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695138b5329f4825326ac6c8
|
facebook/research-plan-gen
|
facebook
|
{"configs": [{"config_name": "ml", "data_files": [{"split": "train", "path": "ml/train/*.parquet"}, {"split": "test", "path": "ml/test/*.parquet"}]}, {"config_name": "arxiv", "data_files": [{"split": "train", "path": "arxiv/train/*.parquet"}, {"split": "test", "path": "arxiv/test/*.parquet"}]}, {"config_name": "pubmed", "data_files": [{"split": "train", "path": "pubmed/train/*.parquet"}, {"split": "test", "path": "pubmed/test/*.parquet"}]}]}
| false
|
False
| 2026-01-02T14:56:26
| 178
| 178
| false
|
8ae1ba08759afd32b20eb959ea6addf25c2c0929
|
RPG Dataset
Research Plan Generation dataset with three subsets covering ML, Arxiv, and PubMed research papers. Each subset contains research tasks with evaluation rubrics and reference solutions.
Dataset Statistics
Subset
Train
Test
Total
ML
6,872
685
7,557
Arxiv
6,573
1,496
8,069
Pubmed
6,423
464
6,887
Total
19,868
2,645
22,513
Loading the Dataset
from datasets import load_dataset
# Load a specific subset
ml_data =… See the full description on the dataset page: https://huggingface.co/datasets/facebook/research-plan-gen.
| 1,428
| 1,428
|
[
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2512.23707",
"region:us"
] | 2025-12-28T14:03:33
| null | null |
6942194f0276e439f501108f
|
bigai/TongSIM-Asset
|
bigai
|
{"tags": ["3D"], "viewer": false}
| false
|
False
| 2025-12-29T13:37:50
| 257
| 120
| false
|
163b8d30bf44d23f6a906854128277a150eb3e06
|
TongSIM
GitHub
Visit Github : https://github.com/bigai-ai/tongsim
What is TongSIM-Asset?
As artificial intelligence (AI) rapidly advances, especially in multimodal large language models, research focus is shifting from single-modality text processing to the more complex domains of multimodal and embodied AI. Embodied intelligence focuses on training agents within realistic simulated environments, leveraging physical interaction and action… See the full description on the dataset page: https://huggingface.co/datasets/bigai/TongSIM-Asset.
| 16,159
| 16,159
|
[
"arxiv:2512.20206",
"region:us",
"3D"
] | 2025-12-17T02:45:35
| null | null |
6942c26a0c5ab8715b67e281
|
bshada/open-schematics
|
bshada
|
{"license": "cc-by-4.0", "task_categories": ["text-generation", "image-to-text", "text-to-image"], "language": ["en"], "tags": ["electronics", "schematics", "kicad", "hardware", "pcb", "circuit-design", "engineering"], "size_categories": ["10K<n<100K"]}
| false
|
False
| 2025-12-17T16:52:57
| 127
| 75
| false
|
757ba4c58acf9dcada1cfddd0e93da60e280a871
|
Open Schematics Dataset
A comprehensive dataset of electronic schematics from hardware projects. This dataset is designed for training AI models on circuit design, component recognition, and hardware engineering tasks.
Dataset Description
This dataset contains electronic schematic files along with their visual representations, component information, and metadata from various hardware projects.
Dataset Structure
Each record in the dataset contains:
schematic:… See the full description on the dataset page: https://huggingface.co/datasets/bshada/open-schematics.
| 7,876
| 7,876
|
[
"task_categories:text-generation",
"task_categories:image-to-text",
"task_categories:text-to-image",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"electronics",
"schematics",
"kicad",
"hardware",
"pcb",
"circuit-design",
"engineering"
] | 2025-12-17T14:47:06
| null | null |
6532270e829e1dc2f293d6b8
|
gaia-benchmark/GAIA
|
gaia-benchmark
|
{"language": ["en"], "pretty_name": "General AI Assistants Benchmark", "extra_gated_prompt": "To avoid contamination and data leakage, you agree to not reshare this dataset outside of a gated or private repository on the HF hub.", "extra_gated_fields": {"I agree to not reshare the GAIA submissions set according to the above conditions": "checkbox"}, "configs": [{"config_name": "2023_all", "data_files": [{"split": "test", "path": "2023/test/metadata.parquet"}, {"split": "validation", "path": "2023/validation/metadata.parquet"}]}, {"config_name": "2023_level1", "data_files": [{"split": "test", "path": "2023/test/metadata.level1.parquet"}, {"split": "validation", "path": "2023/validation/metadata.level1.parquet"}]}, {"config_name": "2023_level2", "data_files": [{"split": "test", "path": "2023/test/metadata.level2.parquet"}, {"split": "validation", "path": "2023/validation/metadata.level2.parquet"}]}, {"config_name": "2023_level3", "data_files": [{"split": "test", "path": "2023/test/metadata.level3.parquet"}, {"split": "validation", "path": "2023/validation/metadata.level3.parquet"}]}]}
| false
|
auto
| 2025-10-28T14:44:54
| 574
| 68
| false
|
682dd723ee1e1697e00360edccf2366dc8418dd9
|
GAIA dataset
GAIA is a benchmark which aims at evaluating next-generation LLMs (LLMs with augmented capabilities due to added tooling, efficient prompting, access to search, etc).
We added gating to prevent bots from scraping the dataset. Please do not reshare the validation or test set in a crawlable format.
Data and leaderboard
GAIA is made of more than 450 non-trivial question with an unambiguous answer, requiring different levels of tooling and autonomy to solve. It… See the full description on the dataset page: https://huggingface.co/datasets/gaia-benchmark/GAIA.
| 16,048
| 141,575
|
[
"language:en",
"size_categories:n<1K",
"format:parquet",
"modality:audio",
"modality:document",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2311.12983",
"region:us"
] | 2023-10-20T07:06:54
| null | null |
68d11fc995f5667938a638b3
|
nebius/SWE-rebench-openhands-trajectories
|
nebius
|
{"license": "cc-by-4.0", "tags": ["code", "synthetic", "tools", "agents", "software"], "size_categories": ["10K<n<100K"]}
| false
|
False
| 2025-12-27T14:08:07
| 99
| 64
| false
|
35455389ab51bf5e2306bfd436ef72d0f98bf882
|
Dataset Summary
SWE-rebench-OpenHands-Trajectories is a dataset of multi-turn agent trajectories for software engineering tasks, collected
using Qwen/Qwen3-Coder-480B-A35B-Instruct with OpenHands (v0.54.0) agent scaffolding.
This dataset captures complete agent execution traces as they attempt to resolve real GitHub issues from
nebius/SWE-rebench.
Each trajectory contains the agent's step-by-step reasoning, actions, and environmental observations.
Metric… See the full description on the dataset page: https://huggingface.co/datasets/nebius/SWE-rebench-openhands-trajectories.
| 435
| 510
|
[
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"code",
"synthetic",
"tools",
"agents",
"software"
] | 2025-09-22T10:07:05
| null | null |
621ffdd236468d709f184284
|
wikimedia/wikipedia
|
wikimedia
|
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{"config_name": "20231101.zh-classical", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14869227, "num_examples": 12708}], "download_size": 10098073, "dataset_size": 14869227}, {"config_name": "20231101.zh-min-nan", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 153672031, "num_examples": 432798}], "download_size": 37122048, "dataset_size": 153672031}, {"config_name": "20231101.zh-yue", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 109936351, "num_examples": 134140}], "download_size": 64950815, "dataset_size": 109936351}, {"config_name": "20231101.zu", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7088246, "num_examples": 11561}], "download_size": 3792429, "dataset_size": 7088246}], "language_bcp47": ["be-tarask", "en-simple"]}
| false
|
False
| 2024-01-09T09:40:51
| 1,061
| 63
| false
|
b04c8d1ceb2f5cd4588862100d08de323dccfbaa
|
Dataset Card for Wikimedia Wikipedia
Dataset Summary
Wikipedia dataset containing cleaned articles of all languages.
The dataset is built from the Wikipedia dumps (https://dumps.wikimedia.org/)
with one subset per language, each containing a single train split.
Each example contains the content of one full Wikipedia article with cleaning to strip
markdown and unwanted sections (references, etc.).
All language subsets have already been processed for recent dump, and you… See the full description on the dataset page: https://huggingface.co/datasets/wikimedia/wikipedia.
| 69,514
| 1,628,772
|
[
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"language:ab",
"language:ace",
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"language:yi",
"language:yo",
"language:yue",
"language:za",
"language:zea",
"language:zgh",
"language:zh",
"language:zu",
"license:cc-by-sa-3.0",
"license:gfdl",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22
| null | null |
693c15fec9b2ec3ac765b0e2
|
HiDream-ai/ReCo-Data
|
HiDream-ai
|
{"language": ["en"], "license": "cc-by-nc-sa-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["image-to-video"], "pretty_name": "ReCo-Data", "tags": ["Video-Text-to-Video", "Video-to-Video", "Video Edit", "Video"]}
| false
|
False
| 2025-12-26T02:45:39
| 76
| 63
| false
|
cd7ddefb5175fd4f15c17dc66fe209285cdae8f5
|
ReCo-Data Dataset Card
Introduction
ReCo-Data is a large-scale, high-quality video editing dataset comprising 500K+ instruction-video pairs. This card provides its statistics, collection pipeline, and dataset format.
1. Dataset Statistics
Statistics
Figure Caption:
(a) Overview of scale
(b) Task distribution showing balanced quantities: Replace (156.6K), Style (130.6K), Remove (121.6K), and Add (115.6K). Human evaluation on 200 randomly… See the full description on the dataset page: https://huggingface.co/datasets/HiDream-ai/ReCo-Data.
| 6,940
| 6,940
|
[
"task_categories:image-to-video",
"language:en",
"license:cc-by-nc-sa-4.0",
"size_categories:1M<n<10M",
"format:webdataset",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"arxiv:2512.17650",
"region:us",
"Video-Text-to-Video",
"Video-to-Video",
"Video Edit",
"Video"
] | 2025-12-12T13:17:50
| null | null |
639244f571c51c43091df168
|
Anthropic/hh-rlhf
|
Anthropic
|
{"license": "mit", "tags": ["human-feedback"]}
| false
|
False
| 2023-05-26T18:47:34
| 1,578
| 60
| false
|
09be8c5bbc57cb3887f3a9732ad6aa7ec602a1fa
|
Dataset Card for HH-RLHF
Dataset Summary
This repository provides access to two different kinds of data:
Human preference data about helpfulness and harmlessness from Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. These data are meant to train preference (or reward) models for subsequent RLHF training. These data are not meant for supervised training of dialogue agents. Training dialogue agents on these data is likely to lead… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/hh-rlhf.
| 23,445
| 1,757,848
|
[
"license:mit",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2204.05862",
"region:us",
"human-feedback"
] | 2022-12-08T20:11:33
| null | null |
68ddcd3569eee4d38fd49fda
|
llm-jp/jhle
|
llm-jp
|
{"license": "mit", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "answer_type", "dtype": "string"}, {"name": "raw_subject", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "canary", "dtype": "string"}], "splits": [{"name": "test", "num_examples": 455}]}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "language": ["ja"]}
| false
|
auto
| 2025-10-06T09:19:42
| 60
| 59
| false
|
ebc1cbba9a3c1c1f06852b8dc360d03b37d7991f
|
[!NOTE]
IMPORTANT: Please help us protect the integrity of this benchmark by not publicly sharing, re-uploading, or distributing the dataset.
Japanese Humanity's Last Exam (JHLE)
This repository provides the Humanity's Last Exam dataset translated into Japanese by LLM-jp, a collaborative project in Japan.
Questions requiring images were excluded, and five questions per raw_subject were randomly sampled.
The dataset was machine-translated and then reviewed and corrected by external… See the full description on the dataset page: https://huggingface.co/datasets/llm-jp/jhle.
| 83
| 161
|
[
"language:ja",
"license:mit",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-10-02T00:54:13
| null | null |
69482ca9edae764cb1afe078
|
lirang04/GroundingME
|
lirang04
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "description", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "bbox", "list": "int64"}, {"name": "detection_type", "dtype": "string"}, {"name": "subtask_l1", "dtype": "string"}, {"name": "subtask_l2", "dtype": "string"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 1532196041, "num_examples": 1005}], "download_size": 1526836865, "dataset_size": 1532196041}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "task_categories": ["zero-shot-object-detection"], "license": "other", "tags": ["multimodal", "visual-grounding", "mllm"]}
| false
|
False
| 2025-12-26T17:25:40
| 70
| 59
| false
|
acc9c4a9495148711a564b805cb9b013499803bc
|
GroundingME: Exposing the Visual Grounding Gap in MLLMs through Multi-Dimensional Evaluation
🔍 Overview
Visual grounding—localizing objects from natural language descriptions—represents a critical bridge between language and vision understanding. While multimodal large language models (MLLMs) achieve impressive scores on existing benchmarks, a fundamental question remains: can MLLMs truly ground language in vision with human-like sophistication, or are they merely… See the full description on the dataset page: https://huggingface.co/datasets/lirang04/GroundingME.
| 330
| 330
|
[
"task_categories:zero-shot-object-detection",
"license:other",
"size_categories:1K<n<10K",
"format:parquet",
"format:optimized-parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2512.17495",
"region:us",
"multimodal",
"visual-grounding",
"mllm"
] | 2025-12-21T17:21:45
| null | null |
6791fcbb49c4df6d798ca7c9
|
cais/hle
|
cais
|
{"license": "mit", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "string"}, {"name": "image_preview", "dtype": "image"}, {"name": "answer", "dtype": "string"}, {"name": "answer_type", "dtype": "string"}, {"name": "author_name", "dtype": "string"}, {"name": "rationale", "dtype": "string"}, {"name": "rationale_image", "dtype": "image"}, {"name": "raw_subject", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "canary", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 284205983, "num_examples": 2500}], "download_size": 274276147, "dataset_size": 284205983}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]}
| false
|
auto
| 2025-09-10T19:27:11
| 644
| 56
| false
|
f22d109f258100c8e23dceae8aa41008e3d826cc
|
[!NOTE]
IMPORTANT: Please help us protect the integrity of this benchmark by not publicly sharing, re-uploading, or distributing the dataset.
Humanity's Last Exam
🌐 Website | 📄 Paper | GitHub
Center for AI Safety & Scale AI
Humanity's Last Exam (HLE) is a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. Humanity's Last Exam consists of 2,500 questions across dozens of… See the full description on the dataset page: https://huggingface.co/datasets/cais/hle.
| 20,153
| 128,302
|
[
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-01-23T08:24:27
| null | null |
656523d6bfb751371817c448
|
Idavidrein/gpqa
|
Idavidrein
|
{"license": "cc-by-4.0", "viewer": true, "extra_gated_prompt": "You agree to NOT reveal examples from this dataset in plain text or images online, to reduce the risk of leakage into foundation model training corpora.", "extra_gated_fields": {"I accept these terms": "checkbox"}, "configs": [{"config_name": "gpqa_extended", "data_files": "gpqa_extended.csv"}, {"config_name": "gpqa_main", "data_files": "gpqa_main.csv"}, {"config_name": "gpqa_diamond", "data_files": "gpqa_diamond.csv"}, {"config_name": "gpqa_experts", "data_files": "gpqa_experts.csv"}], "task_categories": ["question-answering", "text-generation"], "language": ["en"], "tags": ["open-domain-qa", "open-book-qa", "multiple-choice-qa"], "pretty_name": "GPQA", "size_categories": ["n<1K"]}
| false
|
auto
| 2024-03-28T21:38:55
| 321
| 55
| false
|
90b8e5be2b1d3d2dbfe016cdab47981150600c4a
|
Dataset Card for GPQA
GPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy, despite spending >30m with full access to Google.
We request that you do not reveal examples from this dataset in plain text or images online, to reduce the risk of leakage into foundation model… See the full description on the dataset page: https://huggingface.co/datasets/Idavidrein/gpqa.
| 66,959
| 1,221,579
|
[
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:1K<n<10K",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2311.12022",
"region:us",
"open-domain-qa",
"open-book-qa",
"multiple-choice-qa"
] | 2023-11-27T23:18:46
| null | null |
694b9773785ac06cb19617cb
|
Lewandofski/OpenVE-3M
|
Lewandofski
|
{"license": "cc-by-nc-4.0", "task_categories": ["Video-to-Video"], "size_categories": ["1M<n<10M"], "tags": ["video"]}
| false
|
False
| 2025-12-25T16:44:15
| 62
| 55
| false
|
c939287d7764a472244791fdba5f543af31499f9
|
OpenVE-3M: A Large-Scale High-Quality Dataset for Instruction-Guided Video Editing
Haoyang He1*,
Jie Wang2*,
Jiangning Zhang1,
Zhucun Xue1,
Xingyuan Bu2,
Qiangpeng Yang2,
Shilei Wen2,
Lei Xie1#,
1Zhejiang University, 2Bytedance
*Equal Contribution. # Corresponding Author.
🌍 Overview
We introduce OpenVE-3M, an open-source, large-scale, and high-quality dataset for instruction-based video editing. The OpenVE-3M dataset includes eight major… See the full description on the dataset page: https://huggingface.co/datasets/Lewandofski/OpenVE-3M.
| 4,073
| 4,073
|
[
"license:cc-by-nc-4.0",
"size_categories:1M<n<10M",
"format:webdataset",
"modality:text",
"modality:video",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"arxiv:2512.07826",
"region:us",
"video"
] | 2025-12-24T07:34:11
| null | null |
68029f72cbf4fea90c2db08e
|
Mxode/Chinese-Instruct
|
Mxode
|
{"configs": [{"config_name": "stem_zh", "data_files": [{"split": "train", "path": "stem_zh/**/*"}]}, {"config_name": "infinity-instruct", "data_files": [{"split": "train", "path": "infinity-instruct/**/*"}]}, {"config_name": "firefly", "data_files": [{"split": "train", "path": "firefly/**/*"}]}, {"config_name": "magpie", "data_files": [{"split": "train", "path": "magpie/**/*"}]}, {"config_name": "dpsk-r1-distil", "default": true, "data_files": [{"split": "train", "path": "dpsk-r1-distil/**/*"}]}, {"config_name": "coig-cqia", "data_files": [{"split": "train", "path": "coig-cqia/**/*"}]}, {"config_name": "disc-law", "data_files": [{"split": "train", "path": "disc-law/**/*"}]}, {"config_name": "neo_sft_phase2", "data_files": [{"split": "train", "path": "neo_sft_phase2/**/*"}]}, {"config_name": "chinese-medical", "data_files": [{"split": "train", "path": "chinese-medical/**/*"}]}, {"config_name": "chinese-reasoning-distil", "data_files": [{"split": "train", "path": "chinese-reasoning-distil/**/*"}]}, {"config_name": "psycho-10k-dpsk-r1", "data_files": [{"split": "train", "path": "psycho-10k-dpsk-r1/**/*"}]}, {"config_name": "sof-c-zh", "data_files": [{"split": "train", "path": "sof-c-zh/**/*"}]}, {"config_name": "industryinstruction", "data_files": [{"split": "train", "path": "industryinstruction/**/*"}]}, {"config_name": "Chinese-QA-AFAF", "data_files": [{"split": "train", "path": "Chinese-QA-AFAF/**/*"}]}], "license": "cc-by-sa-4.0", "task_categories": ["text-generation", "question-answering"], "language": ["zh"]}
| false
|
False
| 2025-05-09T07:05:01
| 127
| 52
| false
|
1576ffc1e0b21d2980b887de6b5f9abc4a7fb2a8
|
中文指令微调数据集
💻 Github Repo
本项目旨在构建一个高质量、多领域、大规模的中文指令微调数据集。
本项目将会持续更新。更多数据集欢迎访问 Github Repo。
[!TIP]
如果您想要一个可用于学习的简化版中文指令数据集，可以访问：Mxode/Chinese-Instruct-Lite
具体构成
dpsk-r1-distil：中文 DeepSeek-R1 蒸馏数据集，来自 Congliu/Chinese-DeepSeek-R1-Distill-data-110k，根据打分质量做了筛选，提取了最终的回答，未包含思考过程。
chinese-reasoning-distil：中文推理蒸馏数据集，来自 Mxode/Chinese-Reasoning-Distil-Data，提取了最终的回答，未包含思考过程。
firefly：中文通用指令微调数据集，指令取自 Mxode/Firefly-1.1M-Rephrased，其本身已经相较于原 Firefly… See the full description on the dataset page: https://huggingface.co/datasets/Mxode/Chinese-Instruct.
| 887
| 12,862
|
[
"task_categories:text-generation",
"task_categories:question-answering",
"language:zh",
"license:cc-by-sa-4.0",
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-04-18T18:52:34
| null | null |
676f70846bf205795346d2be
|
FreedomIntelligence/medical-o1-reasoning-SFT
|
FreedomIntelligence
|
{"license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "language": ["en", "zh"], "tags": ["medical", "biology"], "configs": [{"config_name": "en", "data_files": "medical_o1_sft.json"}, {"config_name": "zh", "data_files": "medical_o1_sft_Chinese.json"}, {"config_name": "en_mix", "data_files": "medical_o1_sft_mix.json"}, {"config_name": "zh_mix", "data_files": "medical_o1_sft_mix_Chinese.json"}]}
| false
|
False
| 2025-04-22T15:11:21
| 1,033
| 48
| false
|
fc2c9e8a37b38f38da6d449564a8c350b244aef4
|
News
[2025/04/22] We split the data and kept only the medical SFT dataset (medical_o1_sft.json). The file medical_o1_sft_mix.json contains a mix of medical and general instruction data.
[2025/02/22] We released the distilled dataset from Deepseek-R1 based on medical verifiable problems. You can use it to initialize your models with the reasoning chain from Deepseek-R1.
[2024/12/25] We open-sourced the medical reasoning dataset for SFT, built on medical verifiable problems and an LLM… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT.
| 5,052
| 145,358
|
[
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"language:zh",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2412.18925",
"region:us",
"medical",
"biology"
] | 2024-12-28T03:29:08
| 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 ✅
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
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Size of downloaded dataset files:
1.73 GB
Size of the auto-converted Parquet files:
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Number of rows:
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