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67d3479522a51de18affff22 | nvidia/Llama-Nemotron-Post-Training-Dataset-v1 | nvidia | {"license": "cc-by-4.0", "configs": [{"config_name": "SFT", "data_files": [{"split": "code", "path": "SFT/code/*.jsonl"}, {"split": "math", "path": "SFT/math/*.jsonl"}, {"split": "science", "path": "SFT/science/*.jsonl"}, {"split": "chat", "path": "SFT/chat/*.jsonl"}, {"split": "safety", "path": "SFT/safety/*.jsonl"}], "default": true}, {"config_name": "RL", "data_files": [{"split": "instruction_following", "path": "RL/instruction_following/*.jsonl"}]}]} | false | null | 2025-03-18T15:56:14 | 320 | 51 | 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. | 12,271 | 12,280 | [
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] | 2025-03-13T21:01:09 | 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 | 40 | 40 | 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. | 438 | 438 | [
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] | 2025-03-31T07:35:23 | 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 | 168 | 38 | 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. | 9,690 | 9,804 | [
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] | 2025-02-27T20:40:05 | 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 | 610 | 37 | 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. | 23,038 | 51,769 | [
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"arxiv:2412.18925",
"region:us",
"medical",
"biology"
] | 2024-12-28T03:29:08 | null | null |
67edf568d1631250f17528af | open-thoughts/OpenThoughts2-1M | open-thoughts | {"dataset_info": {"features": [{"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "question", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 18986223337, "num_examples": 1143205}], "download_size": 8328411205, "dataset_size": 18986223337}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["synthetic", "curator"], "license": "apache-2.0"} | false | null | 2025-04-03T20:31:18 | 35 | 35 | false | 6e9f7d7b5e072a76e65bd204fe6c59d32404c69c |
OpenThoughts2-1M
Open synthetic reasoning dataset with 1M high-quality examples covering math, science, code, and puzzles!
OpenThoughts2-1M builds upon our previous OpenThoughts-114k dataset, augmenting it with existing datasets like OpenR1, as well as additional math and code reasoning data.
This dataset was used to train OpenThinker2-7B and OpenThinker2-32B.
See our blog post for more details.
OpenThinker2 Models
Our OpenThinker2 models trained on this… See the full description on the dataset page: https://huggingface.co/datasets/open-thoughts/OpenThoughts2-1M. | 1,222 | 1,222 | [
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"synthetic",
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] | 2025-04-03T02:41:44 | 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 | 105 | 28 | 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. | 9,495 | 9,495 | [
"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 |
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 | 25 | 25 | 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. | 474 | 474 | [
"task_categories:automatic-speech-recognition",
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"library:dask",
"library:mlcroissant",
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] | 2025-03-30T09:12:51 | 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 | 69 | 24 | 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. | 90 | 90 | [
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] | 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 | 250 | 20 | 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. | 3,677 | 10,451 | [
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"region:us",
"AI",
"LLM",
"Economic Impacts",
"Anthropic"
] | 2025-02-06T00:39:24 | 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 | 7,666 | 18 | false | 68ba7694e23014788dcc8ab5afe613824f45a05c | 🧠 Awesome ChatGPT Prompts [CSV dataset]
This is a Dataset Repository of Awesome ChatGPT Prompts
View All Prompts on GitHub
License
CC-0
| 10,945 | 140,884 | [
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"ChatGPT"
] | 2022-12-13T23:47:45 | 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 | 482 | 17 | 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. | 10,531 | 17,689 | [
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"region:us"
] | 2025-01-30T23:29:32 | null | null |
67e9a644ea97f3c65c463bfb | LLM360/MegaMath | LLM360 | {"license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "tags": ["math", "code", "pre-training", "synthesis"], "size_categories": ["1B<n<10B"]} | false | null | 2025-04-04T14:04:23 | 17 | 17 | false | b2dbbfdb0bb40f8f5893b4057c6f5f430ae34d35 |
MegaMath: Pushing the Limits of Open Math Copora
Megamath is part of TxT360, curated by LLM360 Team.
We introduce MegaMath, an open math pretraining dataset curated from diverse, math-focused sources, with over 300B tokens.
MegaMath is curated via the following three efforts:
Revisiting web data:
We re-extracted mathematical documents from Common Crawl with math-oriented HTML optimizations, fasttext-based filtering and deduplication, all for acquiring higher-quality data on the… See the full description on the dataset page: https://huggingface.co/datasets/LLM360/MegaMath. | 97 | 97 | [
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] | 2025-03-30T20:15:00 | 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 | 44 | 15 | 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. | 3,435 | 3,458 | [
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] | 2025-03-24T10:32:37 | 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 | 676 | 12 | 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. | 344,422 | 4,333,110 | [
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] | 2022-04-12T10:22:10 | gsm8k | null |
67e74b725fb029dd96363693 | inclusionAI/AReaL-boba-Data | inclusionAI | {"license": "apache-2.0"} | false | null | 2025-03-29T01:26:55 | 12 | 12 | false | 1799c00be3f1216ab55a5cae3562d654dbfd7d82 | null | 274 | 274 | [
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67e9eb451ba052dc29fd90f8 | camel-ai/loong | camel-ai | {"authors": ["camel-ai"], "description": "A comprehensive collection of 3,551 high-quality problems across 8 diverse domains, curated for Project Loong. Each problem includes a detailed executable rationale and solution, designed for training and evaluating reasoning models.", "language": ["en"], "license": "mit", "pretty_name": "camel-ai/loong", "tags": ["reasoning", "problem-solving", "project-loong", "multi-domain", "mathematics", "physics", "finance", "optimization"], "task_categories": ["question-answering"], "configs": [{"config_name": "default", "data_files": [{"split": "advanced_physics", "path": "data/advanced_physics-*"}, {"split": "graph_discrete_math", "path": "data/graph_discrete_math-*"}, {"split": "computational_biology", "path": "data/computational_biology-*"}, {"split": "logic", "path": "data/logic-*"}, {"split": "security_and_safety", "path": "data/security_and_safety-*"}, {"split": "advanced_math", "path": "data/advanced_math-*"}, {"split": "finance", "path": "data/finance-*"}, {"split": "mathematical_programming", "path": "data/mathematical_programming-*"}]}], "dataset_info": {"features": [{"name": "source_type", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "rationale", "dtype": "string"}, {"name": "final_answer", "dtype": "string"}, {"name": "meta_data", "dtype": "string"}], "splits": [{"name": "advanced_physics", "num_bytes": 829991.8175161927, "num_examples": 434}, {"name": "graph_discrete_math", "num_bytes": 342323.8141368629, "num_examples": 179}, {"name": "computational_biology", "num_bytes": 581376.7569698676, "num_examples": 304}, {"name": "logic", "num_bytes": 210366.58969304422, "num_examples": 110}, {"name": "security_and_safety", "num_bytes": 996372.6657279639, "num_examples": 521}, {"name": "advanced_math", "num_bytes": 3088564.021402422, "num_examples": 1615}, {"name": "finance", "num_bytes": 611975.5336524922, "num_examples": 320}, {"name": "mathematical_programming", "num_bytes": 130044.80090115461, "num_examples": 68}], "download_size": 2447494, "dataset_size": 6791016.000000001}} | false | null | 2025-04-01T22:04:20 | 12 | 12 | false | 74cadda690866a8b60cbc31e801fba5f173cb392 |
Additional Information
Project Loong Seed Dataset
This dataset is part of Project Loong, a collaborative effort to explore whether reasoning-capable models can bootstrap themselves from small, high-quality seed datasets by generating synthetic data and verifying LLM agent responses.
Dataset Description
This comprehensive collection contains 3,551 human-vetted problems across 8 diverse domains:
🧮 Advanced Math: 1,615 questions
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65af32411edab235a1f38b0b | omar07ibrahim/Alpaca_Stanford_Azerbaijan | omar07ibrahim | null | false | null | 2024-01-23T03:28:27 | 12 | 11 | false | a088761652ed34235281b46bcdb49d36fd0a3bdb | null | 17 | 150 | [
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65b4e0dccbea1825a691a012 | omar07ibrahim/testlimOcrCA | omar07ibrahim | null | false | null | 2024-01-27T10:57:04 | 11 | 11 | false | b1f404a6dcaff40d4d14320dd44a212c79a13c94 | null | 25 | 93 | [
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65d713022db271ebd4139f5f | omar07ibrahim/azcon | omar07ibrahim | null | false | null | 2024-02-22T09:26:09 | 11 | 11 | false | f13a01b9ff1ac643e343c80c7ef356ab10e42f7a | null | 17 | 98 | [
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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 | 29 | 11 | 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.
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621ffdd236468d709f181e5e | cais/mmlu | cais | {"annotations_creators": ["no-annotation"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["multiple-choice-qa"], "paperswithcode_id": "mmlu", "pretty_name": "Measuring Massive Multitask Language Understanding", "language_bcp47": ["en-US"], "dataset_info": [{"config_name": "abstract_algebra", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 49618.6654322746, "num_examples": 100}, {"name": "validation", "num_bytes": 5485.515349444808, "num_examples": 11}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], 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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. | 134,703 | 37,218,591 | [
"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 |
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🍷 FineWeb
15 trillion tokens of the finest data the 🌐 web has to offer
What is it?
The 🍷 FineWeb dataset consists of more than 15T tokens of cleaned and deduplicated english web data from CommonCrawl. The data processing pipeline is optimized for LLM performance and ran on the 🏭 datatrove library, our large scale data processing library.
🍷 FineWeb was originally meant to be a fully open replication of 🦅 RefinedWeb, with a release of the full dataset under… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb. | 189,801 | 2,359,376 | [
"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 |
665eaefe5baf7febc7207877 | OOPPEENN/Galgame_Dataset | OOPPEENN | {"license": "gpl-3.0"} | false | null | 2025-04-05T13:55:37 | 124 | 10 | false | 063289e2dc66f39729e575d9a6c35ec959571419 |
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. | 3,691 | 22,881 | [
"license:gpl-3.0",
"region:us"
] | 2024-06-04T06:06:54 | null | null |
67c03fd6b9fe27a2ac49784d | open-r1/codeforces-cots | open-r1 | {"dataset_info": [{"config_name": "checker_interactor", "features": [{"name": "id", "dtype": "string"}, {"name": "aliases", "sequence": "string"}, {"name": "contest_id", "dtype": "string"}, {"name": "contest_name", "dtype": "string"}, {"name": "contest_type", "dtype": "string"}, {"name": "contest_start", "dtype": "int64"}, {"name": "contest_start_year", "dtype": "int64"}, {"name": "index", "dtype": "string"}, {"name": "time_limit", "dtype": "float64"}, {"name": "memory_limit", "dtype": "float64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "input_format", "dtype": "string"}, {"name": "output_format", "dtype": "string"}, {"name": "interaction_format", "dtype": "string"}, {"name": "note", "dtype": "string"}, {"name": "examples", "list": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "editorial", "dtype": "string"}, {"name": "prompt", 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"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 | 127 | 10 | 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. | 10,286 | 10,366 | [
"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 |
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 | 18 | 10 | 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. | 1,285 | 1,285 | [
"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 |
67e52d5dc04bd73d2f46a929 | MrDragonFox/Elise | MrDragonFox | {"license": "mit"} | false | null | 2025-03-27T11:22:24 | 25 | 10 | 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. | 1,410 | 1,410 | [
"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 |
639244f571c51c43091df168 | Anthropic/hh-rlhf | Anthropic | {"license": "mit", "tags": ["human-feedback"]} | false | null | 2023-05-26T18:47:34 | 1,312 | 9 | 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. | 13,018 | 1,563,546 | [
"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 |
67d97c4be2b27852325fd8e2 | nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim | nvidia | {"license": "cc-by-4.0"} | false | null | 2025-04-02T02:27:47 | 102 | 9 | 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. | 29,201 | 29,201 | [
"license:cc-by-4.0",
"region:us"
] | 2025-03-18T13:59:39 | 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": 284635618, "num_examples": 2500}], "download_size": 274582371, "dataset_size": 284635618}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]} | false | null | 2025-04-04T04:00:14 | 292 | 8 | false | 1e33bd2d1346480b397ad94845067c4a088a33d3 |
Humanity's Last Exam
🌐 Website | 📄 Paper | GitHub
Center for AI Safety & Scale AI
Humanity's Last Exam (HLE) is a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. Humanity's Last Exam consists of 2,500 questions across dozens of 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. | 7,073 | 17,174 | [
"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 |
67a52826e2c430620fe95ca3 | PatronusAI/BLUR | PatronusAI | {"license": "mit", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "blur", "size_categories": ["n<1K"], "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "validation.csv"}, {"split": "public_test", "path": "public_test.csv"}]}]} | false | null | 2025-03-26T02:43:58 | 10 | 8 | false | dda66f8e5fd56102644ef313a6532d4e4cfba514 |
Browsing Lost Unformed Recollections
The leaderboard can be found at https://huggingface.co/spaces/PatronusAI/BLUR-leaderboard. If you use or find this dataset helpful in your research, please do cite our paper:
Paper Link: arXiv
@misc{chwang2025blur,
title = {Browsing {Lost} {Unformed} {Recollections}: {A} {Benchmark} for {Tip}-of-the-{Tongue} {Search} and {Reasoning}},
shorttitle = {Browsing {Lost} {Unformed} {Recollections}},
url = {http://arxiv.org/abs/2503.19193}… See the full description on the dataset page: https://huggingface.co/datasets/PatronusAI/BLUR. | 137 | 172 | [
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:n<1K",
"format:csv",
"modality:audio",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2503.19193",
"region:us"
] | 2025-02-06T21:22:46 | 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 | 57 | 8 | 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. | 125 | 125 | [
"license:other",
"arxiv:2503.15478",
"region:us"
] | 2025-02-11T21:36:02 | null | null |
67b32145bac2756ce9a4a0fe | Congliu/Chinese-DeepSeek-R1-Distill-data-110k | Congliu | {"license": "apache-2.0", "language": ["zh"], "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "text2text-generation", "question-answering"]} | false | null | 2025-02-21T02:18:08 | 609 | 8 | 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. | 5,081 | 11,124 | [
"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 |
67e90aae9f3eff13b7405dfb | KwaiVGI/MultiCamVideo-Dataset | KwaiVGI | {"license": "apache-2.0"} | false | null | 2025-04-03T03:23:10 | 8 | 8 | false | 1603dfd560cc227f218c854fbee81e56a82993e0 | 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. | 278 | 278 | [
"license:apache-2.0",
"arxiv:2503.11647",
"region:us"
] | 2025-03-30T09:11:10 | null | null |
67ec47948647cfa17739af7a | nvidia/OpenCodeReasoning | nvidia | {"dataset_info": [{"config_name": "split_0", "features": [{"name": "id", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "dataset", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "solution", "dtype": "string"}], "splits": [{"name": "split_0", "num_bytes": 28108469190, "num_examples": 567850}]}, {"config_name": "split_1", "features": [{"name": "id", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "dataset", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "index", "dtype": "string"}], "splits": [{"name": "split_1", "num_bytes": 4722811278, "num_examples": 167405}]}], "configs": [{"config_name": "split_0", "data_files": [{"split": "split_0", "path": "split_0/train-*"}]}, {"config_name": "split_1", "data_files": [{"split": "split_1", "path": "split_1/train-*"}]}], "tags": ["synthetic"], "license": "cc-by-4.0", "size_categories": ["100K<n<1M"], "pretty_name": "OpenCodeReasoning"} | false | null | 2025-04-05T00:26:16 | 8 | 8 | false | 4c7dd4975d530d9808268f62444dba001cdc5f80 |
OpenCodeReasoning: Advancing Data Distillation for Competitive Coding
Data Overview
OpenCodeReasoning is the largest reasoning-based synthetic dataset to date for coding, comprises 735,255 samples in Python across 28,319 unique competitive programming
questions. OpenCodeReasoning is designed for supervised fine-tuning (SFT).
Technical Report - Discover the methodology and technical details behind OpenCodeReasoning.
Github Repo - Access the complete pipeline used to… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenCodeReasoning. | 23 | 23 | [
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"library:polars",
"arxiv:2504.01943",
"region:us",
"synthetic"
] | 2025-04-01T20:07:48 | 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 | 14 | 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. | 360 | 862 | [
"task_categories:object-detection",
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"library:datasets",
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"library:polars",
"arxiv:2211.01226",
"region:us",
"lam",
"art",
"historical"
] | 2023-03-04T15:05:33 | 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 | 7 | 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. | 23,360 | 622,174 | [
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"instruction-finetuning"
] | 2023-03-24T18:27:58 | null | null |
65abafba043d53781a266118 | arbml/CIDAR | arbml | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "index", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 6712623, "num_examples": 10000}], "download_size": 3553672, "dataset_size": 6712623}, "license": "apache-2.0", "task_categories": ["text-generation"], "tags": ["Instruction"], "language": ["ar"], "pretty_name": "CIDAR", "size_categories": ["1K<n<10K"]} | false | null | 2025-04-03T08:35:36 | 49 | 7 | false | a3a9da3ea61ee296476d91132f9d0df95a11622a |
Dataset Card for "CIDAR"
🌴CIDAR: Culturally Relevant Instruction Dataset For Arabic
[ Paper - GitHub ]
CIDAR contains 10,000 instructions and their output. The dataset was created by selecting around 9,109 samples from Alpagasus dataset then translating it to Arabic using ChatGPT. In addition, we append that with around 891 Arabic grammar instructions from the webiste Ask the teacher. All the 10,000 samples were reviewed by around 12 reviewers.
📚… See the full description on the dataset page: https://huggingface.co/datasets/arbml/CIDAR. | 576 | 4,181 | [
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"arxiv:2402.03177",
"region:us",
"Instruction"
] | 2024-01-20T11:34:18 | null | null |
6731b4fffb568a134d3f2dd6 | TIGER-Lab/OmniEdit-Filtered-1.2M | TIGER-Lab | {"language": ["en"], "license": "mit", "size_categories": ["1M<n<10M"], "pretty_name": "OmniEdit", "dataset_info": {"features": [{"name": "omni_edit_id", "dtype": "string"}, {"name": "task", "dtype": "string"}, {"name": "src_img", "dtype": "image"}, {"name": "edited_img", "dtype": "image"}, {"name": "edited_prompt_list", "sequence": "string"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "sc_score_1", "dtype": "int64"}, {"name": "sc_score_2", "dtype": "int64"}, {"name": "sc_reasoning", "dtype": "string"}, {"name": "pq_score", "dtype": "int64"}, {"name": "pq_reasoning", "dtype": "string"}, {"name": "o_score", "dtype": "float64"}], "splits": [{"name": "dev", "num_bytes": 1547839078, "num_examples": 700}, {"name": "train", "num_bytes": 2852916299223.88, "num_examples": 1202797}], "download_size": 2978259415518, "dataset_size": 2854464138301.88}, "configs": [{"config_name": "default", "data_files": [{"split": "dev", "path": "data/dev-*"}, {"split": "train", "path": "data/train-*"}]}], "tags": ["image"]} | false | null | 2024-12-06T02:57:59 | 84 | 7 | false | 82455c6cd66db7f0e5bfce8d7a236441af59d6df |
OmniEdit
In this paper, we present OMNI-EDIT, which is an omnipotent editor to handle seven different image editing tasks with any aspect ratio seamlessly. Our contribution is in four folds: (1) OMNI-EDIT is trained by utilizing the supervision
from seven different specialist models to ensure task coverage. (2) we utilize importance sampling based on the scores provided by large multimodal models (like GPT-4o) instead of CLIP-score to improve the data quality.
📃Paper |… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/OmniEdit-Filtered-1.2M. | 16,374 | 72,222 | [
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"arxiv:2411.07199",
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"image"
] | 2024-11-11T07:40:47 | 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 | 680 | 7 | 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. | 25,478 | 150,091 | [
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"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"curator",
"synthetic"
] | 2025-01-27T20:02:16 | null | null |
67ae5cb70100bb7fb11fdb31 | getomni-ai/ocr-benchmark | getomni-ai | {"license": "mit", "size_categories": ["1K<n<10K"]} | false | null | 2025-02-21T06:34:31 | 41 | 7 | 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
| 1,848 | 2,924 | [
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] | 2025-02-13T20:57:27 | null | null |
67e0ce6de8133567a3f347e8 | generalagents/showdown-clicks | generalagents | {"license": "mit", "task_categories": ["image-to-text"], "language": ["en"], "size_categories": ["1K<n<10K"], "configs": [{"config_name": "default", "data_files": [{"split": "dev", "path": "showdown-clicks-dev/data.csv"}]}]} | false | null | 2025-03-31T07:10:38 | 7 | 7 | false | a6dfa6415d7fdd41fb5c0544e1dd9a5da439f1ce |
showdown-clicks
General Agents
🤗 Dataset | GitHub
showdown is a suite of offline and online benchmarks for computer-use agents.
showdown-clicks is a collection of 5,679 left clicks of humans performing various tasks in a macOS desktop environment. It is intended to evaluate instruction-following and low-level control capabilities of computer-use agents.
As of March 2025, we are releasing a subset of the full set, showdown-clicks-dev, containing 557 clicks. All examples are… See the full description on the dataset page: https://huggingface.co/datasets/generalagents/showdown-clicks. | 134 | 134 | [
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-24T03:15:57 | null | null |
67e4200962f39381a49cd4d0 | Rapidata/Recraft-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": 13265684267, "num_examples": 13000}], "download_size": 5160991901, "dataset_size": 13265684267}, "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"], "size_categories": ["100K<n<1M"], "pretty_name": "Recraft V2 vs. Lumina-15-2-25 / Aurora / Frames-23-1-25 / imagen-3 / Flux-1.1-pro / Flux-1-pro / Dalle-3 / Midjourney-5.2 / Stabel-Diffusion-3 - Human Preference Dataset"} | false | null | 2025-03-27T14:46:25 | 7 | 7 | false | eaf11e29c908ce1534dd233390f41c6df2eaff9f |
Rapidata Recraft-V2 Preference
This T2I dataset contains over 195k human responses from over 47k individual annotators, collected in just ~1 Day using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating Recraft-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/Recraft-V2_t2i_human_preference. | 528 | 528 | [
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"recraft",
"recraft v2"
] | 2025-03-26T15:40:57 | 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 | 29 | 7 | false | 5a355b76cee6387d370d99d7ff656e79cc10d2eb | null | 874 | 874 | [
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] | 2025-03-26T21:13:29 | null | null |
67ecdf7e693ef0b1e0d7a06b | a-m-team/AM-Math-Difficulty-RL | a-m-team | {"license": "cc-by-nc-4.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["math"], "size_categories": ["100K<n<1M"]} | false | null | 2025-04-02T08:39:29 | 7 | 7 | false | 32540e9bce5952736795ac78cf049a0757f601d3 | For more open-source datasets, models, and methodologies, please visit our GitHub repository.
We believe that the selection of training data for reinforcement learning is crucial.
To validate this, we conducted several experiments exploring how data difficulty influences training performance.
Our data sources originate from numerous excellent open-source projects, and we sincerely appreciate their contributions, without which our current achievements would not have been possible.… See the full description on the dataset page: https://huggingface.co/datasets/a-m-team/AM-Math-Difficulty-RL. | 164 | 164 | [
"task_categories:text-generation",
"language:en",
"license:cc-by-nc-4.0",
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"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2504.00829",
"region:us",
"math"
] | 2025-04-02T06:55:58 | null | null |
63270b5b0416b7adda873b3e | Gustavosta/Stable-Diffusion-Prompts | Gustavosta | {"license": ["unknown"], "annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "source_datasets": ["original"]} | false | null | 2022-09-18T22:38:59 | 477 | 6 | false | d816d4a05cb89bde39dd99284c459801e1e7e69a |
Stable Diffusion Dataset
This is a set of about 80,000 prompts filtered and extracted from the image finder for Stable Diffusion: "Lexica.art". It was a little difficult to extract the data, since the search engine still doesn't have a public API without being protected by cloudflare.
If you want to test the model with a demo, you can go to: "spaces/Gustavosta/MagicPrompt-Stable-Diffusion".
If you want to see the model, go to: "Gustavosta/MagicPrompt-Stable-Diffusion".
| 8,381 | 109,643 | [
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] | 2022-09-18T12:13:15 | null | null |
64382440c212a363c3ac15c8 | OpenAssistant/oasst1 | OpenAssistant | {"license": "apache-2.0", "dataset_info": {"features": [{"name": "message_id", "dtype": "string"}, {"name": "parent_id", "dtype": "string"}, {"name": "user_id", "dtype": "string"}, {"name": "created_date", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "role", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "review_count", "dtype": "int32"}, {"name": "review_result", "dtype": "bool"}, {"name": "deleted", "dtype": "bool"}, {"name": "rank", "dtype": "int32"}, {"name": "synthetic", "dtype": "bool"}, {"name": "model_name", "dtype": "string"}, {"name": "detoxify", "struct": [{"name": "toxicity", "dtype": "float64"}, {"name": "severe_toxicity", "dtype": "float64"}, {"name": "obscene", "dtype": "float64"}, {"name": "identity_attack", "dtype": "float64"}, {"name": "insult", "dtype": "float64"}, {"name": "threat", "dtype": "float64"}, {"name": "sexual_explicit", "dtype": "float64"}]}, {"name": "message_tree_id", "dtype": "string"}, {"name": "tree_state", "dtype": "string"}, {"name": "emojis", "sequence": [{"name": "name", "dtype": "string"}, {"name": "count", "dtype": "int32"}]}, {"name": "labels", "sequence": [{"name": "name", "dtype": "string"}, {"name": "value", "dtype": "float64"}, {"name": "count", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 100367999, "num_examples": 84437}, {"name": "validation", "num_bytes": 5243405, "num_examples": 4401}], "download_size": 41596430, "dataset_size": 105611404}, "language": ["en", "es", "ru", "de", "pl", "th", "vi", "sv", "bn", "da", "he", "it", "fa", "sk", "id", "nb", "el", "nl", "hu", "eu", "zh", "eo", "ja", "ca", "cs", "bg", "fi", "pt", "tr", "ro", "ar", "uk", "gl", "fr", "ko"], "tags": ["human-feedback"], "size_categories": ["100K<n<1M"], "pretty_name": "OpenAssistant Conversations"} | false | null | 2023-05-02T13:21:21 | 1,373 | 6 | false | fdf72ae0827c1cda404aff25b6603abec9e3399b |
OpenAssistant Conversations Dataset (OASST1)
Dataset Summary
In an effort to democratize research on large-scale alignment, we release OpenAssistant
Conversations (OASST1), a human-generated, human-annotated assistant-style conversation
corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292
quality ratings, resulting in over 10,000 fully annotated conversation trees. The corpus
is a product of a worldwide crowd-sourcing effort… See the full description on the dataset page: https://huggingface.co/datasets/OpenAssistant/oasst1. | 8,360 | 253,091 | [
"language:en",
"language:es",
"language:ru",
"language:de",
"language:pl",
"language:th",
"language:vi",
"language:sv",
"language:bn",
"language:da",
"language:he",
"language:it",
"language:fa",
"language:sk",
"language:id",
"language:nb",
"language:el",
"language:nl",
"language:hu",
"language:eu",
"language:zh",
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"arxiv:2304.07327",
"region:us",
"human-feedback"
] | 2023-04-13T15:48:16 | 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 | 654 | 6 | 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. | 4,066 | 222,713 | [
"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 |
65a67881c26011a4a77e2aca | omar07ibrahim/Alpaca | omar07ibrahim | null | false | null | 2024-01-16T12:37:55 | 6 | 6 | false | 0422485a5c4de92acd262d6ca3cf28cf018322c4 | null | 14 | 83 | [
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-01-16T12:37:21 | null | null |
65afeef01dbd85fd0cdcf7c8 | omar07ibrahim/orca | omar07ibrahim | null | false | null | 2024-01-23T18:24:06 | 6 | 6 | false | 5859a72678e8b94dd942b0cb28bac7d26070aa31 | null | 23 | 93 | [
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-01-23T16:53:04 | null | null |
65b0b0ed7ac1fb9fc7f65747 | omar07ibrahim/orca_firstpart_AZ | omar07ibrahim | null | false | null | 2024-01-24T06:44:45 | 6 | 6 | false | 1e5e9dc9af0abf67bdc423d8aa5fcc5f6af9d6e2 | null | 12 | 90 | [
"size_categories:100K<n<1M",
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-01-24T06:40:45 | null | null |
65b3fe30ca3eb32979ca1411 | omar07ibrahim/test9dpo | omar07ibrahim | null | false | null | 2024-01-26T18:47:27 | 6 | 6 | false | af6b9e605dc6d17e4b776713f15423ba4b07aadd | null | 16 | 99 | [
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"library:datasets",
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"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-01-26T18:47:12 | null | null |
65b55f122d8f64c77adadd66 | omar07ibrahim/ultrafeedback_binarized-BIZIM | omar07ibrahim | null | false | null | 2024-01-27T19:53:48 | 6 | 6 | false | b63048787a93c66475978782c277fef4bf69bb7e | null | 18 | 78 | [
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-01-27T19:52:50 | null | null |
65c666da10735dcd76ea29e1 | ibrahimhamamci/CT-RATE | ibrahimhamamci | {"title": "CT-RATE Dataset", "license": "cc-by-nc-sa-4.0", "extra_gated_prompt": "## Terms and Conditions for Using the CT-RATE Dataset\n\n**1. Acceptance of Terms**\nAccessing and using the CT-RATE dataset implies your agreement to these terms and conditions. If you disagree with any part, please refrain from using the dataset.\n\n**2. Permitted Use**\n- The dataset is intended solely for academic, research, and educational purposes.\n- Any commercial exploitation of the dataset without prior permission is strictly forbidden.\n- You must adhere to all relevant laws, regulations, and research ethics, including data privacy and protection standards.\n\n**3. Data Protection and Privacy**\n- Acknowledge the presence of sensitive information within the dataset and commit to maintaining data confidentiality.\n- Direct attempts to re-identify individuals from the dataset are prohibited.\n- Ensure compliance with data protection laws such as GDPR and HIPAA.\n\n**4. Attribution**\n- Cite the dataset and acknowledge the providers in any publications resulting from its use.\n- Claims of ownership or exclusive rights over the dataset or derivatives are not permitted.\n\n**5. Redistribution**\n- Redistribution of the dataset or any portion thereof is not allowed.\n- Sharing derived data must respect the privacy and confidentiality terms set forth.\n\n**6. Disclaimer**\nThe dataset is provided \"as is\" without warranty of any kind, either expressed or implied, including but not limited to the accuracy or completeness of the data.\n\n**7. Limitation of Liability**\nUnder no circumstances will the dataset providers be liable for any claims or damages resulting from your use of the dataset.\n\n**8. Access Revocation**\nViolation of these terms may result in the termination of your access to the dataset.\n\n**9. Amendments**\nThe terms and conditions may be updated at any time; continued use of the dataset signifies acceptance of the new terms.\n\n**10. Governing Law**\nThese terms are governed by the laws of the location of the dataset providers, excluding conflict of law rules.\n\n**Consent:**\nAccessing and using the CT-RATE dataset signifies your acknowledgment and agreement to these terms and conditions.\n", "extra_gated_fields": {"Name": "text", "Institution": "text", "Email": "text", "I have read and agree with Terms and Conditions for using the CT-RATE dataset": "checkbox"}, "configs": [{"config_name": "labels", "data_files": [{"split": "train", "path": "dataset/multi_abnormality_labels/train_predicted_labels.csv"}, {"split": "validation", "path": "dataset/multi_abnormality_labels/valid_predicted_labels.csv"}]}, {"config_name": "reports", "data_files": [{"split": "train", "path": "dataset/radiology_text_reports/train_reports.csv"}, {"split": "validation", "path": "dataset/radiology_text_reports/validation_reports.csv"}]}, {"config_name": "metadata", "data_files": [{"split": "train", "path": "dataset/metadata/train_metadata.csv"}, {"split": "validation", "path": "dataset/metadata/validation_metadata.csv"}]}]} | false | null | 2025-04-04T15:00:57 | 133 | 6 | false | 86d4322aa852bed5c1528c6b9787f4c1f731ca85 |
Developing Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomography
Welcome to the official page for our paper, which introduces CT-RATE—a pioneering dataset in 3D medical imaging that uniquely pairs textual data with image data focused on chest CT volumes. Here, you will find the CT-RATE dataset, comprising chest CT volumes paired with corresponding radiology text reports, multi-abnormality labels, and metadata, all freely accessible to researchers.… See the full description on the dataset page: https://huggingface.co/datasets/ibrahimhamamci/CT-RATE. | 36,050 | 346,479 | [
"license:cc-by-nc-sa-4.0",
"size_categories:100K<n<1M",
"format:csv",
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"modality:text",
"library:datasets",
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"library:mlcroissant",
"library:polars",
"arxiv:2403.17834",
"region:us"
] | 2024-02-09T17:54:34 | null | null |
65d61e35d192e46c9367fa6f | omar07ibrahim/datasetaz | omar07ibrahim | null | false | null | 2024-02-21T16:02:52 | 6 | 6 | false | 05b76dd2702c9c68003ad6388600b3ce53a02102 | null | 16 | 96 | [
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-02-21T16:00:53 | null | null |
65fc5a783bc54054aa2e6e62 | gretelai/synthetic_text_to_sql | gretelai | {"license": "apache-2.0", "task_categories": ["question-answering", "table-question-answering", "text-generation"], "language": ["en"], "tags": ["synthetic", "SQL", "text-to-SQL", "code"], "size_categories": ["100K<n<1M"]} | false | null | 2024-05-10T22:30:56 | 518 | 6 | false | 273a86f5f290e8d61b6767a9ff690c82bc990dc4 |
Image generated by DALL-E. See prompt for more details
synthetic_text_to_sql
gretelai/synthetic_text_to_sql is a rich dataset of high quality synthetic Text-to-SQL samples,
designed and generated using Gretel Navigator, and released under Apache 2.0.
Please see our release blogpost for more details.
The dataset includes:
105,851 records partitioned into 100,000 train and 5,851 test records
~23M total tokens, including ~12M SQL tokens
Coverage across 100 distinct… See the full description on the dataset page: https://huggingface.co/datasets/gretelai/synthetic_text_to_sql. | 6,713 | 44,528 | [
"task_categories:question-answering",
"task_categories:table-question-answering",
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
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"format:parquet",
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"library:polars",
"arxiv:2306.05685",
"region:us",
"synthetic",
"SQL",
"text-to-SQL",
"code"
] | 2024-03-21T16:04:08 | null | null |
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