Dataset Viewer
_id
stringlengths 24
24
| id
stringlengths 5
121
| author
stringlengths 2
42
| cardData
stringlengths 2
1.07M
⌀ | disabled
bool 2
classes | gated
null | lastModified
timestamp[ns]date 2021-02-05 16:03:35
2025-04-02 23:31:22
| likes
int64 0
7.66k
| trendingScore
float64 -1
66
| private
bool 1
class | sha
stringlengths 40
40
| description
stringlengths 0
6.67k
⌀ | downloads
int64 0
4.62M
| downloadsAllTime
int64 0
142M
| tags
sequencelengths 1
7.92k
| createdAt
timestamp[ns]date 2022-03-02 23:29:22
2025-04-02 23:29:40
| paperswithcode_id
stringclasses 653
values | citation
stringlengths 0
10.7k
⌀ |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 | 295 | 66 | 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. | 10,290 | 10,299 | [
"license:cc-by-4.0",
"size_categories:10M<n<100M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"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 | 154 | 54 | 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. | 8,311 | 8,387 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-27T20:40:05 | 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"], "configs": [{"config_name": "am_0.5M", "data_files": "am_0.5M.jsonl.zst", "features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "info", "struct": [{"name": "answer_content", "dtype": "string"}, {"name": "reference_answer", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "test_case", "struct": [{"name": "test_code", "dtype": "string"}, {"name": "test_entry_point", "dtype": "string"}]}, {"name": "think_content", "dtype": "string"}]}, {"name": "role", "dtype": "string"}]}]}, {"config_name": "am_0.9M", "data_files": "am_0.9M.jsonl.zst", "features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "info", "struct": [{"name": "answer_content", "dtype": "string"}, {"name": "reference_answer", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "test_case", "struct": [{"name": "test_code", "dtype": "string"}, {"name": "test_entry_point", "dtype": "string"}]}, {"name": "think_content", "dtype": "string"}]}, {"name": "role", "dtype": "string"}]}]}, {"config_name": "am_0.9M_sample_1k", "data_files": "am_0.9M_sample_1k.jsonl", "features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "info", "struct": [{"name": "answer_content", "dtype": "string"}, {"name": "reference_answer", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "test_case", "struct": [{"name": "test_code", "dtype": "string"}, {"name": "test_entry_point", "dtype": "string"}]}, {"name": "think_content", "dtype": "string"}]}, {"name": "role", "dtype": "string"}]}]}]} | false | null | 2025-03-30T01:30:08 | 102 | 53 | false | 53531c06634904118a2dcd83961918c4d69d1cdf | For more open-source datasets, models, and methodologies, please visit our GitHub repository.
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… See the full description on the dataset page: https://huggingface.co/datasets/a-m-team/AM-DeepSeek-R1-Distilled-1.4M. | 7,348 | 7,348 | [
"task_categories:text-generation",
"language:zh",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:1M<n<10M",
"arxiv:2503.19633",
"region:us",
"code",
"math",
"reasoning",
"thinking",
"deepseek-r1",
"distill"
] | 2025-03-09T10:23:33 | 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 | 599 | 52 | 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. | 24,396 | 50,257 | [
"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 |
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 | 65 | 38 | 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. | 78 | 78 | [
"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 |
67a404bc8c6d42c5ec097433 | Anthropic/EconomicIndex | Anthropic | {"language": "en", "pretty_name": "EconomicIndex", "tags": ["AI", "LLM", "Economic Impacts", "Anthropic"], "viewer": true, "license": "mit", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "release_2025_03_27/automation_vs_augmentation_by_task.csv"}]}]} | false | null | 2025-03-27T22:08:25 | 239 | 37 | false | 2f63ea41bda89c22c00bbd3dd487771087717614 |
The Anthropic Economic Index
Overview
The Anthropic Economic Index provides insights into how AI is being incorporated into real-world tasks across the modern economy.
Data Releases
This repository contains multiple data releases, each with its own documentation:
2025-02-10 Release: Initial release with O*NET task mappings, automation vs. augmentation data, and more
2025-03-27 Release: Updated analysis with Claude 3.7 Sonnet data and cluster-level insights… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/EconomicIndex. | 3,349 | 9,876 | [
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"AI",
"LLM",
"Economic Impacts",
"Anthropic"
] | 2025-02-06T00:39:24 | null | null |
67ea45bbcb39affecc10763e | virtuoussy/Multi-subject-RLVR | virtuoussy | {"license": "apache-2.0", "task_categories": ["question-answering"], "language": ["en"]} | false | null | 2025-04-02T10:29:40 | 35 | 35 | false | 5be8ffa52bf3ccbfe0d4f601ddee1183cb1be0ab | Multi-subject data for paper "Expanding RL with Verifiable Rewards Across Diverse Domains".
we use a multi-subject multiple-choice QA dataset ExamQA (Yu et al., 2021).
Originally written in Chinese, ExamQA covers at least 48 first-level subjects.
We remove the distractors and convert each instance into a free-form QA pair.
This dataset consists of 638k college-level instances, with both questions and objective answers written by domain experts for examination purposes.
We also use GPT-4o-mini… See the full description on the dataset page: https://huggingface.co/datasets/virtuoussy/Multi-subject-RLVR. | 119 | 119 | [
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2503.23829",
"region:us"
] | 2025-03-31T07:35:23 | null | null |
67e5170dd9b7021d4a7f48be | Rapidata/OpenAI-4o_t2i_human_preference | Rapidata | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "image1", "dtype": "image"}, {"name": "image2", "dtype": "image"}, {"name": "model1", "dtype": "string"}, {"name": "model2", "dtype": "string"}, {"name": "weighted_results_image1_preference", "dtype": "float32"}, {"name": "weighted_results_image2_preference", "dtype": "float32"}, {"name": "detailed_results_preference", "dtype": "string"}, {"name": "weighted_results_image1_coherence", "dtype": "float32"}, {"name": "weighted_results_image2_coherence", "dtype": "float32"}, {"name": "detailed_results_coherence", "dtype": "string"}, {"name": "weighted_results_image1_alignment", "dtype": "float32"}, {"name": "weighted_results_image2_alignment", "dtype": "float32"}, {"name": "detailed_results_alignment", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10832696953, "num_examples": 13000}], "download_size": 5203247080, "dataset_size": 10832696953}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "cdla-permissive-2.0", "task_categories": ["text-to-image", "image-to-text", "image-classification", "reinforcement-learning"], "language": ["en"], "tags": ["Human", "Preference", "Coherence", "Alignment", "country", "language", "flux", "midjourney", "dalle3", "stabeldiffusion", "alignment", "flux1.1", "flux1", "imagen3", "aurora", "lumina", "recraft", "recraft v2", "ideogram", "frames", "OpenAI 4o", "4o", "OpenAI"], "size_categories": ["10K<n<100K"], "pretty_name": "OpenAI 4o vs. Ideogram V2 / Recraft V2 / Lumina-15-2-25 / Frames-23-1-25 / Aurora / imagen-3 / Flux-1.1-pro / Flux-1-pro / Dalle-3 / Midjourney-5.2 / Stabel-Diffusion-3 - Human Preference Dataset"} | false | null | 2025-03-28T20:00:43 | 28 | 28 | false | 9fafb39b4bb3bac6e2fbabd13503fa1199fde400 |
Rapidata OpenAI 4o Preference
This T2I dataset contains over 200'000 human responses from over ~45,000 individual annotators, collected in less than half a day using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating OpenAI 4o (version from 26.3.2025) across three categories: preference, coherence, and alignment.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/OpenAI-4o_t2i_human_preference. | 780 | 780 | [
"task_categories:text-to-image",
"task_categories:image-to-text",
"task_categories:image-classification",
"task_categories:reinforcement-learning",
"language:en",
"license:cdla-permissive-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"Human",
"Preference",
"Coherence",
"Alignment",
"country",
"language",
"flux",
"midjourney",
"dalle3",
"stabeldiffusion",
"alignment",
"flux1.1",
"flux1",
"imagen3",
"aurora",
"lumina",
"recraft",
"recraft v2",
"ideogram",
"frames",
"OpenAI 4o",
"4o",
"OpenAI"
] | 2025-03-27T09:14:53 | 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 | 2025-03-28T15:26:57 | 40 | 27 | false | c41b695c60b0af3c3701e41d483031246c378088 |
II-Thought RL v0: A Large-Scale Curated Dataset for Reinforcement Learning
See our blog here for additional details.
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… See the full description on the dataset page: https://huggingface.co/datasets/Intelligent-Internet/II-Thought-RL-v0. | 2,676 | 2,676 | [
"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 |
67e46df98c0347025bba131b | sychonix/emotion | 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 | 25 | 25 | false | 5a355b76cee6387d370d99d7ff656e79cc10d2eb | null | 530 | 530 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-26T21:13:29 | null | null |
67e52d5dc04bd73d2f46a929 | MrDragonFox/Elise | MrDragonFox | {"license": "mit"} | false | null | 2025-03-27T11:22:24 | 22 | 22 | false | ee867f95526856352ba9c607e6f97e6b9c65b043 | this is very much a clone of
https://huggingface.co/datasets/Jinsaryko/Elise
but with classified emotions like laughs and giggles
not ment to be comprehenive - its about 3h in total and will be enough to for a finetuned voice and some basic emotional tags
short but sweet - acts as demo test set
"giggles - 76",
"laughs - 336",
"long pause - 2",
"chuckles - 20",
"whispers - 2",
"normal volume - 2",
"sighs - 156",
"clicks tongue - 2",
"gasps - 4",
"moans - 8",
"sonora - 2",
"habla en inglés -… See the full description on the dataset page: https://huggingface.co/datasets/MrDragonFox/Elise. | 1,090 | 1,090 | [
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-27T10:50:05 | 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 | 608 | 20 | 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. | 5,743 | 10,899 | [
"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"
] | 2025-02-17T11:45:09 | 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 | 477 | 19 | 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. | 12,110 | 17,042 | [
"task_categories:text-generation",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2502.13124",
"region:us"
] | 2025-01-30T23:29:32 | null | null |
67d97c4be2b27852325fd8e2 | nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim | nvidia | {"license": "cc-by-4.0"} | false | null | 2025-04-02T02:27:47 | 100 | 19 | false | 8fc782b6de78e17914dd52053c9c680e4bde8fb1 |
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. | 28,199 | 28,199 | [
"license:cc-by-4.0",
"region:us"
] | 2025-03-18T13:59:39 | null | null |
67e90b135e63bac35a2dbaf0 | MohamedRashad/Quran-Recitations | MohamedRashad | {"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "audio", "dtype": "audio"}], "splits": [{"name": "train", "num_bytes": 49579449331.918, "num_examples": 124689}], "download_size": 33136131149, "dataset_size": 49579449331.918}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "task_categories": ["automatic-speech-recognition", "text-to-speech"], "language": ["ar"], "size_categories": ["100K<n<1M"]} | false | null | 2025-03-30T11:19:54 | 18 | 18 | false | 65ee6114d526c02f7f96d696bb254a2dd666270c |
Quran-Recitations Dataset
Overview
The Quran-Recitations dataset is a rich and reverent collection of Quranic verses, meticulously paired with their respective recitations by esteemed Qaris. This dataset serves as a valuable resource for researchers, developers, and students interested in Quranic studies, speech recognition, audio analysis, and Islamic applications.
Dataset Structure
source: The name of the Qari (reciter) who performed… See the full description on the dataset page: https://huggingface.co/datasets/MohamedRashad/Quran-Recitations. | 124 | 124 | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"language:ar",
"size_categories:100K<n<1M",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-30T09:12:51 | 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 | 675 | 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. | 335,232 | 4,290,526 | [
"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 |
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", "dtype": "string"}, {"name": "generation", "dtype": "string"}, {"name": "finish_reason", "dtype": "string"}, {"name": "api_metadata", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "prompt_tokens_details", "dtype": "null"}, {"name": "total_tokens", "dtype": "int64"}]}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 994149425, "num_examples": 35718}], "download_size": 274975300, "dataset_size": 994149425}, {"config_name": "solutions", "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": "int64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "input_format", "dtype": "string"}, {"name": "output_format", "dtype": "string"}, {"name": "examples", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "note", "dtype": "string"}, {"name": "editorial", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "generation", "dtype": "string"}, {"name": "finish_reason", "dtype": "string"}, {"name": "api_metadata", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "prompt_tokens_details", "dtype": "null"}, {"name": "total_tokens", "dtype": "int64"}]}, {"name": "interaction_format", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 4968074271, "num_examples": 47780}], "download_size": 1887049179, "dataset_size": 4968074271}, {"config_name": "solutions_decontaminated", "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": "examples", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "note", "dtype": "string"}, {"name": "editorial", "dtype": "string"}, {"name": "problem", "dtype": "string"}, {"name": "generation", "dtype": "string"}, {"name": "finish_reason", "dtype": "string"}, {"name": "api_metadata", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "prompt_tokens_details", "dtype": "null"}, {"name": "total_tokens", "dtype": "int64"}]}, {"name": "interaction_format", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "problem_type", "dtype": "string"}, {"name": "public_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "private_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "generated_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "public_tests_ms", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "failed_solutions", "list": [{"name": "code", "dtype": "string"}, {"name": "passedTestCount", "dtype": "int64"}, {"name": "programmingLanguage", "dtype": "string"}, {"name": "verdict", "dtype": "string"}]}, {"name": "accepted_solutions", "list": [{"name": "code", "dtype": "string"}, {"name": "passedTestCount", "dtype": "int64"}, {"name": "passed_test_count", "dtype": "null"}, {"name": "programmingLanguage", "dtype": "string"}, {"name": "programming_language", "dtype": "string"}, {"name": "submission_id", "dtype": "string"}, {"name": "verdict", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 6719356671, "num_examples": 40665}], "download_size": 2023394671, "dataset_size": 6719356671}, {"config_name": "solutions_py", "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", "dtype": "string"}, {"name": "generation", "dtype": "string"}, {"name": "finish_reason", "dtype": "string"}, {"name": "api_metadata", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "prompt_tokens_details", "dtype": "null"}, {"name": "total_tokens", "dtype": "int64"}]}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1000253222, "num_examples": 9556}], "download_size": 411697337, "dataset_size": 1000253222}, {"config_name": "solutions_py_decontaminated", "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", "dtype": "string"}, {"name": "generation", "dtype": "string"}, {"name": "finish_reason", "dtype": "string"}, {"name": "api_metadata", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "prompt_tokens_details", "dtype": "null"}, {"name": "total_tokens", "dtype": "int64"}]}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "accepted_solutions", "list": [{"name": "code", "dtype": "string"}, {"name": "passedTestCount", "dtype": "int64"}, {"name": "passed_test_count", "dtype": "null"}, {"name": "programmingLanguage", "dtype": "string"}, {"name": "programming_language", "dtype": "string"}, {"name": "submission_id", "dtype": "string"}, {"name": "verdict", "dtype": "string"}]}, {"name": "failed_solutions", "list": [{"name": "code", "dtype": "string"}, {"name": "passedTestCount", "dtype": "int64"}, {"name": "programmingLanguage", "dtype": "string"}, {"name": "verdict", "dtype": "string"}]}, {"name": "generated_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "private_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "problem_type", "dtype": "string"}, {"name": "public_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "public_tests_ms", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1349328880, "num_examples": 8133}], "download_size": 500182086, "dataset_size": 1349328880}, {"config_name": "solutions_short_and_long_decontaminated", "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": "examples", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "note", "dtype": "string"}, {"name": "editorial", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "generation", "dtype": "string"}, {"name": "finish_reason", "dtype": "string"}, {"name": "api_metadata", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "prompt_tokens_details", "dtype": "null"}, {"name": "total_tokens", "dtype": "int64"}]}, {"name": "interaction_format", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "accepted_solutions", "list": [{"name": "code", "dtype": "string"}, {"name": "passedTestCount", "dtype": "int64"}, {"name": "passed_test_count", "dtype": "null"}, {"name": "programmingLanguage", "dtype": "string"}, {"name": "programming_language", "dtype": "string"}, {"name": "submission_id", "dtype": "string"}, {"name": "verdict", "dtype": "string"}]}, {"name": "failed_solutions", "list": [{"name": "code", "dtype": "string"}, {"name": "passedTestCount", "dtype": "int64"}, {"name": "programmingLanguage", "dtype": "string"}, {"name": "verdict", "dtype": "string"}]}, {"name": "generated_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "private_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "problem_type", "dtype": "string"}, {"name": "public_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "public_tests_ms", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 2699204607, "num_examples": 16266}], "download_size": 1002365269, "dataset_size": 2699204607}, {"config_name": "solutions_w_editorials", "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": "int64"}, {"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", "dtype": "string"}, {"name": "generation", "dtype": "string"}, {"name": "finish_reason", "dtype": "string"}, {"name": "api_metadata", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "prompt_tokens_details", "dtype": "null"}, {"name": "total_tokens", "dtype": "int64"}]}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 2649620432, "num_examples": 29180}], "download_size": 972089090, "dataset_size": 2649620432}, {"config_name": "solutions_w_editorials_decontaminated", "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": "int64"}, {"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", "dtype": "string"}, {"name": "generation", "dtype": "string"}, {"name": "finish_reason", "dtype": "string"}, {"name": "api_metadata", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "prompt_tokens_details", "dtype": "null"}, {"name": "total_tokens", "dtype": "int64"}]}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "accepted_solutions", "list": [{"name": "code", "dtype": "string"}, {"name": "passedTestCount", "dtype": "int64"}, {"name": "passed_test_count", "dtype": "null"}, {"name": "programmingLanguage", "dtype": "string"}, {"name": "programming_language", "dtype": "string"}, {"name": "submission_id", "dtype": "string"}, {"name": "verdict", "dtype": "string"}]}, {"name": "failed_solutions", "list": [{"name": "code", "dtype": "string"}, {"name": "passedTestCount", "dtype": "int64"}, {"name": "programmingLanguage", "dtype": "string"}, {"name": "verdict", "dtype": "string"}]}, {"name": "generated_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "private_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "problem_type", "dtype": "string"}, {"name": "public_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "public_tests_ms", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 3738669884, "num_examples": 24490}], "download_size": 1012247387, "dataset_size": 3738669884}, {"config_name": "solutions_w_editorials_py", "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", "dtype": "string"}, {"name": "generation", "dtype": "string"}, {"name": "finish_reason", "dtype": "string"}, {"name": "api_metadata", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "prompt_tokens_details", "dtype": "null"}, {"name": "total_tokens", "dtype": "int64"}]}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1067124847, "num_examples": 11672}], "download_size": 415023817, "dataset_size": 1067124847}, {"config_name": "solutions_w_editorials_py_decontaminated", "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", "dtype": "string"}, {"name": "generation", "dtype": "string"}, {"name": "finish_reason", "dtype": "string"}, {"name": "api_metadata", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "prompt_tokens_details", "dtype": "null"}, {"name": "total_tokens", "dtype": "int64"}]}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "accepted_solutions", "list": [{"name": "code", "dtype": "string"}, {"name": "passedTestCount", "dtype": "int64"}, {"name": "passed_test_count", "dtype": "null"}, {"name": "programmingLanguage", "dtype": "string"}, {"name": "programming_language", "dtype": "string"}, {"name": "submission_id", "dtype": "string"}, {"name": "verdict", "dtype": "string"}]}, {"name": "failed_solutions", "list": [{"name": "code", "dtype": "string"}, {"name": "passedTestCount", "dtype": "int64"}, {"name": "programmingLanguage", "dtype": "string"}, {"name": "verdict", "dtype": "string"}]}, {"name": "generated_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "private_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "problem_type", "dtype": "string"}, {"name": "public_tests", "struct": [{"name": "input", "sequence": "string"}, {"name": "output", "sequence": "string"}]}, {"name": "public_tests_ms", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1499075280, "num_examples": 9796}], "download_size": 466078291, "dataset_size": 1499075280}, {"config_name": "test_input_generator", "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": "examples", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "note", "dtype": "string"}, {"name": "editorial", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "generation", "dtype": "string"}, {"name": "finish_reason", "dtype": "string"}, {"name": "api_metadata", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "completion_tokens_details", "dtype": "null"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "prompt_tokens_details", "dtype": "null"}, {"name": "total_tokens", "dtype": "int64"}]}, {"name": "interaction_format", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1851104290, "num_examples": 20620}], "download_size": 724157877, "dataset_size": 1851104290}], "configs": [{"config_name": "checker_interactor", "data_files": [{"split": "train", "path": "checker_interactor/train-*"}]}, {"config_name": "solutions", "default": true, "data_files": [{"split": "train", "path": "solutions/train-*"}]}, {"config_name": "solutions_decontaminated", "data_files": [{"split": "train", "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_short_and_long_decontaminated", "data_files": [{"split": "train", "path": "solutions_short_and_long_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 | 2025-03-28T12:21:06 | 125 | 16 | false | 39ac85c150806230473c70ad72c31f6232fe3f41 |
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. | 9,368 | 9,436 | [
"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 |
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 | 49 | 15 | 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. | 8,771 | 8,771 | [
"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 |
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 | 7,657 | 14 | false | 68ba7694e23014788dcc8ab5afe613824f45a05c | 🧠 Awesome ChatGPT Prompts [CSV dataset]
This is a Dataset Repository of Awesome ChatGPT Prompts
View All Prompts on GitHub
License
CC-0
| 11,513 | 139,852 | [
"task_categories:question-answering",
"license:cc0-1.0",
"size_categories:n<1K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"ChatGPT"
] | 2022-12-13T23:47:45 | null | null |
66212f29fb07c3e05ad0432e | HuggingFaceFW/fineweb | HuggingFaceFW | {"license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "FineWeb", "size_categories": ["n>1T"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/*/*"}]}, {"config_name": "sample-10BT", "data_files": [{"split": "train", "path": "sample/10BT/*"}]}, {"config_name": "sample-100BT", "data_files": [{"split": "train", "path": "sample/100BT/*"}]}, {"config_name": "sample-350BT", "data_files": [{"split": "train", "path": "sample/350BT/*"}]}, {"config_name": "CC-MAIN-2024-51", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-51/*"}]}, {"config_name": "CC-MAIN-2024-46", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-46/*"}]}, {"config_name": "CC-MAIN-2024-42", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-42/*"}]}, {"config_name": "CC-MAIN-2024-38", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-38/*"}]}, {"config_name": "CC-MAIN-2024-33", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-33/*"}]}, {"config_name": "CC-MAIN-2024-30", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-30/*"}]}, {"config_name": "CC-MAIN-2024-26", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-26/*"}]}, {"config_name": "CC-MAIN-2024-22", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-22/*"}]}, {"config_name": "CC-MAIN-2024-18", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-18/*"}]}, {"config_name": "CC-MAIN-2024-10", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-10/*"}]}, {"config_name": "CC-MAIN-2023-50", "data_files": [{"split": "train", "path": "data/CC-MAIN-2023-50/*"}]}, {"config_name": "CC-MAIN-2023-40", "data_files": [{"split": "train", "path": "data/CC-MAIN-2023-40/*"}]}, {"config_name": "CC-MAIN-2023-23", "data_files": [{"split": "train", "path": "data/CC-MAIN-2023-23/*"}]}, {"config_name": "CC-MAIN-2023-14", "data_files": [{"split": "train", "path": "data/CC-MAIN-2023-14/*"}]}, {"config_name": "CC-MAIN-2023-06", "data_files": [{"split": "train", "path": "data/CC-MAIN-2023-06/*"}]}, {"config_name": "CC-MAIN-2022-49", "data_files": [{"split": "train", "path": "data/CC-MAIN-2022-49/*"}]}, {"config_name": "CC-MAIN-2022-40", "data_files": [{"split": "train", "path": "data/CC-MAIN-2022-40/*"}]}, {"config_name": "CC-MAIN-2022-33", "data_files": [{"split": "train", "path": "data/CC-MAIN-2022-33/*"}]}, {"config_name": "CC-MAIN-2022-27", "data_files": [{"split": "train", "path": "data/CC-MAIN-2022-27/*"}]}, {"config_name": "CC-MAIN-2022-21", "data_files": [{"split": "train", "path": "data/CC-MAIN-2022-21/*"}]}, {"config_name": "CC-MAIN-2022-05", "data_files": [{"split": "train", "path": "data/CC-MAIN-2022-05/*"}]}, {"config_name": "CC-MAIN-2021-49", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-49/*"}]}, {"config_name": "CC-MAIN-2021-43", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-43/*"}]}, {"config_name": "CC-MAIN-2021-39", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-39/*"}]}, {"config_name": "CC-MAIN-2021-31", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-31/*"}]}, {"config_name": "CC-MAIN-2021-25", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-25/*"}]}, {"config_name": "CC-MAIN-2021-21", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-21/*"}]}, {"config_name": "CC-MAIN-2021-17", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-17/*"}]}, {"config_name": "CC-MAIN-2021-10", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-10/*"}]}, {"config_name": "CC-MAIN-2021-04", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-04/*"}]}, {"config_name": "CC-MAIN-2020-50", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-50/*"}]}, {"config_name": "CC-MAIN-2020-45", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-45/*"}]}, {"config_name": "CC-MAIN-2020-40", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-40/*"}]}, {"config_name": "CC-MAIN-2020-34", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-34/*"}]}, {"config_name": "CC-MAIN-2020-29", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-29/*"}]}, {"config_name": "CC-MAIN-2020-24", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-24/*"}]}, {"config_name": "CC-MAIN-2020-16", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-16/*"}]}, {"config_name": "CC-MAIN-2020-10", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-10/*"}]}, {"config_name": "CC-MAIN-2020-05", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-05/*"}]}, {"config_name": "CC-MAIN-2019-51", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-51/*"}]}, {"config_name": "CC-MAIN-2019-47", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-47/*"}]}, {"config_name": "CC-MAIN-2019-43", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-43/*"}]}, {"config_name": "CC-MAIN-2019-39", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-39/*"}]}, {"config_name": "CC-MAIN-2019-35", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-35/*"}]}, {"config_name": "CC-MAIN-2019-30", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-30/*"}]}, {"config_name": "CC-MAIN-2019-26", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-26/*"}]}, {"config_name": "CC-MAIN-2019-22", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-22/*"}]}, {"config_name": "CC-MAIN-2019-18", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-18/*"}]}, {"config_name": "CC-MAIN-2019-13", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-13/*"}]}, {"config_name": "CC-MAIN-2019-09", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-09/*"}]}, {"config_name": "CC-MAIN-2019-04", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-04/*"}]}, {"config_name": "CC-MAIN-2018-51", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-51/*"}]}, {"config_name": "CC-MAIN-2018-47", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-47/*"}]}, {"config_name": "CC-MAIN-2018-43", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-43/*"}]}, {"config_name": "CC-MAIN-2018-39", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-39/*"}]}, {"config_name": "CC-MAIN-2018-34", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-34/*"}]}, {"config_name": "CC-MAIN-2018-30", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-30/*"}]}, {"config_name": "CC-MAIN-2018-26", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-26/*"}]}, {"config_name": "CC-MAIN-2018-22", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-22/*"}]}, {"config_name": "CC-MAIN-2018-17", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-17/*"}]}, {"config_name": "CC-MAIN-2018-13", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-13/*"}]}, {"config_name": "CC-MAIN-2018-09", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-09/*"}]}, {"config_name": "CC-MAIN-2018-05", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-05/*"}]}, {"config_name": "CC-MAIN-2017-51", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-51/*"}]}, {"config_name": "CC-MAIN-2017-47", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-47/*"}]}, {"config_name": "CC-MAIN-2017-43", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-43/*"}]}, {"config_name": "CC-MAIN-2017-39", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-39/*"}]}, {"config_name": "CC-MAIN-2017-34", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-34/*"}]}, {"config_name": "CC-MAIN-2017-30", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-30/*"}]}, {"config_name": "CC-MAIN-2017-26", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-26/*"}]}, {"config_name": "CC-MAIN-2017-22", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-22/*"}]}, {"config_name": "CC-MAIN-2017-17", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-17/*"}]}, {"config_name": "CC-MAIN-2017-13", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-13/*"}]}, {"config_name": "CC-MAIN-2017-09", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-09/*"}]}, {"config_name": "CC-MAIN-2017-04", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-04/*"}]}, {"config_name": "CC-MAIN-2016-50", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-50/*"}]}, {"config_name": "CC-MAIN-2016-44", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-44/*"}]}, {"config_name": "CC-MAIN-2016-40", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-40/*"}]}, {"config_name": "CC-MAIN-2016-36", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-36/*"}]}, {"config_name": "CC-MAIN-2016-30", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-30/*"}]}, {"config_name": "CC-MAIN-2016-26", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-26/*"}]}, {"config_name": "CC-MAIN-2016-22", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-22/*"}]}, {"config_name": "CC-MAIN-2016-18", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-18/*"}]}, {"config_name": "CC-MAIN-2016-07", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-07/*"}]}, {"config_name": "CC-MAIN-2015-48", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-48/*"}]}, {"config_name": "CC-MAIN-2015-40", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-40/*"}]}, {"config_name": "CC-MAIN-2015-35", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-35/*"}]}, {"config_name": "CC-MAIN-2015-32", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-32/*"}]}, {"config_name": "CC-MAIN-2015-27", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-27/*"}]}, {"config_name": "CC-MAIN-2015-22", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-22/*"}]}, {"config_name": "CC-MAIN-2015-18", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-18/*"}]}, {"config_name": "CC-MAIN-2015-14", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-14/*"}]}, {"config_name": "CC-MAIN-2015-11", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-11/*"}]}, {"config_name": "CC-MAIN-2015-06", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-06/*"}]}, {"config_name": "CC-MAIN-2014-52", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-52/*"}]}, {"config_name": "CC-MAIN-2014-49", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-49/*"}]}, {"config_name": "CC-MAIN-2014-42", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-42/*"}]}, {"config_name": "CC-MAIN-2014-41", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-41/*"}]}, {"config_name": "CC-MAIN-2014-35", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-35/*"}]}, {"config_name": "CC-MAIN-2014-23", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-23/*"}]}, {"config_name": "CC-MAIN-2014-15", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-15/*"}]}, {"config_name": "CC-MAIN-2014-10", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-10/*"}]}, {"config_name": "CC-MAIN-2013-48", "data_files": [{"split": "train", "path": "data/CC-MAIN-2013-48/*"}]}, {"config_name": "CC-MAIN-2013-20", "data_files": [{"split": "train", "path": "data/CC-MAIN-2013-20/*"}]}]} | false | null | 2025-01-31T14:10:44 | 2,077 | 14 | false | 0f039043b23fe1d4eed300b504aa4b4a68f1c7ba |
🍷 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. | 210,441 | 2,341,112 | [
"task_categories:text-generation",
"language:en",
"license:odc-by",
"size_categories:10B<n<100B",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2306.01116",
"arxiv:2109.07445",
"arxiv:2406.17557",
"doi:10.57967/hf/2493",
"region:us"
] | 2024-04-18T14:33:13 | null | null |
67ce7e23fee7f7ce990104eb | X-ART/LeX-10K | X-ART | {"license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-to-image"], "tags": ["text-rendering", "art"]} | false | null | 2025-03-31T08:19:17 | 14 | 14 | false | 18a5cf77b06608dac50442903bf5b9ad46bd7059 |
🖼️ LeX-10K: High-Quality Dataset for Text Rendering
LeX-10K is a curated dataset of 10,000 high-resolution, visually diverse 1024×1024 images tailored for text-to-image generation with a focus on aesthetics, text fidelity, and stylistic richness.
Project Page | Paper
🌟 Why LeX-10K?
We compare LeX-10K with two widely used datasets: AnyWord-3M and MARIO-10M.As shown below, LeX-10K significantly outperforms both in terms of aesthetic quality, text readability, and… See the full description on the dataset page: https://huggingface.co/datasets/X-ART/LeX-10K. | 826 | 826 | [
"task_categories:text-to-image",
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2503.21749",
"region:us",
"text-rendering",
"art"
] | 2025-03-10T05:52:35 | null | null |
621ffdd236468d709f181e16 | dair-ai/emotion | dair-ai | {"annotations_creators": ["machine-generated"], "language_creators": ["machine-generated"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification"], "paperswithcode_id": "emotion", "pretty_name": "Emotion", "tags": ["emotion-classification"], "dataset_info": [{"config_name": "split", "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": 1287193, "dataset_size": 2173401}, {"config_name": "unsplit", "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": 45444017, "num_examples": 416809}], "download_size": 26888538, "dataset_size": 45444017}], "configs": [{"config_name": "split", "data_files": [{"split": "train", "path": "split/train-*"}, {"split": "validation", "path": "split/validation-*"}, {"split": "test", "path": "split/test-*"}], "default": true}, {"config_name": "unsplit", "data_files": [{"split": "train", "path": "unsplit/train-*"}]}], "train-eval-index": [{"config": "default", "task": "text-classification", "task_id": "multi_class_classification", "splits": {"train_split": "train", "eval_split": "test"}, "col_mapping": {"text": "text", "label": "target"}, "metrics": [{"type": "accuracy", "name": "Accuracy"}, {"type": "f1", "name": "F1 macro", "args": {"average": "macro"}}, {"type": "f1", "name": "F1 micro", "args": {"average": "micro"}}, {"type": "f1", "name": "F1 weighted", "args": {"average": "weighted"}}, {"type": "precision", "name": "Precision macro", "args": {"average": "macro"}}, {"type": "precision", "name": "Precision micro", "args": {"average": "micro"}}, {"type": "precision", "name": "Precision weighted", "args": {"average": "weighted"}}, {"type": "recall", "name": "Recall macro", "args": {"average": "macro"}}, {"type": "recall", "name": "Recall micro", "args": {"average": "micro"}}, {"type": "recall", "name": "Recall weighted", "args": {"average": "weighted"}}]}]} | false | null | 2024-08-08T06:10:47 | 348 | 12 | false | cab853a1dbdf4c42c2b3ef2173804746df8825fe |
Dataset Card for "emotion"
Dataset Summary
Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed
Dataset Structure
Data Instances
An example looks as follows.
{
"text": "im feeling quite sad… See the full description on the dataset page: https://huggingface.co/datasets/dair-ai/emotion. | 17,496 | 355,415 | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:other",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"emotion-classification"
] | 2022-03-02T23:29:22 | emotion | null |
639244f571c51c43091df168 | Anthropic/hh-rlhf | Anthropic | {"license": "mit", "tags": ["human-feedback"]} | false | null | 2023-05-26T18:47:34 | 1,311 | 12 | 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… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/hh-rlhf. | 13,010 | 1,562,241 | [
"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 |
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": 276479149, "num_examples": 2700}], "download_size": 266651469, "dataset_size": 276479149}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]} | false | null | 2025-02-15T00:05:49 | 291 | 12 | false | 1a9f4713d5a6bc9b7988db7c42e1dccdf41d1f43 |
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 2,700 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of… See the full description on the dataset page: https://huggingface.co/datasets/cais/hle. | 6,203 | 15,490 | [
"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 |
67b20fc10861cec33b3afb8a | Conard/fortune-telling | Conard | {"license": "mit"} | false | null | 2025-02-17T05:13:43 | 109 | 12 | false | 6261fe0d35a75997972bbfcd9828020e340303fb | null | 5,914 | 7,065 | [
"license:mit",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-16T16:18:09 | 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", "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", "num_bytes": 131489.4633955277, "num_examples": 265}, {"name": "validation", "num_bytes": 14461.813193990856, "num_examples": 29}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 51655, "dataset_size": 148150.45202811505}, {"config_name": "college_biology", "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": 71450.87822247542, "num_examples": 144}, {"name": "validation", "num_bytes": 7978.931417374265, "num_examples": 16}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 43017, "dataset_size": 81628.98507844617}, {"config_name": "college_chemistry", "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": 3989.4657086871325, "num_examples": 8}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 26781, "dataset_size": 55807.30657955822}, {"config_name": "college_computer_science", "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": 41132, "dataset_size": 57303.3562203159}, {"config_name": "college_mathematics", "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": 26779, "dataset_size": 57303.3562203159}, {"config_name": "college_medicine", "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": 85840.29119783506, "num_examples": 173}, {"name": "validation", "num_bytes": 10971.030698889615, "num_examples": 22}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 56303, "dataset_size": 99010.49733532117}, {"config_name": "college_physics", "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": 50611.0387409201, "num_examples": 102}, {"name": "validation", "num_bytes": 5485.515349444808, "num_examples": 11}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 29539, "dataset_size": 58295.7295289614}, {"config_name": "computer_security", "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": 30150, "dataset_size": 57303.3562203159}, {"config_name": "conceptual_physics", "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": 116603.86376584532, "num_examples": 235}, {"name": "validation", "num_bytes": 12965.76355323318, "num_examples": 26}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 34968, "dataset_size": 131768.802757675}, {"config_name": "econometrics", "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": 56565.27859279305, "num_examples": 114}, {"name": "validation", "num_bytes": 5984.198563030699, "num_examples": 12}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 36040, "dataset_size": 64748.652594420244}, {"config_name": "electrical_engineering", "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": 71947.06487679818, "num_examples": 145}, {"name": "validation", "num_bytes": 7978.931417374265, "num_examples": 16}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 26746, "dataset_size": 82125.17173276893}, {"config_name": "elementary_mathematics", "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": 187558.555333998, "num_examples": 378}, {"name": "validation", "num_bytes": 20446.011757021555, "num_examples": 41}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 54987, "dataset_size": 210203.74252961605}, {"config_name": "formal_logic", "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": 62519.518444666, "num_examples": 126}, {"name": "validation", "num_bytes": 6981.5649902024825, "num_examples": 14}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 32884, "dataset_size": 71700.25887346498}, {"config_name": "global_facts", "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": 4986.8321358589155, "num_examples": 10}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 19258, "dataset_size": 56804.67300673001}, {"config_name": "high_school_biology", "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": 153817.86284005127, "num_examples": 310}, {"name": "validation", "num_bytes": 15957.86283474853, "num_examples": 32}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 78216, "dataset_size": 171974.90111339628}, {"config_name": "high_school_chemistry", "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": 100725.89082751745, "num_examples": 203}, {"name": "validation", "num_bytes": 10971.030698889615, "num_examples": 22}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 45799, "dataset_size": 113896.09696500355}, {"config_name": "high_school_computer_science", "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": 4488.148922273024, "num_examples": 9}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 39072, "dataset_size": 56305.989793144116}, {"config_name": "high_school_european_history", "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": 81870.79796325309, "num_examples": 165}, {"name": "validation", "num_bytes": 8976.297844546049, "num_examples": 18}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 196270, "dataset_size": 93046.27124639563}, {"config_name": "high_school_geography", "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": 98244.95755590372, "num_examples": 198}, {"name": "validation", "num_bytes": 10971.030698889615, "num_examples": 22}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 38255, "dataset_size": 111415.16369338983}, {"config_name": "high_school_government_and_politics", "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": 95764.02428428999, "num_examples": 193}, {"name": "validation", "num_bytes": 10472.347485303722, "num_examples": 21}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 52963, "dataset_size": 108435.5472081902}, {"config_name": "high_school_macroeconomics", "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": 193512.79518587096, "num_examples": 390}, {"name": "validation", "num_bytes": 21443.378184193338, "num_examples": 43}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 68758, "dataset_size": 217155.34880866078}, {"config_name": "high_school_mathematics", "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": 133970.39666714144, "num_examples": 270}, {"name": "validation", "num_bytes": 14461.813193990856, "num_examples": 29}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 45210, "dataset_size": 150631.38529972878}, {"config_name": "high_school_microeconomics", "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": 118092.42372881356, "num_examples": 238}, {"name": "validation", "num_bytes": 12965.76355323318, "num_examples": 26}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 49885, "dataset_size": 133257.36272064323}, {"config_name": "high_school_physics", "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": 74924.18480273466, "num_examples": 151}, {"name": "validation", "num_bytes": 8477.614630960157, "num_examples": 17}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 45483, "dataset_size": 85600.9748722913}, {"config_name": "high_school_psychology", "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": 270421.7266058966, "num_examples": 545}, {"name": "validation", "num_bytes": 29920.992815153495, "num_examples": 60}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 113158, "dataset_size": 302541.8948596466}, {"config_name": "high_school_statistics", "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": 107176.31733371314, "num_examples": 216}, {"name": "validation", "num_bytes": 11469.713912475507, "num_examples": 23}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 74924, "dataset_size": 120845.20668478514}, {"config_name": "high_school_us_history", "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": 101222.0774818402, "num_examples": 204}, {"name": "validation", "num_bytes": 10971.030698889615, "num_examples": 22}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 200043, "dataset_size": 114392.2836193263}, {"config_name": "high_school_world_history", "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": 117596.23707449081, "num_examples": 237}, {"name": "validation", "num_bytes": 12965.76355323318, "num_examples": 26}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 250302, "dataset_size": 132761.17606632048}, {"config_name": "human_aging", "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": 110649.62391397236, "num_examples": 223}, {"name": "validation", "num_bytes": 11469.713912475507, "num_examples": 23}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 41196, "dataset_size": 124318.51326504436}, {"config_name": "human_sexuality", "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": 65000.451716279735, "num_examples": 131}, {"name": "validation", "num_bytes": 5984.198563030699, "num_examples": 12}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 32533, "dataset_size": 73183.82571790692}, {"config_name": "international_law", "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": 60038.58517305227, "num_examples": 121}, {"name": "validation", "num_bytes": 6482.88177661659, "num_examples": 13}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 41592, "dataset_size": 68720.64238826535}, {"config_name": "jurisprudence", "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": 53588.15866685657, "num_examples": 108}, {"name": "validation", "num_bytes": 5485.515349444808, "num_examples": 11}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 33578, "dataset_size": 61272.84945489787}, {"config_name": "logical_fallacies", "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": 80878.4246546076, "num_examples": 163}, {"name": "validation", "num_bytes": 8976.297844546049, "num_examples": 18}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 33669, "dataset_size": 92053.89793775014}, {"config_name": "machine_learning", "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": 55572.90528414756, "num_examples": 112}, {"name": "validation", "num_bytes": 5485.515349444808, "num_examples": 11}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 31121, "dataset_size": 63257.596072188855}, {"config_name": "management", "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": 51107.225395242844, "num_examples": 103}, {"name": "validation", "num_bytes": 5485.515349444808, "num_examples": 11}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 22828, "dataset_size": 58791.91618328414}, {"config_name": "marketing", "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": 116107.67711152257, "num_examples": 234}, {"name": "validation", "num_bytes": 12467.08033964729, "num_examples": 25}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 49747, "dataset_size": 130773.93288976635}, {"config_name": "medical_genetics", "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": 25775, "dataset_size": 57303.3562203159}, {"config_name": "miscellaneous", "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": 388514.15033471014, "num_examples": 783}, {"name": "validation", "num_bytes": 42886.756368386676, "num_examples": 86}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 115097, "dataset_size": 433600.08214169333}, {"config_name": "moral_disputes", "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": 171680.58239567012, "num_examples": 346}, {"name": "validation", "num_bytes": 18949.96211626388, "num_examples": 38}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 76043, "dataset_size": 192829.71995053047}, {"config_name": "moral_scenarios", "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": 444087.05561885773, "num_examples": 895}, {"name": "validation", "num_bytes": 49868.32135858916, "num_examples": 100}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 109869, "dataset_size": 496154.5524160434}, {"config_name": "nutrition", "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": 151833.1162227603, "num_examples": 306}, {"name": "validation", "num_bytes": 16456.54604833442, "num_examples": 33}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 69050, "dataset_size": 170488.8377096912}, {"config_name": "philosophy", "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": 154314.04949437402, "num_examples": 311}, {"name": "validation", "num_bytes": 16955.229261920314, "num_examples": 34}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 61912, "dataset_size": 173468.45419489083}, {"config_name": "prehistory", "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": 160764.47600056973, "num_examples": 324}, {"name": "validation", "num_bytes": 17453.912475506204, "num_examples": 35}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 68826, "dataset_size": 180417.5639146724}, {"config_name": "professional_accounting", "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": 139924.6365190144, "num_examples": 282}, {"name": "validation", "num_bytes": 15459.179621162639, "num_examples": 31}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 87297, "dataset_size": 157582.99157877354}, {"config_name": "professional_law", "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": 761150.3277310925, "num_examples": 1534}, {"name": "validation", "num_bytes": 84776.14630960157, "num_examples": 170}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 1167828, "dataset_size": 848125.6494792906}, {"config_name": "professional_medicine", "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": 134962.7699757869, "num_examples": 272}, {"name": "validation", "num_bytes": 15459.179621162639, "num_examples": 31}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 153242, "dataset_size": 152621.12503554605}, {"config_name": "professional_psychology", "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": 303666.2324455206, "num_examples": 612}, {"name": "validation", "num_bytes": 34409.14173742652, "num_examples": 69}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 159357, "dataset_size": 340274.5496215436}, {"config_name": "public_relations", "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": 54580.53197550207, "num_examples": 110}, {"name": "validation", "num_bytes": 5984.198563030699, "num_examples": 12}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 31500, "dataset_size": 62763.90597712925}, {"config_name": "security_studies", "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": 121565.73030907278, "num_examples": 245}, {"name": "validation", "num_bytes": 13464.446766819072, "num_examples": 27}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 140258, "dataset_size": 137229.35251448833}, {"config_name": "sociology", "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": 99733.51751887196, "num_examples": 201}, {"name": "validation", "num_bytes": 10971.030698889615, "num_examples": 22}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 56480, "dataset_size": 112903.72365635807}, {"config_name": "us_foreign_policy", "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": 29027, "dataset_size": 57303.3562203159}, {"config_name": "virology", "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": 82366.98461757584, "num_examples": 166}, {"name": "validation", "num_bytes": 8976.297844546049, "num_examples": 18}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 38229, "dataset_size": 93542.45790071838}, {"config_name": "world_religions", "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": 84847.91788918957, "num_examples": 171}, {"name": "validation", "num_bytes": 9474.98105813194, "num_examples": 19}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 27165, "dataset_size": 96522.07438591801}], "configs": [{"config_name": "abstract_algebra", "data_files": [{"split": "test", "path": "abstract_algebra/test-*"}, {"split": "validation", "path": "abstract_algebra/validation-*"}, {"split": "dev", "path": "abstract_algebra/dev-*"}]}, {"config_name": "all", "data_files": [{"split": "test", "path": "all/test-*"}, {"split": "validation", "path": "all/validation-*"}, {"split": "dev", "path": "all/dev-*"}, {"split": "auxiliary_train", "path": "all/auxiliary_train-*"}]}, {"config_name": "anatomy", "data_files": [{"split": "test", "path": "anatomy/test-*"}, {"split": "validation", "path": "anatomy/validation-*"}, {"split": "dev", "path": "anatomy/dev-*"}]}, {"config_name": "astronomy", "data_files": [{"split": "test", "path": "astronomy/test-*"}, {"split": "validation", "path": "astronomy/validation-*"}, {"split": "dev", "path": "astronomy/dev-*"}]}, {"config_name": "auxiliary_train", "data_files": [{"split": "train", "path": "auxiliary_train/train-*"}]}, {"config_name": "business_ethics", "data_files": [{"split": "test", "path": "business_ethics/test-*"}, {"split": "validation", "path": "business_ethics/validation-*"}, {"split": "dev", "path": "business_ethics/dev-*"}]}, {"config_name": "clinical_knowledge", "data_files": [{"split": "test", "path": "clinical_knowledge/test-*"}, {"split": "validation", "path": "clinical_knowledge/validation-*"}, {"split": "dev", "path": "clinical_knowledge/dev-*"}]}, {"config_name": "college_biology", "data_files": [{"split": "test", "path": "college_biology/test-*"}, {"split": "validation", "path": "college_biology/validation-*"}, {"split": "dev", "path": "college_biology/dev-*"}]}, {"config_name": "college_chemistry", "data_files": [{"split": "test", "path": "college_chemistry/test-*"}, {"split": "validation", "path": "college_chemistry/validation-*"}, {"split": "dev", "path": "college_chemistry/dev-*"}]}, {"config_name": "college_computer_science", "data_files": [{"split": "test", "path": "college_computer_science/test-*"}, {"split": "validation", "path": "college_computer_science/validation-*"}, {"split": "dev", "path": "college_computer_science/dev-*"}]}, {"config_name": "college_mathematics", "data_files": [{"split": "test", "path": "college_mathematics/test-*"}, {"split": "validation", "path": "college_mathematics/validation-*"}, {"split": "dev", "path": "college_mathematics/dev-*"}]}, {"config_name": "college_medicine", "data_files": [{"split": "test", "path": "college_medicine/test-*"}, {"split": "validation", "path": "college_medicine/validation-*"}, {"split": "dev", "path": "college_medicine/dev-*"}]}, {"config_name": "college_physics", "data_files": [{"split": "test", "path": "college_physics/test-*"}, {"split": "validation", "path": "college_physics/validation-*"}, {"split": "dev", "path": "college_physics/dev-*"}]}, {"config_name": "computer_security", "data_files": [{"split": "test", "path": "computer_security/test-*"}, {"split": "validation", "path": "computer_security/validation-*"}, {"split": "dev", "path": "computer_security/dev-*"}]}, {"config_name": "conceptual_physics", "data_files": [{"split": "test", "path": "conceptual_physics/test-*"}, {"split": "validation", "path": "conceptual_physics/validation-*"}, {"split": "dev", "path": "conceptual_physics/dev-*"}]}, {"config_name": "econometrics", "data_files": [{"split": "test", "path": "econometrics/test-*"}, {"split": "validation", "path": "econometrics/validation-*"}, {"split": "dev", "path": "econometrics/dev-*"}]}, {"config_name": "electrical_engineering", "data_files": [{"split": "test", "path": "electrical_engineering/test-*"}, {"split": "validation", "path": "electrical_engineering/validation-*"}, {"split": "dev", "path": "electrical_engineering/dev-*"}]}, {"config_name": "elementary_mathematics", "data_files": [{"split": "test", "path": "elementary_mathematics/test-*"}, {"split": "validation", "path": "elementary_mathematics/validation-*"}, {"split": "dev", "path": "elementary_mathematics/dev-*"}]}, {"config_name": "formal_logic", "data_files": [{"split": "test", "path": "formal_logic/test-*"}, {"split": "validation", "path": "formal_logic/validation-*"}, {"split": "dev", "path": "formal_logic/dev-*"}]}, {"config_name": "global_facts", "data_files": [{"split": "test", "path": "global_facts/test-*"}, {"split": "validation", "path": "global_facts/validation-*"}, {"split": "dev", "path": "global_facts/dev-*"}]}, {"config_name": "high_school_biology", "data_files": [{"split": "test", "path": "high_school_biology/test-*"}, {"split": "validation", "path": "high_school_biology/validation-*"}, {"split": "dev", "path": "high_school_biology/dev-*"}]}, {"config_name": "high_school_chemistry", "data_files": [{"split": "test", "path": "high_school_chemistry/test-*"}, {"split": "validation", "path": "high_school_chemistry/validation-*"}, {"split": "dev", "path": "high_school_chemistry/dev-*"}]}, {"config_name": "high_school_computer_science", "data_files": [{"split": "test", "path": "high_school_computer_science/test-*"}, {"split": "validation", "path": "high_school_computer_science/validation-*"}, {"split": "dev", "path": "high_school_computer_science/dev-*"}]}, {"config_name": "high_school_european_history", "data_files": [{"split": "test", "path": "high_school_european_history/test-*"}, {"split": "validation", "path": "high_school_european_history/validation-*"}, {"split": "dev", "path": "high_school_european_history/dev-*"}]}, {"config_name": "high_school_geography", "data_files": [{"split": "test", "path": "high_school_geography/test-*"}, {"split": "validation", "path": "high_school_geography/validation-*"}, {"split": "dev", "path": "high_school_geography/dev-*"}]}, {"config_name": "high_school_government_and_politics", "data_files": [{"split": "test", "path": "high_school_government_and_politics/test-*"}, {"split": "validation", "path": "high_school_government_and_politics/validation-*"}, {"split": "dev", "path": "high_school_government_and_politics/dev-*"}]}, {"config_name": "high_school_macroeconomics", "data_files": [{"split": "test", "path": "high_school_macroeconomics/test-*"}, {"split": "validation", "path": "high_school_macroeconomics/validation-*"}, {"split": "dev", "path": "high_school_macroeconomics/dev-*"}]}, {"config_name": "high_school_mathematics", "data_files": [{"split": "test", "path": "high_school_mathematics/test-*"}, {"split": "validation", "path": "high_school_mathematics/validation-*"}, {"split": "dev", "path": "high_school_mathematics/dev-*"}]}, {"config_name": "high_school_microeconomics", "data_files": [{"split": "test", "path": "high_school_microeconomics/test-*"}, {"split": "validation", "path": "high_school_microeconomics/validation-*"}, {"split": "dev", "path": "high_school_microeconomics/dev-*"}]}, {"config_name": "high_school_physics", "data_files": [{"split": "test", "path": "high_school_physics/test-*"}, {"split": "validation", "path": "high_school_physics/validation-*"}, {"split": "dev", "path": "high_school_physics/dev-*"}]}, {"config_name": "high_school_psychology", "data_files": [{"split": "test", "path": "high_school_psychology/test-*"}, {"split": "validation", "path": "high_school_psychology/validation-*"}, {"split": "dev", "path": "high_school_psychology/dev-*"}]}, {"config_name": "high_school_statistics", "data_files": [{"split": "test", "path": "high_school_statistics/test-*"}, {"split": "validation", "path": "high_school_statistics/validation-*"}, {"split": "dev", "path": "high_school_statistics/dev-*"}]}, {"config_name": "high_school_us_history", "data_files": [{"split": "test", "path": "high_school_us_history/test-*"}, {"split": "validation", "path": "high_school_us_history/validation-*"}, {"split": "dev", "path": "high_school_us_history/dev-*"}]}, {"config_name": "high_school_world_history", "data_files": [{"split": "test", "path": "high_school_world_history/test-*"}, {"split": "validation", "path": "high_school_world_history/validation-*"}, {"split": "dev", "path": "high_school_world_history/dev-*"}]}, {"config_name": "human_aging", "data_files": [{"split": "test", "path": "human_aging/test-*"}, {"split": "validation", "path": "human_aging/validation-*"}, {"split": "dev", "path": "human_aging/dev-*"}]}, {"config_name": "human_sexuality", "data_files": [{"split": "test", "path": "human_sexuality/test-*"}, {"split": "validation", "path": "human_sexuality/validation-*"}, {"split": "dev", "path": "human_sexuality/dev-*"}]}, {"config_name": "international_law", "data_files": [{"split": "test", "path": "international_law/test-*"}, {"split": "validation", "path": "international_law/validation-*"}, {"split": "dev", "path": "international_law/dev-*"}]}, {"config_name": "jurisprudence", "data_files": [{"split": "test", "path": "jurisprudence/test-*"}, {"split": "validation", "path": "jurisprudence/validation-*"}, {"split": "dev", "path": "jurisprudence/dev-*"}]}, {"config_name": "logical_fallacies", "data_files": [{"split": "test", "path": "logical_fallacies/test-*"}, {"split": "validation", "path": "logical_fallacies/validation-*"}, {"split": "dev", "path": "logical_fallacies/dev-*"}]}, {"config_name": "machine_learning", "data_files": [{"split": "test", "path": "machine_learning/test-*"}, {"split": "validation", "path": "machine_learning/validation-*"}, {"split": "dev", "path": "machine_learning/dev-*"}]}, {"config_name": "management", "data_files": [{"split": "test", "path": "management/test-*"}, {"split": "validation", "path": "management/validation-*"}, {"split": "dev", "path": "management/dev-*"}]}, {"config_name": "marketing", "data_files": [{"split": "test", "path": "marketing/test-*"}, {"split": "validation", "path": "marketing/validation-*"}, {"split": "dev", "path": "marketing/dev-*"}]}, {"config_name": "medical_genetics", "data_files": [{"split": "test", "path": "medical_genetics/test-*"}, {"split": "validation", "path": "medical_genetics/validation-*"}, {"split": "dev", "path": "medical_genetics/dev-*"}]}, {"config_name": "miscellaneous", "data_files": [{"split": "test", "path": "miscellaneous/test-*"}, {"split": "validation", "path": "miscellaneous/validation-*"}, {"split": "dev", "path": "miscellaneous/dev-*"}]}, {"config_name": "moral_disputes", "data_files": [{"split": "test", "path": "moral_disputes/test-*"}, {"split": "validation", "path": "moral_disputes/validation-*"}, {"split": "dev", "path": "moral_disputes/dev-*"}]}, {"config_name": "moral_scenarios", "data_files": [{"split": "test", "path": "moral_scenarios/test-*"}, {"split": "validation", "path": "moral_scenarios/validation-*"}, {"split": "dev", "path": "moral_scenarios/dev-*"}]}, {"config_name": "nutrition", "data_files": [{"split": "test", "path": "nutrition/test-*"}, {"split": "validation", "path": "nutrition/validation-*"}, {"split": "dev", "path": "nutrition/dev-*"}]}, {"config_name": "philosophy", "data_files": [{"split": "test", "path": "philosophy/test-*"}, {"split": "validation", "path": "philosophy/validation-*"}, {"split": "dev", "path": "philosophy/dev-*"}]}, {"config_name": "prehistory", "data_files": [{"split": "test", "path": "prehistory/test-*"}, {"split": "validation", "path": "prehistory/validation-*"}, {"split": "dev", "path": "prehistory/dev-*"}]}, {"config_name": "professional_accounting", "data_files": [{"split": "test", "path": "professional_accounting/test-*"}, {"split": "validation", "path": "professional_accounting/validation-*"}, {"split": "dev", "path": "professional_accounting/dev-*"}]}, {"config_name": "professional_law", "data_files": [{"split": "test", "path": "professional_law/test-*"}, {"split": "validation", "path": "professional_law/validation-*"}, {"split": "dev", "path": "professional_law/dev-*"}]}, {"config_name": "professional_medicine", "data_files": [{"split": "test", "path": "professional_medicine/test-*"}, {"split": "validation", "path": "professional_medicine/validation-*"}, {"split": "dev", "path": "professional_medicine/dev-*"}]}, {"config_name": "professional_psychology", "data_files": [{"split": "test", "path": "professional_psychology/test-*"}, {"split": "validation", "path": "professional_psychology/validation-*"}, {"split": "dev", "path": "professional_psychology/dev-*"}]}, {"config_name": "public_relations", "data_files": [{"split": "test", "path": "public_relations/test-*"}, {"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 | null | 2024-03-08T20:36:26 | 441 | 11 | 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… See the full description on the dataset page: https://huggingface.co/datasets/cais/mmlu. | 145,172 | 37,203,844 | [
"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 |
65af32411edab235a1f38b0b | omar07ibrahim/Alpaca_Stanford_Azerbaijan | omar07ibrahim | null | false | null | 2024-01-23T03:28:27 | 12 | 11 | false | a088761652ed34235281b46bcdb49d36fd0a3bdb | null | 16 | 146 | [
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-01-23T03:28:01 | null | null |
65afa00e637e10fba969eb56 | omar07ibrahim/alpaca-cleaned_AZERBAIJANI | omar07ibrahim | null | false | null | 2024-01-23T11:18:42 | 13 | 11 | false | ad9e82bceb5c7a2d438dfcf04132854fc0328781 | null | 26 | 241 | [
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-01-23T11:16:30 | null | null |
65b4e0dccbea1825a691a012 | omar07ibrahim/testlimOcrCA | omar07ibrahim | null | false | null | 2024-01-27T10:57:04 | 11 | 11 | false | b1f404a6dcaff40d4d14320dd44a212c79a13c94 | null | 17 | 85 | [
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-01-27T10:54:20 | null | null |
65d713022db271ebd4139f5f | omar07ibrahim/azcon | omar07ibrahim | null | false | null | 2024-02-22T09:26:09 | 11 | 11 | false | f13a01b9ff1ac643e343c80c7ef356ab10e42f7a | null | 13 | 93 | [
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-02-22T09:25:22 | 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 | 53 | 11 | 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. | 113 | 113 | [
"license:other",
"arxiv:2503.15478",
"region:us"
] | 2025-02-11T21:36:02 | null | null |
67e2d9efa823152c92e695d4 | nyuuzyou/archiveofourown | nyuuzyou | {"annotations_creators": ["found"], "language": ["ar", "bg", "ca", "cs", "da", "de", "el", "en", "es", "et", "fa", "fi", "fr", "he", "hi", "hr", "hu", "id", "it", "ja", "ko", "lt", "lv", "ms", "nl", "no", "pl", "pt", "ro", "ru", "sk", "sl", "sr", "sv", "th", "tr", "uk", "vi", "zh"], "language_bcp47": ["pt-BR", "zh-HK", "zh-TW"], "license": ["cc0-1.0"], "multilinguality": ["multilingual"], "pretty_name": "Archive of Our Own (AO3)", "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["text-generation", "text-classification"], "task_ids": ["language-modeling", "topic-classification"], "configs": [{"config_name": "train", "data_files": [{"split": "train", "path": "*.jsonl.zst"}], "default": true}]} | false | null | 2025-03-25T17:07:57 | 13 | 11 | false | 4f0f0eb835ab627a78d02d2973ca0263400720df |
Dataset Card for Archive of Our Own (AO3)
Dataset Summary
This dataset contains approximately 12.6 million publicly available works from Archive of Our Own (AO3), a fan-created, fan-run, non-profit archive for transformative fanworks. The dataset was created by processing works with IDs from 1 to 63,200,000 that are publicly accessible. Each entry contains the full text of the work along with comprehensive metadata including title, author, fandom, relationships… See the full description on the dataset page: https://huggingface.co/datasets/nyuuzyou/archiveofourown. | 568 | 568 | [
"task_categories:text-generation",
"task_categories:text-classification",
"task_ids:language-modeling",
"task_ids:topic-classification",
"annotations_creators:found",
"multilinguality:multilingual",
"source_datasets:original",
"language:ar",
"language:bg",
"language:ca",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:fa",
"language:fi",
"language:fr",
"language:he",
"language:hi",
"language:hr",
"language:hu",
"language:id",
"language:it",
"language:ja",
"language:ko",
"language:lt",
"language:lv",
"language:ms",
"language:nl",
"language:no",
"language:pl",
"language:pt",
"language:ro",
"language:ru",
"language:sk",
"language:sl",
"language:sr",
"language:sv",
"language:th",
"language:tr",
"language:uk",
"language:vi",
"language:zh",
"license:cc0-1.0",
"size_categories:10M<n<100M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"region:us"
] | 2025-03-25T16:29:35 | null | null |
67e74b725fb029dd96363693 | inclusionAI/AReaL-boba-Data | inclusionAI | {"license": "apache-2.0"} | false | null | 2025-03-29T01:26:55 | 11 | 11 | false | 1799c00be3f1216ab55a5cae3562d654dbfd7d82 | null | 225 | 225 | [
"license:apache-2.0",
"region:us"
] | 2025-03-29T01:22:58 | null | 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 | 675 | 10 | 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. | 23,866 | 620,035 | [
"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 |
6798a4f11290e49f6e4b7ceb | LWHYC/PASTA-Gen-30K | LWHYC | {"license": "mit"} | false | null | 2025-02-18T05:47:25 | 12 | 9 | false | af31b74e90824a4ccd06c8f583664763942a91e2 |
Workflow of PASTA Model Development and Training Pipeline. a, Overview of organs and lesion
types involved in PASTA training. b, Examples of lesions generated by PASTA-Gen from healthy organs. c, Lesion generation process pipeline of PASTA-Gen. d, Two-stage training of PASTA using the PASTA-Gen-30K
dataset.
Model, Paper
Overview
PASTA-Gen-30K, a large-scale synthetic dataset of 30,000 CT volumes with precise lesion masks and structured textual reports from 15 lesion types (10… See the full description on the dataset page: https://huggingface.co/datasets/LWHYC/PASTA-Gen-30K. | 7,189 | 11,635 | [
"license:mit",
"arxiv:2502.06171",
"region:us"
] | 2025-01-28T09:35:45 | 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 | 53 | 9 | false | 9f6440001c15da8e7c7516fdbb3d2ce49de711de |
This dataset actually only contains ~17k unique prompts and was duplicated by ~100x by accident.
| 3,477 | 3,477 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"math"
] | 2025-03-17T09:44:12 | null | null |
67e46d6a65b73a7980e27dc3 | sychonix/movies-dataset | sychonix | {"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "genre", "dtype": "string"}, {"name": "rating", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 127, "num_examples": 4}], "download_size": 1527, "dataset_size": 127}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-03-26T21:11:09 | 9 | 9 | false | 167e24b4fdce9aff2984628bf5466858f3ca3e55 | null | 100 | 100 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-26T21:11:06 | null | null |
650a9248d26103b6eee3ea7b | lmsys/lmsys-chat-1m | lmsys | {"size_categories": ["1M<n<10M"], "task_categories": ["conversational"], "extra_gated_prompt": "You agree to the [LMSYS-Chat-1M Dataset License Agreement](https://huggingface.co/datasets/lmsys/lmsys-chat-1m#lmsys-chat-1m-dataset-license-agreement).", "extra_gated_fields": {"Name": "text", "Email": "text", "Affiliation": "text", "Country": "text"}, "extra_gated_button_content": "I agree to the terms and conditions of the LMSYS-Chat-1M Dataset License Agreement.", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "conversation_id", "dtype": "string"}, {"name": "model", "dtype": "string"}, {"name": "conversation", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "turn", "dtype": "int64"}, {"name": "language", "dtype": "string"}, {"name": "openai_moderation", "list": [{"name": "categories", "struct": [{"name": "harassment", "dtype": "bool"}, {"name": "harassment/threatening", "dtype": "bool"}, {"name": "hate", "dtype": "bool"}, {"name": "hate/threatening", "dtype": "bool"}, {"name": "self-harm", "dtype": "bool"}, {"name": "self-harm/instructions", "dtype": "bool"}, {"name": "self-harm/intent", "dtype": "bool"}, {"name": "sexual", "dtype": "bool"}, {"name": "sexual/minors", "dtype": "bool"}, {"name": "violence", "dtype": "bool"}, {"name": "violence/graphic", "dtype": "bool"}]}, {"name": "category_scores", "struct": [{"name": "harassment", "dtype": "float64"}, {"name": "harassment/threatening", "dtype": "float64"}, {"name": "hate", "dtype": "float64"}, {"name": "hate/threatening", "dtype": "float64"}, {"name": "self-harm", "dtype": "float64"}, {"name": "self-harm/instructions", "dtype": "float64"}, {"name": "self-harm/intent", "dtype": "float64"}, {"name": "sexual", "dtype": "float64"}, {"name": "sexual/minors", "dtype": "float64"}, {"name": "violence", "dtype": "float64"}, {"name": "violence/graphic", "dtype": "float64"}]}, {"name": "flagged", "dtype": "bool"}]}, {"name": "redacted", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 2626438904, "num_examples": 1000000}], "download_size": 1488850250, "dataset_size": 2626438904}} | false | null | 2024-07-27T09:28:42 | 653 | 8 | false | 200748d9d3cddcc9d782887541057aca0b18c5da |
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
This dataset contains one million real-world conversations with 25 state-of-the-art LLMs.
It is collected from 210K unique IP addresses in the wild on the Vicuna demo and Chatbot Arena website from April to August 2023.
Each sample includes a conversation ID, model name, conversation text in OpenAI API JSON format, detected language tag, and OpenAI moderation API tag.
User consent is obtained through the "Terms of… See the full description on the dataset page: https://huggingface.co/datasets/lmsys/lmsys-chat-1m. | 4,041 | 222,232 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2309.11998",
"region:us"
] | 2023-09-20T06:33:44 | 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 | 676 | 8 | 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. | 27,600 | 147,275 | [
"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 |
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 | 534 | 8 | 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. | 48,657 | 82,203 | [
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-10T13:41:48 | null | null |
67ae5cb70100bb7fb11fdb31 | getomni-ai/ocr-benchmark | getomni-ai | {"license": "mit", "size_categories": ["1K<n<10K"]} | false | null | 2025-02-21T06:34:31 | 39 | 8 | false | 4ed0d95271ca00107726230f7a0944ed9e90d897 |
OmniAI OCR Benchmark
A comprehensive benchmark that compares OCR and data extraction capabilities of different multimodal LLMs such as gpt-4o and gemini-2.0, evaluating both text and JSON extraction accuracy.
Benchmark Results (Feb 2025) | Source Code
| 1,720 | 2,578 | [
"license:mit",
"size_categories:1K<n<10K",
"format:imagefolder",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 2025-02-13T20:57:27 | null | null |
67d129c98475d8ca6b49b0e2 | Jinsaryko/Elise | Jinsaryko | {"license": "mit", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 22050}}}, {"name": "text", "dtype": "string"}, {"name": "speaker_name", "dtype": "string"}, {"name": "utterance_pitch_mean", "dtype": "float32"}, {"name": "utterance_pitch_std", "dtype": "float32"}, {"name": "snr", "dtype": "float64"}, {"name": "c50", "dtype": "float64"}, {"name": "speaking_rate", "dtype": "string"}, {"name": "phonemes", "dtype": "string"}, {"name": "stoi", "dtype": "float64"}, {"name": "si-sdr", "dtype": "float64"}, {"name": "pesq", "dtype": "float64"}, {"name": "noise", "dtype": "string"}, {"name": "reverberation", "dtype": "string"}, {"name": "speech_monotony", "dtype": "string"}, {"name": "sdr_noise", "dtype": "string"}, {"name": "pesq_speech_quality", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 404729782.0316433, "num_examples": 1195}], "download_size": 328587897, "dataset_size": 404729782.0316433}} | false | null | 2025-03-12T07:54:34 | 8 | 8 | false | 6706e80541aa1813973d87201c32160babac024a | null | 139 | 139 | [
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-12T06:29:29 | null | null |
67e156bc3aa73bdc22f5b858 | benjaminogbonna/nigerian_common_voice_dataset | benjaminogbonna | {"license": "apache-2.0", "task_categories": ["automatic-speech-recognition", "text-to-speech"], "pretty_name": "Nigerian Common Voice Dataset", "annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en", "ha", "ig", "yo"], "multilinguality": ["multilingual"], "extra_gated_prompt": "By clicking on \u201cAccess repository\u201d below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "size_categories": ["10K<n<100K"], "dataset_info": [{"config_name": "default", "features": [{"name": "audio", "dtype": "audio"}, {"name": "client_id", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "accent", "dtype": "string"}, {"name": "locale", "dtype": "string"}], "splits": [{"name": "english_train", "num_bytes": 76891, "num_examples": 3}, {"name": "english_validation", "num_bytes": 76388, "num_examples": 3}, {"name": "english_test", "num_bytes": 44707, "num_examples": 3}, {"name": "hausa_train", "num_bytes": 87721, "num_examples": 3}, {"name": "hausa_validation", "num_bytes": 81663, "num_examples": 3}, {"name": "hausa_test", "num_bytes": 86685, "num_examples": 3}, {"name": "igbo_train", "num_bytes": 77798, "num_examples": 3}, {"name": "igbo_validation", "num_bytes": 109802, "num_examples": 3}, {"name": "igbo_test", "num_bytes": 103504, "num_examples": 3}, {"name": "yoruba_train", "num_bytes": 111252, "num_examples": 3}, {"name": "yoruba_validation", "num_bytes": 125347, "num_examples": 3}, {"name": "yoruba_test", "num_bytes": 116250, "num_examples": 3}], "download_size": 1127146, "dataset_size": 1098008}, {"config_name": "english", "features": [{"name": "audio", "dtype": "audio"}, {"name": "client_id", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "accent", "dtype": "string"}, {"name": "locale", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 102291684.678, "num_examples": 2721}, {"name": "validation", "num_bytes": 12091603, "num_examples": 340}, {"name": "test", "num_bytes": 11585499, "num_examples": 341}], "download_size": 121504884, "dataset_size": 125968786.678}, {"config_name": "hausa", "features": [{"name": "audio", "dtype": "audio"}, {"name": "client_id", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "accent", "dtype": "string"}, {"name": "locale", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 189263575.55, "num_examples": 7206}, {"name": "validation", "num_bytes": 23256496, "num_examples": 901}, {"name": "test", "num_bytes": 24050751, "num_examples": 901}], "download_size": 234586970, "dataset_size": 236570822.55}, {"config_name": "igbo", "features": [{"name": "audio", "dtype": "audio"}, {"name": "client_id", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "accent", "dtype": "string"}, {"name": "locale", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 147708753.853, "num_examples": 4571}, {"name": "validation", "num_bytes": 19026693, "num_examples": 571}, {"name": "test", "num_bytes": 19092378, "num_examples": 572}], "download_size": 185986664, "dataset_size": 185827824.853}, {"config_name": "yoruba", "features": [{"name": "audio", "dtype": "audio"}, {"name": "client_id", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "accent", "dtype": "string"}, {"name": "locale", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 124429039.456, "num_examples": 3336}, {"name": "validation", "num_bytes": 15302013, "num_examples": 417}, {"name": "test", "num_bytes": 15182108, "num_examples": 418}], "download_size": 147489914, "dataset_size": 154913160.456}], "configs": [{"config_name": "english", "data_files": [{"split": "train", "path": "english/train-*"}, {"split": "validation", "path": "english/validation-*"}, {"split": "test", "path": "english/test-*"}]}, {"config_name": "hausa", "data_files": [{"split": "train", "path": "hausa/train-*"}, {"split": "validation", "path": "hausa/validation-*"}, {"split": "test", "path": "hausa/test-*"}]}, {"config_name": "igbo", "data_files": [{"split": "train", "path": "igbo/train-*"}, {"split": "validation", "path": "igbo/validation-*"}, {"split": "test", "path": "igbo/test-*"}]}, {"config_name": "yoruba", "data_files": [{"split": "train", "path": "yoruba/train-*"}, {"split": "validation", "path": "yoruba/validation-*"}, {"split": "test", "path": "yoruba/test-*"}]}]} | false | null | 2025-03-30T16:49:15 | 8 | 8 | false | 97c7f160c0d563339d2f32d55945abc406696cf2 |
Dataset Card for Nigerian Common Voice Dataset
Dataset Summary
The Nigerian Common Voice Dataset is a comprehensive dataset consisting of 158 hours of audio recordings and corresponding transcription (sentence).
This dataset includes metadata like accent, locale that can help improve the accuracy of speech recognition engines. This dataset is specifically curated to address the gap in speech and language
datasets for African accents, making it a valuable resource for… See the full description on the dataset page: https://huggingface.co/datasets/benjaminogbonna/nigerian_common_voice_dataset. | 121 | 121 | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"language:en",
"language:ha",
"language:ig",
"language:yo",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-24T12:57:32 | null | null |
67e426ebce38fa56b7c71f53 | Rapidata/Ideogram-V2_t2i_human_preference | Rapidata | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "image1", "dtype": "image"}, {"name": "image2", "dtype": "image"}, {"name": "model1", "dtype": "string"}, {"name": "model2", "dtype": "string"}, {"name": "weighted_results_image1_preference", "dtype": "float32"}, {"name": "weighted_results_image2_preference", "dtype": "float32"}, {"name": "detailed_results_preference", "dtype": "string"}, {"name": "weighted_results_image1_coherence", "dtype": "float32"}, {"name": "weighted_results_image2_coherence", "dtype": "float32"}, {"name": "detailed_results_coherence", "dtype": "string"}, {"name": "weighted_results_image1_alignment", "dtype": "float32"}, {"name": "weighted_results_image2_alignment", "dtype": "float32"}, {"name": "detailed_results_alignment", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 29035433897, "num_examples": 13000}], "download_size": 6908537024, "dataset_size": 29035433897}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "cdla-permissive-2.0", "task_categories": ["text-to-image", "image-to-text", "image-classification", "reinforcement-learning"], "language": ["en"], "tags": ["Human", "Preference", "Coherence", "Alignment", "country", "language", "flux", "midjourney", "dalle3", "stabeldiffusion", "alignment", "flux1.1", "flux1", "imagen3", "aurora", "lumina", "recraft", "recraft v2", "ideogram", "frames"], "size_categories": ["100K<n<1M"], "pretty_name": "Ideogram V2 vs. Recraft V2 / Lumina-15-2-25 / Frames-23-1-25 / Aurora / imagen-3 / Flux-1.1-pro / Flux-1-pro / Dalle-3 / Midjourney-5.2 / Stabel-Diffusion-3 - Human Preference Dataset"} | false | null | 2025-03-27T13:41:51 | 8 | 8 | false | 9d9bb0aa365e9fbc77e865731ec96655a10e0990 |
Rapidata Ideogram-V2 Preference
This T2I dataset contains over 195k human responses from over 42k individual annotators, collected in just ~1 Day using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating Ideogram-V2 across three categories: preference, coherence, and alignment.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please consider liking it.… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Ideogram-V2_t2i_human_preference. | 322 | 322 | [
"task_categories:text-to-image",
"task_categories:image-to-text",
"task_categories:image-classification",
"task_categories:reinforcement-learning",
"language:en",
"license:cdla-permissive-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"Human",
"Preference",
"Coherence",
"Alignment",
"country",
"language",
"flux",
"midjourney",
"dalle3",
"stabeldiffusion",
"alignment",
"flux1.1",
"flux1",
"imagen3",
"aurora",
"lumina",
"recraft",
"recraft v2",
"ideogram",
"frames"
] | 2025-03-26T16:10:19 | null | null |
67e90aae9f3eff13b7405dfb | KwaiVGI/MultiCamVideo-Dataset | KwaiVGI | {"license": "apache-2.0"} | false | null | 2025-04-02T14:01:28 | 8 | 8 | false | c386c3dffcd41437cef573ef720d135467cbe4e0 | Github
Project Page
Paper
📷 MultiCamVideo Dataset
1. Dataset Introduction
TL;DR: The MultiCamVideo Dataset, introduced in ReCamMaster, is a multi-camera synchronized video dataset rendered using Unreal Engine 5. It includes synchronized multi-camera videos and its corresponding camera trajectories. The MultiCamVideo Dataset can be valuable in fields such as camera-controlled video generation, synchronized video production, and 3D/4D reconstruction.
The… See the full description on the dataset page: https://huggingface.co/datasets/KwaiVGI/MultiCamVideo-Dataset. | 155 | 155 | [
"license:apache-2.0",
"arxiv:2503.11647",
"region:us"
] | 2025-03-30T09:11:10 | null | null |
64035e3d723a03e62696f152 | biglam/european_art | biglam | {"dataset_info": [{"config_name": "coco", "features": [{"name": "image", "dtype": "image"}, {"name": "source", "dtype": "string"}, {"name": "width", "dtype": "int16"}, {"name": "height", "dtype": "int16"}, {"name": "dept", "dtype": "int8"}, {"name": "segmented", "dtype": "int8"}, {"name": "objects", "list": [{"name": "category_id", "dtype": {"class_label": {"names": {"0": "zebra", "1": "tree", "2": "nude", "3": "crucifixion", "4": "scroll", "5": "head", "6": "swan", "7": "shield", "8": "lily", "9": "mouse", "10": "knight", "11": "dragon", "12": "horn", "13": "dog", "14": "palm", "15": "tiara", "16": "helmet", "17": "sheep", "18": "deer", "19": "person", "20": "sword", "21": "rooster", "22": "bear", "23": "halo", "24": "lion", "25": "monkey", "26": "prayer", "27": "crown of thorns", "28": "elephant", "29": "zucchetto", "30": "unicorn", "31": "holy shroud", "32": "cat", "33": "apple", "34": "banana", "35": "chalice", "36": "bird", "37": "eagle", "38": "pegasus", "39": "crown", "40": "camauro", "41": "saturno", "42": "arrow", "43": "dove", "44": "centaur", "45": "horse", "46": "hands", "47": "skull", "48": "orange", "49": "monk", "50": "trumpet", "51": "key of heaven", "52": "fish", "53": "cow", "54": "angel", "55": "devil", "56": "book", "57": "stole", "58": "butterfly", "59": "serpent", "60": "judith", "61": "mitre", "62": "banner", "63": "donkey", "64": "shepherd", "65": "boat", "66": "god the father", "67": "crozier", "68": "jug", "69": "lance"}}}}, {"name": "image_id", "dtype": "string"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "segmentation", "list": {"list": "float32"}}, {"name": "iscrowd", "dtype": "bool"}]}, {"name": "image_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8285204, "num_examples": 15156}], "download_size": 18160510195, "dataset_size": 8285204}, {"config_name": "default", "features": [{"name": "image", "dtype": "image"}, {"name": "file_id", "dtype": "string"}, {"name": "annotations", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18197594657, "num_examples": 15154}], "download_size": 18151946901, "dataset_size": 18197594657}, {"config_name": "raw", "features": [{"name": "image", "dtype": "image"}, {"name": "source", "dtype": "string"}, {"name": "width", "dtype": "int16"}, {"name": "height", "dtype": "int16"}, {"name": "dept", "dtype": "int8"}, {"name": "segmented", "dtype": "int8"}, {"name": "objects", "list": [{"name": "name", "dtype": {"class_label": {"names": {"0": "zebra", "1": "tree", "2": "nude", "3": "crucifixion", "4": "scroll", "5": "head", "6": "swan", "7": "shield", "8": "lily", "9": "mouse", "10": "knight", "11": "dragon", "12": "horn", "13": "dog", "14": "palm", "15": "tiara", "16": "helmet", "17": "sheep", "18": "deer", "19": "person", "20": "sword", "21": "rooster", "22": "bear", "23": "halo", "24": "lion", "25": "monkey", "26": "prayer", "27": "crown of thorns", "28": "elephant", "29": "zucchetto", "30": "unicorn", "31": "holy shroud", "32": "cat", "33": "apple", "34": "banana", "35": "chalice", "36": "bird", "37": "eagle", "38": "pegasus", "39": "crown", "40": "camauro", "41": "saturno", "42": "arrow", "43": "dove", "44": "centaur", "45": "horse", "46": "hands", "47": "skull", "48": "orange", "49": "monk", "50": "trumpet", "51": "key of heaven", "52": "fish", "53": "cow", "54": "angel", "55": "devil", "56": "book", "57": "stole", "58": "butterfly", "59": "serpent", "60": "judith", "61": "mitre", "62": "banner", "63": "donkey", "64": "shepherd", "65": "boat", "66": "god the father", "67": "crozier", "68": "jug", "69": "lance"}}}}, {"name": "pose", "dtype": {"class_label": {"names": {"0": "stand", "1": "sit", "2": "partial", "3": "Unspecified", "4": "squats", "5": "lie", "6": "bend", "7": "fall", "8": "walk", "9": "push", "10": "pray", "11": "undefined", "12": "kneel", "13": "unrecognize", "14": "unknown", "15": "other", "16": "ride"}}}}, {"name": "diffult", "dtype": "int32"}, {"name": "xmin", "dtype": "float64"}, {"name": "ymin", "dtype": "float64"}, {"name": "xmax", "dtype": "float64"}, {"name": "ymax", "dtype": "float64"}]}], "splits": [{"name": "train", "num_bytes": 9046918, "num_examples": 15156}], "download_size": 18160510195, "dataset_size": 9046918}], "license": "cc-by-nc-2.0", "task_categories": ["object-detection", "image-classification"], "tags": ["lam", "art", "historical"], "pretty_name": "DEArt: Dataset of European Art", "size_categories": ["10K<n<100K"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-03-31T18:04:12 | 13 | 7 | false | f00afe1c164f7d1d9819e3b55b1fe693e4cfa91c |
Dataset Card for DEArt: Dataset of European Art
Dataset Summary
DEArt is an object detection and pose classification dataset meant to be a reference for paintings between the XIIth and the XVIIIth centuries. It contains more than 15000 images, about 80% non-iconic, aligned with manual annotations for the bounding boxes identifying all instances of 69 classes as well as 12 possible poses for boxes identifying human-like objects. Of these, more than 50 classes are cultural… See the full description on the dataset page: https://huggingface.co/datasets/biglam/european_art. | 65 | 564 | [
"task_categories:object-detection",
"task_categories:image-classification",
"license:cc-by-nc-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2211.01226",
"region:us",
"lam",
"art",
"historical"
] | 2023-03-04T15:05:33 | null | null |
665eaefe5baf7febc7207877 | OOPPEENN/Galgame_Dataset | OOPPEENN | {"license": "gpl-3.0"} | false | null | 2025-04-02T11:39:53 | 120 | 7 | false | 2d943cb4cf7ddc281257ceee2ec9bf4502376ba4 |
0x0 使用协议：
必须遵守GNU General Public License v3.0内的所有协议！附加：禁止商用，本数据集以及使用本数据集训练出来的任何模型都不得用于任何商业行为，如要用于商业用途，请找数据列表内的所有厂商授权（笑），因违反开源协议而出现的任何问题都与本人无关！
训练出来的模型必须开源，是否在README内引用本数据集由训练者自主决定，不做强制要求。
0x1 数据说明：
解压密码：9ll9Ke4iq0jqyq3gS1Wy。
标注说明：标注，说话人和对应的音频是直接读游戏引擎的脚本生成的，应该是100%准确率，全部存放在index.json里面，如果还有错误可以在开issues反馈（有些遗漏的控制符可能没洗干净）。
务必根据index.json里面的键值对找音频，不在index内的音频请直接丢弃，说话人为？？？的请直接丢弃。
数据语言：日语（100%）
数据时长：8823h 22m 07s
角色总数：25387人（未合并）
音频格式：ogg（6031257个），opus（172948个），wav（34753个）… See the full description on the dataset page: https://huggingface.co/datasets/OOPPEENN/Galgame_Dataset. | 3,126 | 21,960 | [
"license:gpl-3.0",
"region:us"
] | 2024-06-04T06:06:54 | null | null |
67b78333f663232795e6cb29 | SynthLabsAI/Big-Math-RL-Verified | SynthLabsAI | {"dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "domain", "sequence": "string"}, {"name": "llama8b_solve_rate", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 76969060, "num_examples": 251122}], "download_size": 32238760, "dataset_size": 76969060}, "task_categories": ["question-answering", "text-generation"], "language": ["en"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "size_categories": ["100K<n<1M"], "tags": ["mathematics", "math", "reinforcement-learning", "RL", "reasoning", "verifiable", "open-ended-questions", "closed-form-answers"], "license": "apache-2.0"} | false | null | 2025-03-25T15:33:48 | 163 | 7 | false | c75d2f117cddfecb6bd08756e61e508e59732b21 |
Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models
Big-Math is the largest open-source dataset of high-quality mathematical problems, curated specifically for reinforcement learning (RL) training in language models. With over 250,000 rigorously filtered and verified problems, Big-Math bridges the gap between quality and quantity, establishing a robust foundation for advancing reasoning in LLMs.
Request Early Access to Private… See the full description on the dataset page: https://huggingface.co/datasets/SynthLabsAI/Big-Math-RL-Verified. | 7,474 | 9,357 | [
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2502.17387",
"region:us",
"mathematics",
"math",
"reinforcement-learning",
"RL",
"reasoning",
"verifiable",
"open-ended-questions",
"closed-form-answers"
] | 2025-02-20T19:32:03 | null | null |
End of preview. Expand
in Data Studio
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
- 4,524
Data Sourcing report
powered
by
Spawning.aiNo elements in this dataset have been identified as either opted-out, or opted-in, by their creator.