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67d3479522a51de18affff22 | nvidia/Llama-Nemotron-Post-Training-Dataset-v1 | nvidia | {"license": "cc-by-4.0", "configs": [{"config_name": "SFT", "data_files": [{"split": "code", "path": "SFT/code/*.jsonl"}, {"split": "math", "path": "SFT/math/*.jsonl"}, {"split": "science", "path": "SFT/science/*.jsonl"}, {"split": "chat", "path": "SFT/chat/*.jsonl"}, {"split": "safety", "path": "SFT/safety/*.jsonl"}], "default": true}, {"config_name": "RL", "data_files": [{"split": "instruction_following", "path": "RL/instruction_following/*.jsonl"}]}]} | false | null | 2025-03-18T15:56:14 | 236 | 136 | false | ed905e6239c9d191e4c965a403dde07a5383b5eb |
Llama-Nemotron-Post-Training-Dataset-v1 Release
Data Overview
This dataset is a compilation of SFT and RL data that supports improvements of math, code, general reasoning, and instruction following capabilities of the original Llama instruct model, in support of NVIDIA’s release of Llama-3.3-Nemotron-Super-49B-v1 and Llama-3.1-Nemotron-Nano-8B-v1.
Llama-3.3-Nemotron-Super-49B-v1 is a large language model (LLM) which is a derivative of Meta’s Llama-3.3-70B-Instruct (AKA… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset-v1. | 6,212 | 6,221 | [
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"library:pandas",
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"library:polars",
"region:us"
] | 2025-03-13T21:01:09 | null | null |
67c0cda5c0b7a236a5f070e3 | glaiveai/reasoning-v1-20m | glaiveai | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 177249016911, "num_examples": 22199375}], "download_size": 87247205094, "dataset_size": 177249016911}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "size_categories": ["10M<n<100M"]} | false | null | 2025-03-19T13:21:37 | 105 | 80 | false | da6bb3d0ff8fd8ea5abacee8519762ca6aaf367e |
We are excited to release a synthetic reasoning dataset containing 22mil+ general reasoning questions and responses generated using deepseek-ai/DeepSeek-R1-Distill-Llama-70B. While there have been multiple efforts to build open reasoning datasets for math and code tasks, we noticed a lack of large datasets containing reasoning traces for diverse non code/math topics like social and natural sciences, education, creative writing and general conversations, which is why we decided to release this… See the full description on the dataset page: https://huggingface.co/datasets/glaiveai/reasoning-v1-20m. | 5,344 | 5,344 | [
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"region:us"
] | 2025-02-27T20:40:05 | null | null |
67d97c4be2b27852325fd8e2 | nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim | nvidia | {"license": "cc-by-4.0"} | false | null | 2025-03-21T15:02:34 | 84 | 59 | false | 9cd48351839af877ff365fa8bf06e1cf9e57d539 |
PhysicalAI-Robotics-GR00T-X-Embodiment-Sim
Github Repo: Isaac GR00T N1
We provide a set of datasets used for post-training of GR00T N1. Each dataset is a collection of trajectories from different robot embodiments and tasks.
Cross-embodied bimanual manipulation: 9k trajectories
Dataset Name
#trajectories
bimanual_panda_gripper.Threading
1000
bimanual_panda_hand.LiftTray
1000
bimanual_panda_gripper.ThreePieceAssembly
1000… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim. | 25,541 | 25,541 | [
"license:cc-by-4.0",
"region:us"
] | 2025-03-18T13:59:39 | 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"}]} | false | null | 2025-02-22T05:15:38 | 551 | 47 | false | 61536c1d80b2c799df6800cc583897b77d2c86d2 |
News
[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 verifier.
Introduction
This dataset is used to fine-tune HuatuoGPT-o1, a medical LLM designed for advanced medical reasoning. This dataset is constructed using GPT-4o… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT. | 27,577 | 46,320 | [
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"language:zh",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2412.18925",
"region:us",
"medical",
"biology"
] | 2024-12-28T03:29:08 | null | null |
67abc2c2d6edf5606aa5c0d7 | facebook/collaborative_agent_bench | facebook | {"license": "other", "extra_gated_prompt": "## License", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Date of birth": "date_picker", "Country": "country", "Affiliation": "text", "I accept the terms and conditions": "checkbox", "geo": "ip_location"}, "extra_gated_description": "SWEET-RL Research License and Acceptable Use Policy", "extra_gated_button_content": "I Accept Self-taught Evaluator Research License and AUP"} | false | null | 2025-03-20T04:17:14 | 43 | 43 | false | cf3526da25989b53f105fe9b74c1174a3e19c548 | This dataset is released as part of SWEET-RL: Training Multi-Turn LLM Agents on
Collaborative Reasoning Tasks research project.
Please refer to our project materials here for training and evaluation details.
Citation
If you use data, model, or code from this work, please cite with the following BibTex entry:
@misc{zhou2025sweetrltrainingmultiturnllm,
title={SWEET-RL: Training Multi-Turn LLM Agents on Collaborative Reasoning Tasks},
author={Yifei Zhou and Song Jiang and… See the full description on the dataset page: https://huggingface.co/datasets/facebook/collaborative_agent_bench. | 70 | 70 | [
"license:other",
"arxiv:2503.15478",
"region:us"
] | 2025-02-11T21:36:02 | null | null |
67d6cac12e36db434b2be97e | manycore-research/SpatialLM-Testset | manycore-research | {"license": "cc-by-nc-4.0", "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "test.csv"}]}]} | false | null | 2025-03-19T15:05:46 | 37 | 36 | false | 3a5c44deac7ac1de370c5341d2748250cbbf52e3 |
SpatialLM Testset
We provide a test set of 107 preprocessed point clouds and their corresponding GT layouts, point clouds are reconstructed from RGB videos using MASt3R-SLAM. SpatialLM-Testset is quite challenging compared to prior clean RGBD scan datasets due to the noises and occlusions in the point clouds reconstructed from monocular RGB videos.
Folder Structure
Outlines of the dataset files:
project-root/
├── pcd/*.ply… See the full description on the dataset page: https://huggingface.co/datasets/manycore-research/SpatialLM-Testset. | 5,849 | 5,849 | [
"license:cc-by-nc-4.0",
"size_categories:n<1K",
"format:csv",
"modality:3d",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-16T12:57:37 | null | null |
679c0b5c32cf4c58bdcba8eb | facebook/natural_reasoning | facebook | {"license": "cc-by-nc-4.0", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "Natural Reasoning", "size_categories": ["1M<n<10M"]} | false | null | 2025-02-21T06:02:40 | 461 | 34 | false | 99eea5dc6bfa45a925eb42600e81dc90377ba237 | NaturalReasoning is a large-scale dataset for general reasoning tasks. It consists of high-quality challenging reasoning questions backtranslated from pretraining corpora DCLM and FineMath. The questions have been deduplicated and decontaminated from popular reasoning benchmarks including MATH, GPQA, MMLU-Pro, MMLU-STEM. For each question, we extract the reference final answer from the original document from the pretraining corpora if possible. We also provide a model-generated response from… See the full description on the dataset page: https://huggingface.co/datasets/facebook/natural_reasoning. | 13,643 | 15,223 | [
"task_categories:text-generation",
"language:en",
"license:cc-by-nc-4.0",
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"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2502.13124",
"region:us"
] | 2025-01-30T23:29:32 | null | null |
67c03fd6b9fe27a2ac49784d | open-r1/codeforces-cots | open-r1 | {"dataset_info": [{"config_name": "checker_interactor", "features": [{"name": "id", "dtype": "string"}, {"name": "aliases", "sequence": "string"}, {"name": "contest_id", "dtype": "string"}, {"name": "contest_name", "dtype": "string"}, {"name": "contest_type", "dtype": "string"}, {"name": "contest_start", "dtype": "int64"}, {"name": "contest_start_year", "dtype": "int64"}, {"name": "index", "dtype": "string"}, {"name": "time_limit", "dtype": "float64"}, {"name": "memory_limit", "dtype": "float64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "input_format", "dtype": "string"}, {"name": "output_format", "dtype": "string"}, {"name": "interaction_format", "dtype": "string"}, {"name": "note", "dtype": "string"}, {"name": "examples", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "editorial", "dtype": "string"}, {"name": "prompt", 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"path": "solutions_decontaminated/train-*"}]}, {"config_name": "solutions_py", "data_files": [{"split": "train", "path": "solutions_py/train-*"}]}, {"config_name": "solutions_py_decontaminated", "data_files": [{"split": "train", "path": "solutions_py_decontaminated/train-*"}]}, {"config_name": "solutions_w_editorials", "data_files": [{"split": "train", "path": "solutions_w_editorials/train-*"}]}, {"config_name": "solutions_w_editorials_decontaminated", "data_files": [{"split": "train", "path": "solutions_w_editorials_decontaminated/train-*"}]}, {"config_name": "solutions_w_editorials_py", "data_files": [{"split": "train", "path": "solutions_w_editorials_py/train-*"}]}, {"config_name": "solutions_w_editorials_py_decontaminated", "data_files": [{"split": "train", "path": "solutions_w_editorials_py_decontaminated/train-*"}]}, {"config_name": "test_input_generator", "data_files": [{"split": "train", "path": "test_input_generator/train-*"}]}], "license": "cc-by-4.0"} | false | null | 2025-03-17T11:29:08 | 109 | 32 | false | 5f9671cf3779c3c709bd9f6f61b38ef3f061d5c8 |
Dataset Card for CodeForces-CoTs
Dataset description
CodeForces-CoTs is a large-scale dataset for training reasoning models on competitive programming tasks. It consists of 10k CodeForces problems with up to five reasoning traces generated by DeepSeek R1. We did not filter the traces for correctness, but found that around 84% of the Python ones pass the public tests.
The dataset consists of several subsets:
solutions: we prompt R1 to solve the problem and produce code.… See the full description on the dataset page: https://huggingface.co/datasets/open-r1/codeforces-cots. | 6,883 | 6,883 | [
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-27T10:35:02 | null | null |
67b32145bac2756ce9a4a0fe | Congliu/Chinese-DeepSeek-R1-Distill-data-110k | Congliu | {"license": "apache-2.0", "language": ["zh"], "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "text2text-generation", "question-answering"]} | false | null | 2025-02-21T02:18:08 | 589 | 30 | false | 8520b649430617c2be4490f424d251d09d835ed3 |
中文基于满血DeepSeek-R1蒸馏数据集（Chinese-Data-Distill-From-R1）
🤗 Hugging Face | 🤖 ModelScope | 🚀 Github | 📑 Blog
注意：提供了直接SFT使用的版本，点击下载。将数据中的思考和答案整合成output字段，大部分SFT代码框架均可直接直接加载训练。
本数据集为中文开源蒸馏满血R1的数据集，数据集中不仅包含math数据，还包括大量的通用类型数据，总数量为110K。
为什么开源这个数据？
R1的效果十分强大，并且基于R1蒸馏数据SFT的小模型也展现出了强大的效果，但检索发现，大部分开源的R1蒸馏数据集均为英文数据集。 同时，R1的报告中展示，蒸馏模型中同时也使用了部分通用场景数据集。
为了帮助大家更好地复现R1蒸馏模型的效果，特此开源中文数据集。该中文数据集中的数据分布如下：
Math：共计36568个样本，
Exam：共计2432个样本，
STEM：共计12648个样本，… See the full description on the dataset page: https://huggingface.co/datasets/Congliu/Chinese-DeepSeek-R1-Distill-data-110k. | 7,587 | 10,155 | [
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"task_categories:text2text-generation",
"task_categories:question-answering",
"language:zh",
"license:apache-2.0",
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"modality:tabular",
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-17T11:45:09 | null | null |
67d9394e2e311ae0f2e8183f | PixelAI-Team/TalkBody4D | PixelAI-Team | {"viewer": false, "license": "cc-by-nc-4.0", "extra_gated_prompt": "The dataset is encrypted to prevent unauthorized access. Please fill out the request form : https://forms.gle/eC2aLRXZ8DAdKcis7. We'll check with your PI.", "extra_gated_fields": {"Name": "text", "E-Mail": "text", "Company/Organization": "text", "PI's Name": "text", "PI's E-Mail": "text", "Specific date": "date_picker", "I want to use this dataset for": {"type": "select", "options": ["Research", "Education", {"label": "Other", "value": "other"}]}, "I have signed the request form": "checkbox"}, "size_categories": ["100B<n<1T"]} | false | null | 2025-03-25T12:05:54 | 29 | 29 | false | e20725b0891c858f73fff56ad1ea34e46bfc54ec |
TalkBody4D Dataset
This dataset contains four multi-view image sequences used in our paper "TaoAvatar: Real-Time Lifelike Full-Body Talking Avatars
for Augmented Reality via 3D Gaussian Splatting". They are captured with 59 well-calibrated RGB cameras in 20 fps, with a resolution of 3000×4000 and lengths ranging from 800 to 1000 frames. We use the data to evaluate our method for building animatable human body avatars.
We also provide the SMPL-X fitting in the dataset.… See the full description on the dataset page: https://huggingface.co/datasets/PixelAI-Team/TalkBody4D. | 55 | 55 | [
"license:cc-by-nc-4.0",
"size_categories:1M<n<10M",
"format:webdataset",
"modality:image",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"region:us"
] | 2025-03-18T09:13:50 | null | null |
67cd6c25b770987b3f80af97 | a-m-team/AM-DeepSeek-R1-Distilled-1.4M | a-m-team | {"license": "cc-by-nc-4.0", "task_categories": ["text-generation"], "language": ["zh", "en"], "tags": ["code", "math", "reasoning", "thinking", "deepseek-r1", "distill"], "size_categories": ["1M<n<10M"]} | false | null | 2025-03-25T09:26:50 | 49 | 25 | false | a593d04efe5f6a96d8793c8ad8d86c19209ad74b | AM-DeepSeek-R1-Distilled-1.4M is a large-scale general reasoning task dataset composed of
high-quality and challenging reasoning problems. These problems are collected from numerous
open-source datasets, semantically deduplicated, and cleaned to eliminate test set contamination.
All responses in the dataset are distilled from the reasoning model (mostly DeepSeek-R1) and have undergone
rigorous verification: mathematical problems are validated through answer checking, code
problems via… See the full description on the dataset page: https://huggingface.co/datasets/a-m-team/AM-DeepSeek-R1-Distilled-1.4M. | 1,047 | 1,047 | [
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"license:cc-by-nc-4.0",
"size_categories:1M<n<10M",
"region:us",
"code",
"math",
"reasoning",
"thinking",
"deepseek-r1",
"distill"
] | 2025-03-09T10:23:33 | null | null |
67d967709b5f9bcc5eef92e1 | HuggingFaceTB/stack-edu | HuggingFaceTB | {"dataset_info": [{"config_name": "C", "features": [{"name": "blob_id", "dtype": "large_string"}, {"name": "language", "dtype": "large_string"}, {"name": "repo_name", "dtype": "large_string"}, {"name": "path", "dtype": "large_string"}, {"name": "src_encoding", "dtype": "large_string"}, {"name": "length_bytes", "dtype": "int64"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}, {"name": "detected_licenses", "large_list": "large_string"}, {"name": "license_type", "dtype": "large_string"}], "splits": [{"name": "train", "num_bytes": 1100442974, "num_examples": 5848375}], "download_size": 571816053, "dataset_size": 1100442974}, {"config_name": "CSharp", "features": [{"name": "blob_id", "dtype": "large_string"}, {"name": "language", "dtype": "large_string"}, {"name": "repo_name", "dtype": "large_string"}, {"name": "path", "dtype": "large_string"}, {"name": "src_encoding", "dtype": 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💻 Stack-Edu
Stack-Edu is a 125B token dataset of educational code filtered from The Stack v2, precisely the curated training corpus of StarCoder2 models denoted StarCoder2Data. It is intended for Language Models training.
This dataset was curated using a classifier-based filtering strategy, inspired by 📚 FineWeb-Edu, to retain only the highest-quality educational programming content.
Stack-Edu shows consistent improvement over StarCoder2data on all the programming languages on… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/stack-edu. | 968 | 968 | [
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🍷 FineWeb
15 trillion tokens of the finest data the 🌐 web has to offer
What is it?
The 🍷 FineWeb dataset consists of more than 15T tokens of cleaned and deduplicated english web data from CommonCrawl. The data processing pipeline is optimized for LLM performance and ran on the 🏭 datatrove library, our large scale data processing library.
🍷 FineWeb was originally meant to be a fully open replication of 🦅 RefinedWeb, with a release of the full dataset under… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb. | 238,801 | 2,304,977 | [
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"region:us"
] | 2024-04-18T14:33:13 | null | null |
67dabea523ec1d597d1e0012 | MaziyarPanahi/Llama-Nemotron-Post-Training-Dataset-v1-ShareGPT | MaziyarPanahi | {"license": "cc-by-4.0"} | false | null | 2025-03-23T21:34:33 | 28 | 22 | false | 1dc5274f9328178e12e1aa471049c08a72f5287e |
Llama-Nemotron-Post-Training-Dataset-v1 in ShareGPT Format
This dataset is a conversion of NVIDIA's Llama-Nemotron-Post-Training-Dataset-v1
into the ShareGPT format while preserving the original splits and columns.
Format
Each example contains all original fields plus a messages array:
{
"input": "original input text",
"output": "original output text",
... (other original columns) ...,
"messages": [
{"role": "user", "content": "User message"},
{"role":… See the full description on the dataset page: https://huggingface.co/datasets/MaziyarPanahi/Llama-Nemotron-Post-Training-Dataset-v1-ShareGPT. | 564 | 564 | [
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"region:us"
] | 2025-03-19T12:55:01 | null | null |
67b20fc10861cec33b3afb8a | Conard/fortune-telling | Conard | {"license": "mit"} | false | null | 2025-02-17T05:13:43 | 97 | 21 | false | 6261fe0d35a75997972bbfcd9828020e340303fb | null | 5,950 | 6,058 | [
"license:mit",
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-16T16:18:09 | null | null |
67d305619f485955bf117049 | nvidia/HelpSteer3 | nvidia | {"license": "cc-by-4.0", "language": ["en", "zh", "ko", "fr", "es", "ru", "ja", "de", "it", "pt", "pl", "id", "nl", "vi"], "pretty_name": "HelpSteer3", "size_categories": ["10K<n<100K"], "tags": ["human-feedback"], "configs": [{"config_name": "preference", "default": true, "data_files": [{"split": "train", "path": "preference/train.jsonl.gz"}, {"split": "validation", "path": "preference/validation.jsonl.gz"}]}, {"config_name": "feedback", "data_files": [{"split": "train", "path": "feedback/train.jsonl.gz"}, {"split": "validation", "path": "feedback/validation.jsonl.gz"}]}, {"config_name": "edit", "data_files": [{"split": "train", "path": "edit/train.jsonl.gz"}, {"split": "validation", "path": "edit/validation.jsonl.gz"}]}, {"config_name": "edit_quality", "data_files": [{"split": "train", "path": "edit_quality/train.jsonl.gz"}, {"split": "validation", "path": "edit_quality/validation.jsonl.gz"}]}]} | false | null | 2025-03-18T19:51:32 | 30 | 20 | false | 7366103dbb732074dcf866560d2431d0ae8c9b1d |
HelpSteer3
HelpSteer3 is an open-source Helpfulness Dataset (CC-BY-4.0) that supports aligning models to become more helpful in responding to user prompts.
When used to tune Llama 3.3 70B Instruct Models to perform a novel approach to Inference Time Scaling (ITS) for open-ended, general-domain tasks, we achieve as high as 93.4% on Arena Hard, which makes it No. 1 on the benchmark as of 18 Mar 2025.
See details on the paper at https://arxiv.org/abs/2503.04378.
Models were trained… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/HelpSteer3. | 714 | 714 | [
"language:en",
"language:zh",
"language:ko",
"language:fr",
"language:es",
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"language:ja",
"language:de",
"language:it",
"language:pt",
"language:pl",
"language:id",
"language:nl",
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"license:cc-by-4.0",
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"library:mlcroissant",
"library:polars",
"arxiv:2503.04378",
"region:us",
"human-feedback"
] | 2025-03-13T16:18:41 | null | null |
67d7eeec9830e5c1e2a8f708 | BytedTsinghua-SIA/DAPO-Math-17k | BytedTsinghua-SIA | {"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["math"], "pretty_name": "DAPO-Math-17k", "size_categories": ["1M<n<10M"]} | false | null | 2025-03-18T07:47:04 | 44 | 20 | false | 9f6440001c15da8e7c7516fdbb3d2ce49de711de |
This dataset actually only contains ~17k unique prompts and was duplicated by ~100x by accident.
| 2,405 | 2,405 | [
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"library:polars",
"region:us",
"math"
] | 2025-03-17T09:44:12 | null | null |
67cba813ef7ed9b8e2a948c7 | canopylabs/zac-sample-dataset | canopylabs | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 48000}}}], "splits": [{"name": "train", "num_bytes": 13147142.424794896, "num_examples": 20}], "download_size": 10349037, "dataset_size": 13147142.424794896}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-03-08T02:14:46 | 19 | 19 | false | 5464e5b186dab0d49049eca0b28774ad9371fc89 | null | 742 | 742 | [
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"region:us"
] | 2025-03-08T02:14:43 | null | null |
625552d2b339bb03abe3432d | openai/gsm8k | openai | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "task_ids": [], "paperswithcode_id": "gsm8k", "pretty_name": "Grade School Math 8K", "tags": ["math-word-problems"], "dataset_info": [{"config_name": "main", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3963202, "num_examples": 7473}, {"name": "test", "num_bytes": 713732, "num_examples": 1319}], "download_size": 2725633, "dataset_size": 4676934}, {"config_name": "socratic", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5198108, "num_examples": 7473}, {"name": "test", "num_bytes": 936859, "num_examples": 1319}], "download_size": 3164254, "dataset_size": 6134967}], "configs": [{"config_name": "main", "data_files": [{"split": "train", "path": "main/train-*"}, {"split": "test", "path": "main/test-*"}]}, {"config_name": "socratic", "data_files": [{"split": "train", "path": "socratic/train-*"}, {"split": "test", "path": "socratic/test-*"}]}]} | false | null | 2024-01-04T12:05:15 | 658 | 17 | false | e53f048856ff4f594e959d75785d2c2d37b678ee |
Dataset Card for GSM8K
Dataset Summary
GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.
These problems take between 2 and 8 steps to solve.
Solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the… See the full description on the dataset page: https://huggingface.co/datasets/openai/gsm8k. | 331,112 | 4,207,833 | [
"task_categories:text2text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2110.14168",
"region:us",
"math-word-problems"
] | 2022-04-12T10:22:10 | gsm8k | null |
63990f21cc50af73d29ecfa3 | fka/awesome-chatgpt-prompts | fka | {"license": "cc0-1.0", "tags": ["ChatGPT"], "task_categories": ["question-answering"], "size_categories": ["100K<n<1M"]} | false | null | 2025-01-06T00:02:53 | 7,645 | 15 | false | 68ba7694e23014788dcc8ab5afe613824f45a05c | 🧠 Awesome ChatGPT Prompts [CSV dataset]
This is a Dataset Repository of Awesome ChatGPT Prompts
View All Prompts on GitHub
License
CC-0
| 12,297 | 137,869 | [
"task_categories:question-answering",
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"library:datasets",
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"library:mlcroissant",
"library:polars",
"region:us",
"ChatGPT"
] | 2022-12-13T23:47:45 | null | null |
67e134c540496e1ded36dcc3 | Intelligent-Internet/II-Thought-RL-v0 | Intelligent-Internet | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "problem", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "verification_info", "dtype": "string"}, {"name": "data_source", "dtype": "string"}, {"name": "domain", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4819048664, "num_examples": 341795}], "download_size": 2448038647, "dataset_size": 4819048664}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-03-25T14:50:20 | 15 | 15 | false | f837a0f6022aa8e0c48aa45a1deb7d118f55905b |
II-Thought RL v0: A Large-Scale Curated Dataset for Reinforcement Learning
We introduce II-Thought RL v0, the first large-scale, multi-task dataset designed for Reinforcement Learning. This dataset consists of high-quality question-answer pairs that have undergone a rigorous multi-step filtering process, leveraging Gemini 2.0 Flash and Qwen 32B as quality evaluators.
In this initial release, we have curated and refined publicly available datasets while also introducing our own… See the full description on the dataset page: https://huggingface.co/datasets/Intelligent-Internet/II-Thought-RL-v0. | 525 | 525 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2412.08819",
"region:us"
] | 2025-03-24T10:32:37 | null | null |
67810a69793bdaf579c3710a | starvector/svg-stack | starvector | {} | false | null | 2025-01-10T11:57:29 | 15 | 14 | false | 1d922ec145f5ab6e3ff0e874235aff0d8a9dec91 |
Dataset Card for svg-stack
Dataset Description
This dataset contains SVG code examples for training and evaluating SVG models for image vectorization.
Dataset Structure
Features
The dataset contains the following fields:
Field Name
Description
Filename
Unique ID for each SVG
Svg
SVG code
Usage
from datasets import load_dataset
dataset = load_dataset("starvector/svg-stack")… See the full description on the dataset page: https://huggingface.co/datasets/starvector/svg-stack. | 664 | 1,193 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2312.11556",
"region:us"
] | 2025-01-10T11:54:17 | null | null |
6797e648de960c48ff034e54 | open-thoughts/OpenThoughts-114k | open-thoughts | {"dataset_info": [{"config_name": "default", "features": [{"name": "system", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 2635015668, "num_examples": 113957}], "download_size": 1078777193, "dataset_size": 2635015668}, {"config_name": "metadata", "features": [{"name": "problem", "dtype": "string"}, {"name": "deepseek_reasoning", "dtype": "string"}, {"name": "deepseek_solution", "dtype": "string"}, {"name": "ground_truth_solution", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "test_cases", "dtype": "string"}, {"name": "starter_code", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5525214077.699433, "num_examples": 113957}], "download_size": 2469729724, "dataset_size": 5525214077.699433}], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}, {"config_name": "metadata", "data_files": [{"split": "train", "path": "metadata/train-*"}]}], "tags": ["curator", "synthetic"], "license": "apache-2.0"} | false | null | 2025-02-20T07:16:57 | 668 | 14 | false | 56b06e3066a8163577ac93b24613a560e685d029 |
Open-Thoughts-114k
Open synthetic reasoning dataset with 114k high-quality examples covering math, science, code, and puzzles!
Inspect the content with rich formatting with Curator Viewer.
Available Subsets
default subset containing ready-to-train data used to finetune the OpenThinker-7B and OpenThinker-32B models:
ds = load_dataset("open-thoughts/OpenThoughts-114k", split="train")
metadata subset containing extra columns used in dataset construction:… See the full description on the dataset page: https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k. | 37,321 | 142,380 | [
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"curator",
"synthetic"
] | 2025-01-27T20:02:16 | null | null |
66a53dc7d40a13036c5f2ebe | mlabonne/FineTome-100k | mlabonne | {"dataset_info": {"features": [{"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "source", "dtype": "string"}, {"name": "score", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 239650960.7474458, "num_examples": 100000}], "download_size": 116531415, "dataset_size": 239650960.7474458}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2024-07-29T09:52:30 | 191 | 12 | false | c2343c1372ff31f51aa21248db18bffa3193efdb |
FineTome-100k
The FineTome dataset is a subset of arcee-ai/The-Tome (without arcee-ai/qwen2-72b-magpie-en), re-filtered using HuggingFaceFW/fineweb-edu-classifier.
It was made for my article "Fine-tune Llama 3.1 Ultra-Efficiently with Unsloth".
| 18,483 | 83,776 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-07-27T18:34:47 | null | null |
67b143989d15e90f2c15ac76 | zhang0jhon/Aesthetic-4K | zhang0jhon | {"license": "mit"} | false | null | 2025-03-25T02:40:34 | 13 | 12 | false | 40a07c883a5c824a42b3dac2e393f705579cbf82 |
Aesthetic-4K Dataset
We introduce Aesthetic-4K, a high-quality dataset for ultra-high-resolution image generation, featuring carefully selected images and captions generated by GPT-4o.
Additionally, we have meticulously filtered out low-quality images through manual inspection, excluding those with motion blur, focus issues, or mismatched text prompts.
For more details, please refer to our paper:
Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent Diffusion Models (CVPR… See the full description on the dataset page: https://huggingface.co/datasets/zhang0jhon/Aesthetic-4K. | 1,113 | 2,257 | [
"license:mit",
"size_categories:1K<n<10K",
"format:imagefolder",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:2503.18352",
"region:us"
] | 2025-02-16T01:47:04 | null | null |
67b3495a2f3994b7d95dde92 | Congliu/Chinese-DeepSeek-R1-Distill-data-110k-SFT | Congliu | {"license": "apache-2.0", "language": ["zh"], "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "text2text-generation", "question-answering"]} | false | null | 2025-02-19T13:24:55 | 157 | 12 | false | 263435dc9a8cc822449b6f3531794486f8141be6 |
中文基于满血DeepSeek-R1蒸馏数据集（Chinese-Data-Distill-From-R1）
🤗 Hugging Face | 🤖 ModelScope | 🚀 Github | 📑 Blog
注意：该版本为，可以直接SFT使用的版本，将原始数据中的思考和答案整合成output字段，大部分SFT代码框架均可直接直接加载训练。
本数据集为中文开源蒸馏满血R1的数据集，数据集中不仅包含math数据，还包括大量的通用类型数据，总数量为110K。
为什么开源这个数据？
R1的效果十分强大，并且基于R1蒸馏数据SFT的小模型也展现出了强大的效果，但检索发现，大部分开源的R1蒸馏数据集均为英文数据集。 同时，R1的报告中展示，蒸馏模型中同时也使用了部分通用场景数据集。
为了帮助大家更好地复现R1蒸馏模型的效果，特此开源中文数据集。该中文数据集中的数据分布如下：
Math：共计36568个样本，
Exam：共计2432个样本，
STEM：共计12648个样本，… See the full description on the dataset page: https://huggingface.co/datasets/Congliu/Chinese-DeepSeek-R1-Distill-data-110k-SFT. | 4,829 | 6,165 | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:question-answering",
"language:zh",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-17T14:36:10 | null | null |
67c122a87c100c8caa21c89d | TIGER-Lab/VisualWebInstruct | TIGER-Lab | {"language": ["en"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["question-answering", "visual-question-answering"], "pretty_name": "VisualWebInstruct", "tags": ["math", "science"], "configs": [{"config_name": "example", "data_files": [{"split": "train", "path": "data/train-*"}]}, {"config_name": "conversation", "data_files": [{"split": "train", "path": "mixed_conversation.parquet"}]}, {"config_name": "visualwebinstruct", "data_files": [{"split": "train", "path": "visualwebinstruct_qa.parquet"}]}]} | false | null | 2025-03-24T05:26:29 | 27 | 12 | false | 08c69039c7caa146fbff0fbf936b9d2fe1a69ded |
VisualWebInstruct: Scaling up Multimodal Instruction Data through Web Search
VisualWebInstruct is a large-scale, diverse multimodal instruction dataset designed to enhance vision-language models' reasoning capabilities. The dataset contains approximately 900K question-answer (QA) pairs, with 40% consisting of visual QA pairs associated with 163,743 unique images, while the remaining 60% are text-only QA pairs.
Links
GitHub Repository
Research Paper
Project Website… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/VisualWebInstruct. | 1,111 | 1,131 | [
"task_categories:question-answering",
"task_categories:visual-question-answering",
"language:en",
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2503.10582",
"region:us",
"math",
"science"
] | 2025-02-28T02:42:48 | null | null |
67ce2fb269ac5540794d0bf6 | CharlieDreemur/OpenManus-RL | CharlieDreemur | {"language": ["en"], "tags": ["sft", "instruction-tuning", "conversational-ai"], "license": "apache-2.0", "task_categories": ["text-generation"], "pretty_name": "OpenManusRL", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "conversations", "list": [{"name": "role", "dtype": "string"}, {"name": "content", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 277895199, "num_examples": 48927}], "download_size": 73312767, "dataset_size": 277895199}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-03-15T01:29:38 | 41 | 12 | false | b102de3f0a2e40221fc923ed9f34756251fc666c |
Dataset Card for OpenManusRL
Dataset Description
Overview
💻 [Github Repo]
OpenManusRL combines agent trajectories from AgentInstruct, Agent-FLAN and AgentTraj-L(AgentGym) with features:
🔍 ReAct Framework - Reasoning-Acting integration
🧠 Structured Training - Separate format/reasoning learning
🚫 Anti-Hallucination - Negative samples + environment grounding
🌐 6 Domains - OS, DB, Web, KG, Household, E-commerce
Dataset Overview
Source… See the full description on the dataset page: https://huggingface.co/datasets/CharlieDreemur/OpenManus-RL. | 1,732 | 1,732 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
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"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2310.12823",
"arxiv:2403.12881",
"arxiv:2406.04151",
"region:us",
"sft",
"instruction-tuning",
"conversational-ai"
] | 2025-03-10T00:17:54 | null | null |
67aa021ced8d8663d42505cc | open-r1/OpenR1-Math-220k | open-r1 | {"license": "apache-2.0", "language": ["en"], "configs": [{"config_name": "all", "data_files": [{"split": "train", "path": "all/train-*"}]}, {"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}, {"config_name": "extended", "data_files": [{"split": "train", "path": "extended/train-*"}]}], "dataset_info": [{"config_name": "all", "features": [{"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "problem_type", "dtype": "string"}, {"name": "question_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "uuid", "dtype": "string"}, {"name": "is_reasoning_complete", "sequence": "bool"}, {"name": "generations", "sequence": "string"}, {"name": "correctness_math_verify", "sequence": "bool"}, {"name": "correctness_llama", "sequence": "bool"}, {"name": "finish_reasons", "sequence": "string"}, {"name": "correctness_count", "dtype": "int64"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 9734110026, "num_examples": 225129}], "download_size": 4221672067, "dataset_size": 9734110026}, {"config_name": "default", "features": [{"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "problem_type", "dtype": "string"}, {"name": "question_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "uuid", "dtype": "string"}, {"name": "is_reasoning_complete", "sequence": "bool"}, {"name": "generations", "sequence": "string"}, {"name": "correctness_math_verify", "sequence": "bool"}, {"name": "correctness_llama", "sequence": "bool"}, {"name": "finish_reasons", "sequence": "string"}, {"name": "correctness_count", "dtype": "int64"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 4964543659, "num_examples": 93733}], "download_size": 2149897914, "dataset_size": 4964543659}, {"config_name": "extended", "features": [{"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "problem_type", "dtype": "string"}, {"name": "question_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "uuid", "dtype": "string"}, {"name": "is_reasoning_complete", "sequence": "bool"}, {"name": "generations", "sequence": "string"}, {"name": "correctness_math_verify", "sequence": "bool"}, {"name": "correctness_llama", "sequence": "bool"}, {"name": "finish_reasons", "sequence": "string"}, {"name": "correctness_count", "dtype": "int64"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 4769566550, "num_examples": 131396}], "download_size": 2063936457, "dataset_size": 4769566550}]} | false | null | 2025-02-18T11:45:27 | 526 | 11 | false | e4e141ec9dea9f8326f4d347be56105859b2bd68 |
OpenR1-Math-220k
Dataset description
OpenR1-Math-220k is a large-scale dataset for mathematical reasoning. It consists of 220k math problems with two to four reasoning traces generated by DeepSeek R1 for problems from NuminaMath 1.5.
The traces were verified using Math Verify for most samples and Llama-3.3-70B-Instruct as a judge for 12% of the samples, and each problem contains at least one reasoning trace with a correct answer.
The dataset consists of two splits:… See the full description on the dataset page: https://huggingface.co/datasets/open-r1/OpenR1-Math-220k. | 52,617 | 73,322 | [
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"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-10T13:41:48 | null | null |
67e1d4abcf3845b431426e00 | fibonacciai/shahname | fibonacciai | {"license": "apache-2.0"} | false | null | 2025-03-24T21:58:42 | 11 | 11 | false | e8cabbe038e14a1beab6ca817e24ed065c153d14 | null | 20 | 20 | [
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] | 2025-03-24T21:54:51 | null | null |
67e1d78e8d10b2d2eda58934 | fibonacciai/Persian-Wikipedia-QA | fibonacciai | {"license": "apache-2.0"} | false | null | 2025-03-24T22:17:36 | 11 | 11 | false | 244031478b3150add15e952d2ae51e369c891989 | null | 20 | 20 | [
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"library:polars",
"region:us"
] | 2025-03-24T22:07:10 | null | null |
679847425963ddcff04d065c | fibonacciai/Persian-llm-fibonacci-1-pro | fibonacciai | {"license": "cc-by-nc-4.0", "language": ["en", "fa"], "tags": ["persian", "persian llm", "iranian", "ai", "fibonacci", "fibonacciai", "realrobot"], "pretty_name": "a", "size_categories": ["1B<n<10B"]} | false | null | 2025-01-28T22:58:29 | 19 | 10 | false | 3779f9e958b86e1b680735335b17b0bf627c45fe |
Persian-llm-fibonacci-1-7b-chat.P1_0 🌟
Description 📄
The Persian-llm-fibonacci-1-7b-chat.P1_0 is a 1.7 billion parameter language model (LLM) specifically designed for Persian-language chat and text interactions. Developed as part of the FibonacciAI project, this model is optimized to generate fluent and natural Persian text, making it ideal for conversational AI applications.
Built on advanced language model architectures (e.g., GPT), it excels in tasks like chat… See the full description on the dataset page: https://huggingface.co/datasets/fibonacciai/Persian-llm-fibonacci-1-pro. | 57 | 143 | [
"language:en",
"language:fa",
"license:cc-by-nc-4.0",
"size_categories:1B<n<10B",
"region:us",
"persian",
"persian llm",
"iranian",
"ai",
"fibonacci",
"fibonacciai",
"realrobot"
] | 2025-01-28T02:56:02 | null | null |
67dbb87a09b2df33607508e0 | snuh/ClinicalQA | snuh | {"license": "apache-2.0", "dataset_info": {"features": [{"name": "question_id", "dtype": "int64"}, {"name": "chief_complaint", "dtype": "string"}, {"name": "purpose", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "exam", "dtype": "string"}, {"name": "options", "struct": [{"name": "option_A", "dtype": "string"}, {"name": "option_B", "dtype": "string"}, {"name": "option_C", "dtype": "string"}, {"name": "option_D", "dtype": "string"}, {"name": "option_E", "dtype": "string"}]}, {"name": "answer", "dtype": "string"}, {"name": "explanation", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3477660, "num_examples": 1015}], "download_size": 1641371, "dataset_size": 3477660}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-03-21T07:21:29 | 10 | 10 | false | 924db36bd2780b706b82c4a2ec7339e5eaaabe03 |
SNUH-HARI/ClinicalQA
Curated and shared by: SNUH-HARI (Seoul National University Hospital Healthcare AI Research Institute)
Language(s) (NLP): Korean
Repository: SNUH-HARI/ClinicalQA
Dataset Summary
The ClinicalQA dataset is designed for Korean medical knowledge question-answering. This dataset includes questions and answers at the level of the national medical licensing examination and consists of problems based on various chief complaints and medical specialties.… See the full description on the dataset page: https://huggingface.co/datasets/snuh/ClinicalQA. | 301 | 301 | [
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-20T06:40:58 | null | null |
67e09e26768ad57609dd4d57 | fibonacciai/fibonacci-2025 | fibonacciai | {"license": "apache-2.0", "task_categories": ["text-classification"], "language": ["fa", "en"]} | false | null | 2025-03-24T23:01:14 | 10 | 10 | false | e2bbed0f4e8c6c5dde26ebf1cbbfbb809765abae | null | 35 | 35 | [
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-23T23:49:58 | null | null |
67954a35c16b74e280f72f15 | ServiceNow-AI/R1-Distill-SFT | ServiceNow-AI | {"license": "cc-by-nc-sa-4.0", "configs": [{"config_name": "v0", "data_files": [{"split": "train", "path": "v0/train-*"}]}, {"config_name": "v1", "data_files": [{"split": "train", "path": "v1/train-*"}]}], "dataset_info": [{"config_name": "v0", "features": [{"name": "id", "dtype": "string"}, {"name": "reannotated_assistant_content", "dtype": "string"}, {"name": "problem", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "verified", "dtype": "null"}, {"name": "quality_metrics", "dtype": "null"}], "splits": [{"name": "train", "num_bytes": 1279431141, "num_examples": 171647}], "download_size": 554111459, "dataset_size": 1279431141}, {"config_name": "v1", "features": [{"name": "id", "dtype": "string"}, {"name": "reannotated_assistant_content", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "reannotated_messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "source_dataset", "dtype": "string"}, {"name": "verified", "dtype": "null"}, {"name": "quality_metrics", "dtype": "null"}], "splits": [{"name": "train", "num_bytes": 25783989151, "num_examples": 1679162}], "download_size": 11128580062, "dataset_size": 25783989151}]} | false | null | 2025-02-08T22:46:58 | 289 | 9 | false | 16e851e107d928b9069dcce428a2d3d7154e5353 |
🔉 𝗦𝗟𝗔𝗠 𝗹𝗮𝗯 - 𝗥𝟭-𝗗𝗶𝘀𝘁𝗶𝗹𝗹-𝗦𝗙𝗧 Dataset
Lewis Tunstall, Ed Beeching, Loubna Ben Allal, Clem Delangue 🤗 and others at Hugging Face announced today that they are - 𝗼𝗽𝗲𝗻𝗹𝘆 𝗿𝗲𝗽𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗥𝟭 🔥
We at 𝗦𝗟𝗔𝗠 𝗹𝗮𝗯 (ServiceNow Language Models) have been cooking up something as well.
Inspired by Open-r1, we have decided to open source the data stage-by-stage to support the open source community.
𝗕𝗼𝗼𝗸𝗺𝗮𝗿𝗸 this page!
KEY DETAILS:
⚗️ Distilled… See the full description on the dataset page: https://huggingface.co/datasets/ServiceNow-AI/R1-Distill-SFT. | 3,717 | 11,109 | [
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"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-01-25T20:31:49 | null | null |
67a404bc8c6d42c5ec097433 | Anthropic/EconomicIndex | Anthropic | {"license": "mit", "pretty_name": "EconomicIndex", "tags": ["text"], "viewer": true, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "onet_task_mappings.csv"}]}]} | false | null | 2025-02-10T19:28:32 | 202 | 9 | false | 218b35116baa43c55beffe61f243bd81f5f84cf8 |
Overview
This directory contains O*NET task mapping and automation vs. augmentation data from "Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations." The data and provided analysis are described below.
Please see our blog post and paper for further visualizations and complete analysis.
Data
SOC_Structure.csv - Standard Occupational Classification (SOC) system hierarchy from the U.S. Department of Labor O*NET database… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/EconomicIndex. | 2,652 | 8,285 | [
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"region:us",
"text"
] | 2025-02-06T00:39:24 | 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}], 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"splits": [{"name": "train", "num_bytes": 161000625, "num_examples": 99842}], "download_size": 47518592, "dataset_size": 161000625}, {"config_name": "business_ethics", "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": 31619, "dataset_size": 57303.3562203159}, {"config_name": "clinical_knowledge", "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", 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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… See the full description on the dataset page: https://huggingface.co/datasets/cais/mmlu. | 147,369 | 37,169,681 | [
"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:mlcroissant",
"library:polars",
"arxiv:2009.03300",
"arxiv:2005.00700",
"arxiv:2005.14165",
"arxiv:2008.02275",
"region:us"
] | 2022-03-02T23:29:22 | mmlu | null |
641debae1d05404efd046a4f | yahma/alpaca-cleaned | yahma | {"license": "cc-by-4.0", "language": ["en"], "tags": ["instruction-finetuning"], "pretty_name": "Alpaca-Cleaned", "task_categories": ["text-generation"]} | false | null | 2023-04-10T20:29:06 | 665 | 8 | false | 12567cabf869d7c92e573c7c783905fc160e9639 |
Dataset Card for Alpaca-Cleaned
Repository: https://github.com/gururise/AlpacaDataCleaned
Dataset Description
This is a cleaned version of the original Alpaca Dataset released by Stanford. The following issues have been identified in the original release and fixed in this dataset:
Hallucinations: Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer.
"instruction":"Summarize the… See the full description on the dataset page: https://huggingface.co/datasets/yahma/alpaca-cleaned. | 23,606 | 614,908 | [
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"instruction-finetuning"
] | 2023-03-24T18:27:58 | null | null |
64df8d51a7d1b71134c1fbdf | ticoAg/Chinese-medical-dialogue | ticoAg | {"license": "apache-2.0", "raw csv": "356 MB", "examples": 799743} | false | null | 2023-08-18T15:33:15 | 50 | 8 | false | a8569e1504bd818714cdcabdff668557ed269803 |
Note
process data from Chinese-medical-dialogue-data
单轮医患对话
raw data samples
department
title
ask
answer
心血管科
高血压患者能吃党参吗？
我有高血压这两天女婿来的时候给我拿了些党参泡水喝，您好高血压可以吃党参吗？
高血压病人可以口服党参的。党参有降血脂，降血压的作用，可以彻底消除血液中的垃圾，从而对冠心病以及心血管疾病的患者都有一定的稳定预防工作作用，因此平时口服党参能远离三高的危害。另外党参除了益气养血，降低中枢神经作用，调整消化系统功能，健脾补肺的功能。感谢您的进行咨询，期望我的解释对你有所帮助。
内分泌科
糖尿病还会进行遗传吗？
糖尿病有隔代遗传吗？我妈是糖尿病，很多年了，也没养好，我现在也是，我妹子也是，我儿子现在二十岁，没什么问题，但是以后会不会也得糖尿病啊，真是难过，我现在就已经开始让他控制点吃东西。… See the full description on the dataset page: https://huggingface.co/datasets/ticoAg/Chinese-medical-dialogue. | 601 | 1,456 | [
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"region:us"
] | 2023-08-18T15:25:05 | null | null |
652c26161a3250bbfe6b96d0 | AI4Math/MathVista | AI4Math | {"annotations_creators": ["expert-generated", "found"], "language_creators": ["expert-generated", "found"], "language": ["en", "zh", "fa"], "license": "cc-by-sa-4.0", "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["multiple-choice", "question-answering", "visual-question-answering", "text-classification"], "task_ids": ["multiple-choice-qa", "closed-domain-qa", "open-domain-qa", "visual-question-answering", "multi-class-classification"], "paperswithcode_id": "mathvista", "pretty_name": "MathVista", "tags": ["multi-modal-qa", "math-qa", "figure-qa", "geometry-qa", "math-word-problem", "textbook-qa", "vqa", "arithmetic-reasoning", "statistical-reasoning", "algebraic-reasoning", "geometry-reasoning", "numeric-common-sense", "scientific-reasoning", "logical-reasoning", "geometry-diagram", "synthetic-scene", "chart", "plot", "scientific-figure", "table", "function-plot", "abstract-scene", "puzzle-test", "document-image", "medical-image", "mathematics", "science", "chemistry", "biology", "physics", "engineering", "natural-science"], "configs": [{"config_name": "default", "data_files": [{"split": "testmini", "path": "data/testmini-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "pid", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "string"}, {"name": "decoded_image", "dtype": "image"}, {"name": "choices", "sequence": "string"}, {"name": "unit", "dtype": "string"}, {"name": "precision", "dtype": "float64"}, {"name": "answer", "dtype": "string"}, {"name": "question_type", "dtype": "string"}, {"name": "answer_type", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "category", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "grade", "dtype": "string"}, {"name": "img_height", "dtype": "int64"}, {"name": "img_width", "dtype": "int64"}, {"name": "language", "dtype": "string"}, {"name": "skills", "sequence": "string"}, {"name": "source", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "task", "dtype": "string"}]}, {"name": "query", "dtype": "string"}], "splits": [{"name": "testmini", "num_bytes": 142635198, "num_examples": 1000}, {"name": "test", "num_bytes": 648291350.22, "num_examples": 5141}], "download_size": 885819490, "dataset_size": 790926548.22}} | false | null | 2024-02-11T23:09:05 | 143 | 8 | false | 2b6ad69445fbb5695c9b165475e8decdbeb97747 |
Dataset Card for MathVista
Dataset Description
Paper Information
Dataset Examples
Leaderboard
Dataset Usage
Data Downloading
Data Format
Data Visualization
Data Source
Automatic Evaluation
License
Citation
Dataset Description
MathVista is a consolidated Mathematical reasoning benchmark within Visual contexts. It consists of three newly created datasets, IQTest, FunctionQA, and PaperQA, which address the missing visual domains and are tailored to evaluate… See the full description on the dataset page: https://huggingface.co/datasets/AI4Math/MathVista. | 11,560 | 122,274 | [
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"mathematics",
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"biology",
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] | 2023-10-15T17:49:10 | mathvista | null |
66c84764a47b2d6c582bbb02 | amphion/Emilia-Dataset | amphion | {"license": "cc-by-4.0", "task_categories": ["text-to-speech", "automatic-speech-recognition"], "language": ["zh", "en", "ja", "fr", "de", "ko"], "pretty_name": "Emilia", "size_categories": ["10M<n<100M"], "extra_gated_prompt": "Terms of Access: The researcher has requested permission to use the Emilia dataset, the Emilia-Pipe preprocessing pipeline, and the Emilia-Yodas dataset. In exchange for such permission, the researcher hereby agrees to the following terms and conditions:\n1. The researcher shall use the Emilia dataset under the CC-BY-NC license and\n the Emilia-YODAS dataset under the CC-BY license.\n2. The authors make no representations or warranties regarding the datasets,\n including but not limited to warranties of non-infringement or fitness for\n a particular purpose.\n3. The researcher accepts full responsibility for their use of the datasets and\n shall defend and indemnify the authors of Emilia, Emilia-Pipe, and\n Emilia-Yodas, including their employees, trustees, officers, and agents,\n against any and all claims arising from the researcher's use of the datasets,\n including but not limited to the researcher's use of any copies of copyrighted\n content that they may create from the datasets.\n4. The researcher may provide research associates and colleagues with access\n to the datasets, provided that they first agree to be bound by these terms\n and conditions.\n5. The authors reserve the right to terminate the researcher's access to the\n datasets at any time.\n6. If the researcher is employed by a for-profit, commercial entity, the\n researcher's employer shall also be bound by these terms and conditions,\n and the researcher hereby represents that they are fully authorized to enter\n into this agreement on behalf of such employer.", "extra_gated_fields": {"Name": "text", "Email": "text", "Affiliation": "text", "Position": "text", "Your Supervisor/manager/director": "text", "I agree to the Terms of Access": "checkbox"}} | false | null | 2025-02-28T05:41:37 | 279 | 8 | false | d7f2f7340a6385696f3766c8049fa920a4707c07 |
Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation
This is the official repository 👑 for the Emilia dataset and the source code for the Emilia-Pipe speech data preprocessing pipeline.
News 🔥
2025/02/26: The Emilia-Large dataset, featuring over 200,000 hours of data, is now available!!! Emilia-Large combines the original 101k-hour Emilia dataset (licensed under CC BY-NC 4.0) with the brand-new 114k-hour Emilia-YODAS… See the full description on the dataset page: https://huggingface.co/datasets/amphion/Emilia-Dataset. | 133,682 | 348,130 | [
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67d9fd082ad0bffeb5bbc771 | HuggingFaceTB/issues-kaggle-notebooks | HuggingFaceTB | {"dataset_info": [{"config_name": "issues", "features": [{"name": "repo_name", "dtype": "string"}, {"name": "issue_id", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 30986711842, "num_examples": 15549682}], "download_size": 16370074732, "dataset_size": 30986711842}, {"config_name": "kaggle", "features": [{"name": "file_id", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5209133899, "num_examples": 580195}], "download_size": 2222724371, "dataset_size": 5209133899}], "configs": [{"config_name": "issues", "data_files": [{"split": "train", "path": "issues/train-*"}]}, {"config_name": "kaggle", "data_files": [{"split": "train", "path": "kaggle/train-*"}]}]} | false | null | 2025-03-19T20:00:18 | 8 | 8 | false | ef882ad1ed8274340e8fc9bac087c903f2f75396 |
GitHub Issues & Kaggle Notebooks
Description
GitHub Issues & Kaggle Notebooks is a collection of two code datasets intended for language models training, they are sourced from GitHub issues and notebooks in Kaggle platform. These datasets are a modified part of the StarCoder2 model training corpus, precisely the bigcode/StarCoder2-Extras dataset. We reformat the samples to remove StarCoder2's special tokens and use natural text to delimit comments in issues and display… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/issues-kaggle-notebooks. | 232 | 232 | [
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] | 2025-03-18T23:08:56 | null | null |
67e46df98c0347025bba131b | sychonix/emotion-clone | sychonix | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "sadness", "1": "joy", "2": "love", "3": "anger", "4": "fear", "5": "surprise"}}}}], "splits": [{"name": "train", "num_bytes": 1741533, "num_examples": 16000}, {"name": "validation", "num_bytes": 214695, "num_examples": 2000}, {"name": "test", "num_bytes": 217173, "num_examples": 2000}], "download_size": 1281072, "dataset_size": 2173401}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]} | false | null | 2025-03-26T21:13:34 | 8 | 8 | false | 5a355b76cee6387d370d99d7ff656e79cc10d2eb | null | 0 | 0 | [
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"library:pandas",
"library:mlcroissant",
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"region:us"
] | 2025-03-26T21:13:29 | null | null |
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