Student", "Researcher", "AI Developer/Engineer", "Data Scientist", "Reporter", "Other"]}, "Intended Use": {"type": "select", "options": ["Research", "Commercial", "Education", "Other"]}, "geo": "ip_location", "I agree to cite this dataset": "checkbox", "I accept the license terms": "checkbox"}, "configs": [{"config_name": "filtered_repos", "data_files": [{"split": "train", "path": "ctx-native/filtered_repos/*.parquet"}]}, {"config_name": "filtered_prs", "data_files": [{"split": "train", "path": "ctx-native/filtered_prs/*.parquet"}]}, {"config_name": "llm_enhanced_prs", "data_files": [{"split": "train", "path": "ctx-native/llm_enhanced_prs/*.parquet"}]}, {"config_name": "env_native", "data_files": [{"split": "train", "path": "env-native.jsonl"}]}]}
| false
|
auto
| 2026-01-31T05:45:31
| 17
| 14
| false
|
7df0a81973e77c01633ce648d59effa1f0a1da03
|
daVinci-Dev Dataset: Agent-native Mid-training for Software Engineering
This dataset release contains agent-native trajectories used in daVinci-Dev: Agent-native Mid-training for Software Engineering.
Dataset at a glance
It includes two complementary data sources:
Contextually-native trajectories Dpyctx\mathcal{D}^{\text{ctx}}_{\text{py}}Dpyctxโ(PR-derived, Python Variant)
Constructed from GitHub pull requests.
We only include PRs from repositoriesโฆ See the full description on the dataset page: https://huggingface.co/datasets/GAIR/daVinci-Dev.
| 8,500
| 8,500
|
[
"language:en",
"license:other",
"size_categories:1M<n<10M",
"modality:tabular",
"modality:text",
"arxiv:2601.18418",
"region:us",
"software-engineering",
"agent",
"pull-request",
"code",
"synthetic",
"trajectory",
"patch",
"github",
"python"
] | 2026-01-23T11:02:52
| null | null |
6974c6dab3d0d1206ca7bbbf
|
AI45Research/ATBench
|
AI45Research
|
{"license": "apache-2.0", "tags": ["agent"], "size_categories": ["n<1K"]}
| false
|
False
| 2026-01-27T03:54:40
| 25
| 14
| false
|
10804df8221ba41c64a767dacae6ab9328d07499
|
ATBench: Agent Trajectory Safety and Security Benchmark
โญ Githubย ย | ย ย
๐ Technical Reportย ย | ย ย
๐ค Hugging Faceย ย
ATBench is a trajectory-level benchmark for evaluating agentic safety in realistic, long-horizon interactions. It contains 500 annotated execution trajectories (250 safe / 250 unsafe) with multi-turn interactions (avg. 8.97 turns) and 1,575 unique tools. The benchmark provides taxonomy-grounded, fine-grained safety annotations, enabling precise riskโฆ See the full description on the dataset page: https://huggingface.co/datasets/AI45Research/ATBench.
| 731
| 731
|
[
"license:apache-2.0",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2601.18491",
"region:us",
"agent"
] | 2026-01-24T13:19:22
| null | null |
End of preview. Expand
in Data Studio
Changelog
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 โ
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
- 4,690
Size of downloaded dataset files:
1.72 GB
Size of the auto-converted Parquet files:
1.72 GB
Number of rows:
4,118,586