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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 | 204 | 204 | 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. | 4,338 | 4,347 | [
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"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 | 89 | 87 | 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. | 3,241 | 3,241 | [
"task_categories:text-generation",
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"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-27T20:40:05 | null | null |
67d97c4be2b27852325fd8e2 | nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim | nvidia | {"license": "cc-by-4.0"} | false | null | 2025-03-21T15:02:34 | 72 | 72 | false | 9cd48351839af877ff365fa8bf06e1cf9e57d539 |
PhysicalAI-Robotics-GR00T-X-Embodiment-Sim
Github Repo: Isaac GR00T N1
We provide a set of datasets used for post-training of GR00T N1. Each dataset is a collection of trajectories from different robot embodiments and tasks.
Cross-embodied bimanual manipulation: 9k trajectories
Dataset Name
#trajectories
bimanual_panda_gripper.Threading
1000
bimanual_panda_hand.LiftTray
1000
bimanual_panda_gripper.ThreePieceAssembly
1000… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim. | 14,348 | 14,348 | [
"license:cc-by-4.0",
"region:us"
] | 2025-03-18T13:59:39 | null | null |
676f70846bf205795346d2be | FreedomIntelligence/medical-o1-reasoning-SFT | FreedomIntelligence | {"license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "language": ["en", "zh"], "tags": ["medical", "biology"], "configs": [{"config_name": "en", "data_files": "medical_o1_sft.json"}, {"config_name": "zh", "data_files": "medical_o1_sft_Chinese.json"}]} | false | null | 2025-02-22T05:15:38 | 533 | 51 | false | 61536c1d80b2c799df6800cc583897b77d2c86d2 |
News
[2025/02/22] We released the distilled dataset from Deepseek-R1 based on medical verifiable problems. You can use it to initialize your models with the reasoning chain from Deepseek-R1.
[2024/12/25] We open-sourced the medical reasoning dataset for SFT, built on medical verifiable problems and an LLM verifier.
Introduction
This dataset is used to fine-tune HuatuoGPT-o1, a medical LLM designed for advanced medical reasoning. This dataset is constructed using GPT-4o… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT. | 27,363 | 44,592 | [
"task_categories:question-answering",
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"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2412.18925",
"region:us",
"medical",
"biology"
] | 2024-12-28T03:29:08 | 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 | 584 | 39 | false | 8520b649430617c2be4490f424d251d09d835ed3 |
中文基于满血DeepSeek-R1蒸馏数据集（Chinese-Data-Distill-From-R1）
🤗 Hugging Face | 🤖 ModelScope | 🚀 Github | 📑 Blog
注意：提供了直接SFT使用的版本，点击下载。将数据中的思考和答案整合成output字段，大部分SFT代码框架均可直接直接加载训练。
本数据集为中文开源蒸馏满血R1的数据集，数据集中不仅包含math数据，还包括大量的通用类型数据，总数量为110K。
为什么开源这个数据？
R1的效果十分强大，并且基于R1蒸馏数据SFT的小模型也展现出了强大的效果，但检索发现，大部分开源的R1蒸馏数据集均为英文数据集。 同时，R1的报告中展示，蒸馏模型中同时也使用了部分通用场景数据集。
为了帮助大家更好地复现R1蒸馏模型的效果，特此开源中文数据集。该中文数据集中的数据分布如下：
Math：共计36568个样本，
Exam：共计2432个样本，
STEM：共计12648个样本，… See the full description on the dataset page: https://huggingface.co/datasets/Congliu/Chinese-DeepSeek-R1-Distill-data-110k. | 7,860 | 9,716 | [
"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 |
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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{"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"}, 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"path": "solutions_decontaminated/train-*"}]}, {"config_name": "solutions_py", "data_files": [{"split": "train", "path": "solutions_py/train-*"}]}, {"config_name": "solutions_py_decontaminated", "data_files": [{"split": "train", "path": "solutions_py_decontaminated/train-*"}]}, {"config_name": "solutions_w_editorials", "data_files": [{"split": "train", "path": "solutions_w_editorials/train-*"}]}, {"config_name": "solutions_w_editorials_decontaminated", "data_files": [{"split": "train", "path": "solutions_w_editorials_decontaminated/train-*"}]}, {"config_name": "solutions_w_editorials_py", "data_files": [{"split": "train", "path": "solutions_w_editorials_py/train-*"}]}, {"config_name": "solutions_w_editorials_py_decontaminated", "data_files": [{"split": "train", "path": "solutions_w_editorials_py_decontaminated/train-*"}]}, {"config_name": "test_input_generator", "data_files": [{"split": "train", "path": "test_input_generator/train-*"}]}], "license": "cc-by-4.0"} | false | null | 2025-03-17T11:29:08 | 102 | 38 | false | 5f9671cf3779c3c709bd9f6f61b38ef3f061d5c8 |
Dataset Card for CodeForces-CoTs
Dataset description
CodeForces-CoTs is a large-scale dataset for training reasoning models on competitive programming tasks. It consists of 10k CodeForces problems with up to five reasoning traces generated by DeepSeek R1. We did not filter the traces for correctness, but found that around 84% of the Python ones pass the public tests.
The dataset consists of several subsets:
solutions: we prompt R1 to solve the problem and produce code.… See the full description on the dataset page: https://huggingface.co/datasets/open-r1/codeforces-cots. | 5,942 | 5,942 | [
"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 |
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 | 41 | 37 | false | 9f6440001c15da8e7c7516fdbb3d2ce49de711de |
This dataset actually only contains ~17k unique prompts and was duplicated by ~100x by accident.
| 1,901 | 1,901 | [
"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 |
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 | 35 | 35 | 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. | 35 | 35 | [
"license:other",
"arxiv:2503.15478",
"region:us"
] | 2025-02-11T21:36:02 | 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 | 452 | 32 | false | 99eea5dc6bfa45a925eb42600e81dc90377ba237 | NaturalReasoning is a large-scale dataset for general reasoning tasks. It consists of high-quality challenging reasoning questions backtranslated from pretraining corpora DCLM and FineMath. The questions have been deduplicated and decontaminated from popular reasoning benchmarks including MATH, GPQA, MMLU-Pro, MMLU-STEM. For each question, we extract the reference final answer from the original document from the pretraining corpora if possible. We also provide a model-generated response from… See the full description on the dataset page: https://huggingface.co/datasets/facebook/natural_reasoning. | 13,783 | 14,467 | [
"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 |
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 | 31 | 30 | 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. | 4,034 | 4,034 | [
"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 |
67b20fc10861cec33b3afb8a | Conard/fortune-telling | Conard | {"license": "mit"} | false | null | 2025-02-17T05:13:43 | 90 | 25 | false | 6261fe0d35a75997972bbfcd9828020e340303fb | null | 5,549 | 5,615 | [
"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 |
67dabea523ec1d597d1e0012 | MaziyarPanahi/Llama-Nemotron-Post-Training-Dataset-v1-ShareGPT | MaziyarPanahi | {"license": "cc-by-4.0"} | false | null | 2025-03-23T21:34:33 | 25 | 25 | false | 1dc5274f9328178e12e1aa471049c08a72f5287e |
Llama-Nemotron-Post-Training-Dataset-v1 in ShareGPT Format
This dataset is a conversion of NVIDIA's Llama-Nemotron-Post-Training-Dataset-v1
into the ShareGPT format while preserving the original splits and columns.
Format
Each example contains all original fields plus a messages array:
{
"input": "original input text",
"output": "original output text",
... (other original columns) ...,
"messages": [
{"role": "user", "content": "User message"},
{"role":… See the full description on the dataset page: https://huggingface.co/datasets/MaziyarPanahi/Llama-Nemotron-Post-Training-Dataset-v1-ShareGPT. | 425 | 425 | [
"license:cc-by-4.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-19T12:55:01 | null | null |
67d305619f485955bf117049 | nvidia/HelpSteer3 | nvidia | {"license": "cc-by-4.0", "language": ["en", "zh", "ko", "fr", "es", "ru", "ja", "de", "it", "pt", "pl", "id", "nl", "vi"], "pretty_name": "HelpSteer3", "size_categories": ["10K<n<100K"], "tags": ["human-feedback"], "configs": [{"config_name": "preference", "default": true, "data_files": [{"split": "train", "path": "preference/train.jsonl.gz"}, {"split": "validation", "path": "preference/validation.jsonl.gz"}]}, {"config_name": "feedback", "data_files": [{"split": "train", "path": "feedback/train.jsonl.gz"}, {"split": "validation", "path": "feedback/validation.jsonl.gz"}]}, {"config_name": "edit", "data_files": [{"split": "train", "path": "edit/train.jsonl.gz"}, {"split": "validation", "path": "edit/validation.jsonl.gz"}]}, {"config_name": "edit_quality", "data_files": [{"split": "train", "path": "edit_quality/train.jsonl.gz"}, {"split": "validation", "path": "edit_quality/validation.jsonl.gz"}]}]} | false | null | 2025-03-18T19:51:32 | 24 | 24 | false | 7366103dbb732074dcf866560d2431d0ae8c9b1d |
HelpSteer3
HelpSteer3 is an open-source Helpfulness Dataset (CC-BY-4.0) that supports aligning models to become more helpful in responding to user prompts.
When used to tune Llama 3.3 70B Instruct Models to perform a novel approach to Inference Time Scaling (ITS) for open-ended, general-domain tasks, we achieve as high as 93.4% on Arena Hard, which makes it No. 1 on the benchmark as of 18 Mar 2025.
See details on the paper at https://arxiv.org/abs/2503.04378.
Models were trained… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/HelpSteer3. | 518 | 518 | [
"language:en",
"language:zh",
"language:ko",
"language:fr",
"language:es",
"language:ru",
"language:ja",
"language:de",
"language:it",
"language:pt",
"language:pl",
"language:id",
"language:nl",
"language:vi",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2503.04378",
"region:us",
"human-feedback"
] | 2025-03-13T16:18:41 | null | null |
67d967709b5f9bcc5eef92e1 | HuggingFaceTB/stack-edu | HuggingFaceTB | {"dataset_info": [{"config_name": "C", "features": [{"name": "blob_id", "dtype": "large_string"}, {"name": "language", "dtype": "large_string"}, {"name": "repo_name", "dtype": "large_string"}, {"name": "path", "dtype": "large_string"}, {"name": "src_encoding", "dtype": "large_string"}, {"name": "length_bytes", "dtype": "int64"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}, {"name": "detected_licenses", "large_list": "large_string"}, {"name": "license_type", "dtype": "large_string"}], "splits": [{"name": "train", "num_bytes": 1100442974, "num_examples": 5848375}], "download_size": 571816053, "dataset_size": 1100442974}, {"config_name": "CSharp", "features": [{"name": "blob_id", "dtype": "large_string"}, {"name": "language", "dtype": "large_string"}, {"name": "repo_name", "dtype": "large_string"}, {"name": "path", "dtype": "large_string"}, {"name": "src_encoding", "dtype": 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1632803797, "dataset_size": 3167426435}, {"config_name": "Go", "features": [{"name": "blob_id", "dtype": "large_string"}, {"name": "language", "dtype": "large_string"}, {"name": "repo_name", "dtype": "large_string"}, {"name": "path", "dtype": "large_string"}, {"name": "src_encoding", "dtype": "large_string"}, {"name": "length_bytes", "dtype": "int64"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}, {"name": "detected_licenses", "large_list": "large_string"}, {"name": "license_type", "dtype": "large_string"}, {"name": "detected_licenses_right", "large_list": "large_string"}, {"name": "license_type_right", "dtype": "large_string"}], "splits": [{"name": "train", "num_bytes": 433053889, "num_examples": 1917163}], "download_size": 179388495, "dataset_size": 433053889}, {"config_name": "Java", "features": [{"name": "blob_id", "dtype": "large_string"}, {"name": "language", "dtype": "large_string"}, {"name": "repo_name", "dtype": "large_string"}, {"name": 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💻 Stack-Edu
Stack-Edu is a 125B token dataset of educational code filtered from The Stack v2, precisely the curated training corpus of StarCoder2 models denoted StarCoder2Data. It is intended for Language Models training.
This dataset was curated using a classifier-based filtering strategy, inspired by 📚 FineWeb-Edu, to retain only the highest-quality educational programming content.
Stack-Edu shows consistent improvement over StarCoder2data on all the programming languages on… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/stack-edu. | 469 | 469 | [
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"arxiv:2502.02737",
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] | 2025-03-18T12:30:40 | 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": 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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. | 248,188 | 2,292,866 | [
"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 |
67c8270b5999e7df91a854da | yaak-ai/L2D | yaak-ai | {"license": "apache-2.0", "task_categories": ["robotics"], "tags": ["LeRobot"], "configs": [{"config_name": "default", "data_files": "data/*/*.parquet"}]} | false | null | 2025-03-10T18:34:05 | 29 | 17 | false | 49115405b552802c9838d4a4c85a4ed947f901b3 | This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "KIA Niro EV 2023",
"total_episodes": 100,
"total_frames": 28519,
"total_tasks": 1,
"total_videos": 700,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 10,
"splits": {
"train": "0:100"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/yaak-ai/L2D. | 1,364 | 1,364 | [
"task_categories:robotics",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"LeRobot"
] | 2025-03-05T10:27:23 | 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", "extra_gated_fields": {"Name": "text", "Company/Organization": "text", "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 | 2025-03-24T10:55:26 | 17 | 17 | false | d1435ee6dd72538fb127b3c30f1f17ad3ec95661 |
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. | 41 | 41 | [
"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 |
67cba813ef7ed9b8e2a948c7 | canopylabs/zac-sample-dataset | canopylabs | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 48000}}}], "splits": [{"name": "train", "num_bytes": 13147142.424794896, "num_examples": 20}], "download_size": 10349037, "dataset_size": 13147142.424794896}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-03-08T02:14:46 | 16 | 16 | false | 5464e5b186dab0d49049eca0b28774ad9371fc89 | null | 510 | 510 | [
"size_categories:n<1K",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-08T02:14:43 | null | null |
625552d2b339bb03abe3432d | openai/gsm8k | openai | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "task_ids": [], "paperswithcode_id": "gsm8k", "pretty_name": "Grade School Math 8K", "tags": ["math-word-problems"], "dataset_info": [{"config_name": "main", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3963202, "num_examples": 7473}, {"name": "test", "num_bytes": 713732, "num_examples": 1319}], "download_size": 2725633, "dataset_size": 4676934}, {"config_name": "socratic", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5198108, "num_examples": 7473}, {"name": "test", "num_bytes": 936859, "num_examples": 1319}], "download_size": 3164254, "dataset_size": 6134967}], "configs": [{"config_name": "main", "data_files": [{"split": "train", "path": "main/train-*"}, {"split": "test", "path": "main/test-*"}]}, {"config_name": "socratic", "data_files": [{"split": "train", "path": "socratic/train-*"}, {"split": "test", "path": "socratic/test-*"}]}]} | false | null | 2024-01-04T12:05:15 | 654 | 15 | 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. | 329,958 | 4,187,247 | [
"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 |
67ce2fb269ac5540794d0bf6 | CharlieDreemur/OpenManus-RL | CharlieDreemur | {"language": ["en"], "tags": ["sft", "instruction-tuning", "conversational-ai"], "license": "apache-2.0", "task_categories": ["text-generation"], "pretty_name": "OpenManusRL", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "conversations", "list": [{"name": "role", "dtype": "string"}, {"name": "content", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 277895199, "num_examples": 48927}], "download_size": 73312767, "dataset_size": 277895199}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-03-15T01:29:38 | 40 | 15 | false | b102de3f0a2e40221fc923ed9f34756251fc666c |
Dataset Card for OpenManusRL
Dataset Description
Overview
💻 [Github Repo]
OpenManusRL combines agent trajectories from AgentInstruct, Agent-FLAN and AgentTraj-L(AgentGym) with features:
🔍 ReAct Framework - Reasoning-Acting integration
🧠 Structured Training - Separate format/reasoning learning
🚫 Anti-Hallucination - Negative samples + environment grounding
🌐 6 Domains - OS, DB, Web, KG, Household, E-commerce
Dataset Overview
Source… See the full description on the dataset page: https://huggingface.co/datasets/CharlieDreemur/OpenManus-RL. | 1,434 | 1,434 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2310.12823",
"arxiv:2403.12881",
"arxiv:2406.04151",
"region:us",
"sft",
"instruction-tuning",
"conversational-ai"
] | 2025-03-10T00:17:54 | null | null |
67aa021ced8d8663d42505cc | open-r1/OpenR1-Math-220k | open-r1 | {"license": "apache-2.0", "language": ["en"], "configs": [{"config_name": "all", "data_files": [{"split": "train", "path": "all/train-*"}]}, {"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}, {"config_name": "extended", "data_files": [{"split": "train", "path": "extended/train-*"}]}], "dataset_info": [{"config_name": "all", "features": [{"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "problem_type", "dtype": "string"}, {"name": "question_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "uuid", "dtype": "string"}, {"name": "is_reasoning_complete", "sequence": "bool"}, {"name": "generations", "sequence": "string"}, {"name": "correctness_math_verify", "sequence": "bool"}, {"name": "correctness_llama", "sequence": "bool"}, {"name": "finish_reasons", "sequence": "string"}, {"name": "correctness_count", "dtype": "int64"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 9734110026, "num_examples": 225129}], "download_size": 4221672067, "dataset_size": 9734110026}, {"config_name": "default", "features": [{"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "problem_type", "dtype": "string"}, {"name": "question_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "uuid", "dtype": "string"}, {"name": "is_reasoning_complete", "sequence": "bool"}, {"name": "generations", "sequence": "string"}, {"name": "correctness_math_verify", "sequence": "bool"}, {"name": "correctness_llama", "sequence": "bool"}, {"name": "finish_reasons", "sequence": "string"}, {"name": "correctness_count", "dtype": "int64"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 4964543659, "num_examples": 93733}], "download_size": 2149897914, "dataset_size": 4964543659}, {"config_name": "extended", "features": [{"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "problem_type", "dtype": "string"}, {"name": "question_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "uuid", "dtype": "string"}, {"name": "is_reasoning_complete", "sequence": "bool"}, {"name": "generations", "sequence": "string"}, {"name": "correctness_math_verify", "sequence": "bool"}, {"name": "correctness_llama", "sequence": "bool"}, {"name": "finish_reasons", "sequence": "string"}, {"name": "correctness_count", "dtype": "int64"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 4769566550, "num_examples": 131396}], "download_size": 2063936457, "dataset_size": 4769566550}]} | false | null | 2025-02-18T11:45:27 | 525 | 14 | 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. | 51,790 | 69,696 | [
"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 |
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 | 7,640 | 13 | false | 68ba7694e23014788dcc8ab5afe613824f45a05c | 🧠 Awesome ChatGPT Prompts [CSV dataset]
This is a Dataset Repository of Awesome ChatGPT Prompts
View All Prompts on GitHub
License
CC-0
| 12,011 | 137,062 | [
"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 |
66a145d28f0d2327e07fc119 | cfahlgren1/hub-stats | cfahlgren1 | {"license": "apache-2.0", "configs": [{"config_name": "models", "data_files": "models.parquet"}, {"config_name": "datasets", "data_files": "datasets.parquet"}, {"config_name": "spaces", "data_files": "spaces.parquet"}, {"config_name": "posts", "data_files": "posts.parquet"}, {"config_name": "papers", "data_files": "daily_papers.parquet"}]} | false | null | 2025-03-23T23:41:24 | 40 | 13 | false | d32176a0467d9e921a81d51ae2c872ab2b99741b |
NEW Changes Feb 27th
Added new fields on the models split: downloadsAllTime, safetensors, gguf
Added new field on the datasets split: downloadsAllTime
Added new split: papers which is all of the Daily Papers
Updated Daily
| 7,488 | 17,124 | [
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-07-24T18:20:02 | null | null |
67c5a5ba52976b223005f88b | DropletX/DropletVideo-10M | DropletX | {"license": "cc-by-nc-sa-4.0", "task_categories": ["image-to-video", "text-to-video"], "language": ["en"], "size_categories": ["10M<n<100M"], "extra_gated_prompt": "You agree to not use the data to conduct experiments that cause harm to human subjects.", "extra_gated_fields": {"Name": "text", "Company/Organization": "text", "E-Mail": "text", "Job title": "text"}} | false | null | 2025-03-19T16:15:37 | 24 | 13 | false | 3f5bc6339a46d3b9b2a4469c081b2d37f881d6ee |
🔍 Dataset Note:
DropletVideo-1M
is the premium subset of DropletVideo-10M, filtered with aesthetic score > 4.51 and image quality score > 7.51.
✈️ Introduction
The challenge of spatiotemporal consistency has long existed in the field of video generation. We have released the open-source dataset DropletVideo-10M —the world's largest video generation dataset with spatiotemporal consistency. It… See the full description on the dataset page: https://huggingface.co/datasets/DropletX/DropletVideo-10M. | 313 | 313 | [
"task_categories:image-to-video",
"task_categories:text-to-video",
"language:en",
"license:cc-by-nc-sa-4.0",
"size_categories:10M<n<100M",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2503.06053",
"region:us"
] | 2025-03-03T12:51:06 | null | null |
67c122a87c100c8caa21c89d | TIGER-Lab/VisualWebInstruct | TIGER-Lab | {"language": ["en"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["question-answering", "visual-question-answering"], "pretty_name": "VisualWebInstruct", "tags": ["math", "science"], "configs": [{"config_name": "example", "data_files": [{"split": "train", "path": "data/train-*"}]}, {"config_name": "conversation", "data_files": [{"split": "train", "path": "mixed_conversation.parquet"}]}, {"config_name": "visualwebinstruct", "data_files": [{"split": "train", "path": "visualwebinstruct_qa.parquet"}]}]} | false | null | 2025-03-24T05:26:29 | 25 | 12 | false | 08c69039c7caa146fbff0fbf936b9d2fe1a69ded |
VisualWebInstruct: Scaling up Multimodal Instruction Data through Web Search
VisualWebInstruct is a large-scale, diverse multimodal instruction dataset designed to enhance vision-language models' reasoning capabilities. The dataset contains approximately 900K question-answer (QA) pairs, with 40% consisting of visual QA pairs associated with 163,743 unique images, while the remaining 60% are text-only QA pairs.
Links
GitHub Repository
Research Paper
Project Website… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/VisualWebInstruct. | 962 | 982 | [
"task_categories:question-answering",
"task_categories:visual-question-answering",
"language:en",
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2503.10582",
"region:us",
"math",
"science"
] | 2025-02-28T02:42:48 | null | null |
67a557ba9330ead027242110 | simplescaling/s1K-1.1 | simplescaling | {"language": "en", "license": "mit", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "solution", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "cot_type", "dtype": "string"}, {"name": "source_type", "dtype": "string"}, {"name": "metadata", "dtype": "string"}, {"name": "gemini_thinking_trajectory", "dtype": "string"}, {"name": "gemini_attempt", "dtype": "string"}, {"name": "deepseek_thinking_trajectory", "dtype": "string"}, {"name": "deepseek_attempt", "dtype": "string"}, {"name": "gemini_grade", "dtype": "string"}, {"name": "gemini_grade_reason", "dtype": "string"}, {"name": "deepseek_grade", "dtype": "string"}, {"name": "deepseek_grade_reason", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 48313304, "num_examples": 1000}], "download_size": 22323185, "dataset_size": 48313304}, "tags": ["curator"]} | false | null | 2025-02-27T18:09:26 | 101 | 11 | false | 96c411f1fe4c49d20f0e2a1565f61e1a28b0b84d |
Dataset Card for s1K
Dataset Summary
s1K-1.1 consists of the same 1,000 questions as in s1K but with traces instead generated by DeepSeek r1. We find that these traces lead to much better performance.
Usage
# pip install -q datasets
from datasets import load_dataset
ds = load_dataset("simplescaling/s1K-1.1")["train"]
ds[0]
Dataset Structure
Data Instances
An example looks as follows:
{
'solution': '1. **Rewrite the function using… See the full description on the dataset page: https://huggingface.co/datasets/simplescaling/s1K-1.1. | 7,036 | 9,580 | [
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2501.19393",
"region:us",
"curator"
] | 2025-02-07T00:45:46 | null | null |
67b3495a2f3994b7d95dde92 | Congliu/Chinese-DeepSeek-R1-Distill-data-110k-SFT | Congliu | {"license": "apache-2.0", "language": ["zh"], "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "text2text-generation", "question-answering"]} | false | null | 2025-02-19T13:24:55 | 152 | 11 | false | 263435dc9a8cc822449b6f3531794486f8141be6 |
中文基于满血DeepSeek-R1蒸馏数据集（Chinese-Data-Distill-From-R1）
🤗 Hugging Face | 🤖 ModelScope | 🚀 Github | 📑 Blog
注意：该版本为，可以直接SFT使用的版本，将原始数据中的思考和答案整合成output字段，大部分SFT代码框架均可直接直接加载训练。
本数据集为中文开源蒸馏满血R1的数据集，数据集中不仅包含math数据，还包括大量的通用类型数据，总数量为110K。
为什么开源这个数据？
R1的效果十分强大，并且基于R1蒸馏数据SFT的小模型也展现出了强大的效果，但检索发现，大部分开源的R1蒸馏数据集均为英文数据集。 同时，R1的报告中展示，蒸馏模型中同时也使用了部分通用场景数据集。
为了帮助大家更好地复现R1蒸馏模型的效果，特此开源中文数据集。该中文数据集中的数据分布如下：
Math：共计36568个样本，
Exam：共计2432个样本，
STEM：共计12648个样本，… See the full description on the dataset page: https://huggingface.co/datasets/Congliu/Chinese-DeepSeek-R1-Distill-data-110k-SFT. | 4,919 | 5,888 | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:question-answering",
"language:zh",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-17T14:36:10 | null | null |
67c5ffdbf2e146eac1f0edb9 | DropletX/DropletVideo-1M | DropletX | {"license": "cc-by-nc-sa-4.0", "task_categories": ["image-to-video", "text-to-video"], "language": ["en"], "size_categories": ["10M<n<100M"], "extra_gated_prompt": "You agree to not use the data to conduct experiments that cause harm to human subjects.", "extra_gated_fields": {"Name": "text", "Company/Organization": "text", "E-Mail": "text", "Job title": "text"}} | false | null | 2025-03-19T16:16:33 | 20 | 11 | false | c7f9e1e130fe2858a1ecb46d979d474114ca1baa |
🔍 Dataset Note:
DropletVideo-1M
is the premium subset of DropletVideo-10M, filtered with aesthetic score > 4.51 and image quality score > 7.51.
✈️ Introduction
The challenge of spatiotemporal consistency has long existed in the field of video generation. We have released the open-source dataset DropletVideo-10M —the world's largest video generation dataset with spatiotemporal consistency. It… See the full description on the dataset page: https://huggingface.co/datasets/DropletX/DropletVideo-1M. | 196 | 196 | [
"task_categories:image-to-video",
"task_categories:text-to-video",
"language:en",
"license:cc-by-nc-sa-4.0",
"size_categories:n<1K",
"format:imagefolder",
"modality:image",
"library:datasets",
"library:mlcroissant",
"arxiv:2503.06053",
"region:us"
] | 2025-03-03T19:15:39 | null | null |
67810a69793bdaf579c3710a | starvector/svg-stack | starvector | {} | false | null | 2025-01-10T11:57:29 | 11 | 10 | false | 1d922ec145f5ab6e3ff0e874235aff0d8a9dec91 |
Dataset Card for svg-stack
Dataset Description
This dataset contains SVG code examples for training and evaluating SVG models for image vectorization.
Dataset Structure
Features
The dataset contains the following fields:
Field Name
Description
Filename
Unique ID for each SVG
Svg
SVG code
Usage
from datasets import load_dataset
dataset = load_dataset("starvector/svg-stack")… See the full description on the dataset page: https://huggingface.co/datasets/starvector/svg-stack. | 476 | 991 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2312.11556",
"region:us"
] | 2025-01-10T11:54:17 | null | null |
67dbb87a09b2df33607508e0 | snuh/ClinicalQA | snuh | {"license": "apache-2.0", "dataset_info": {"features": [{"name": "question_id", "dtype": "int64"}, {"name": "chief_complaint", "dtype": "string"}, {"name": "purpose", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "exam", "dtype": "string"}, {"name": "options", "struct": [{"name": "option_A", "dtype": "string"}, {"name": "option_B", "dtype": "string"}, {"name": "option_C", "dtype": "string"}, {"name": "option_D", "dtype": "string"}, {"name": "option_E", "dtype": "string"}]}, {"name": "answer", "dtype": "string"}, {"name": "explanation", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3477660, "num_examples": 1015}], "download_size": 1641371, "dataset_size": 3477660}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-03-21T07:21:29 | 10 | 10 | false | 924db36bd2780b706b82c4a2ec7339e5eaaabe03 |
SNUH-HARI/ClinicalQA
Curated and shared by: SNUH-HARI (Seoul National University Hospital Healthcare AI Research Institute)
Language(s) (NLP): Korean
Repository: SNUH-HARI/ClinicalQA
Dataset Summary
The ClinicalQA dataset is designed for Korean medical knowledge question-answering. This dataset includes questions and answers at the level of the national medical licensing examination and consists of problems based on various chief complaints and medical specialties.… See the full description on the dataset page: https://huggingface.co/datasets/snuh/ClinicalQA. | 204 | 204 | [
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-20T06:40:58 | null | null |
649444227853dd12c3bbadd8 | Amod/mental_health_counseling_conversations | Amod | {"license": "openrail", "task_categories": ["text-generation", "question-answering"], "language": ["en"], "tags": ["medical"], "size_categories": ["1K<n<10K"]} | false | null | 2024-04-05T08:30:03 | 336 | 9 | false | 4672e03c7f1a7b2215eb4302b83ca50449ce2553 |
Amod/mental_health_counseling_conversations
Dataset Summary
This dataset is a collection of questions and answers sourced from two online counseling and therapy platforms. The questions cover a wide range of mental health topics, and the answers are provided by qualified psychologists. The dataset is intended to be used for fine-tuning language models to improve their ability to provide mental health advice.
Supported Tasks and Leaderboards
The… See the full description on the dataset page: https://huggingface.co/datasets/Amod/mental_health_counseling_conversations. | 5,071 | 62,834 | [
"task_categories:text-generation",
"task_categories:question-answering",
"language:en",
"license:openrail",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"doi:10.57967/hf/1581",
"region:us",
"medical"
] | 2023-06-22T12:52:50 | null | null |
6532270e829e1dc2f293d6b8 | gaia-benchmark/GAIA | gaia-benchmark | {"language": ["en"], "pretty_name": "General AI Assistants Benchmark", "extra_gated_prompt": "To avoid contamination and data leakage, you agree to not reshare this dataset outside of a gated or private repository on the HF hub.", "extra_gated_fields": {"I agree to not reshare the GAIA submissions set according to the above conditions": "checkbox"}} | false | null | 2025-02-13T08:36:12 | 274 | 9 | false | 897f2dfbb5c952b5c3c1509e648381f9c7b70316 |
GAIA dataset
GAIA is a benchmark which aims at evaluating next-generation LLMs (LLMs with augmented capabilities due to added tooling, efficient prompting, access to search, etc).
We added gating to prevent bots from scraping the dataset. Please do not reshare the validation or test set in a crawlable format.
Data and leaderboard
GAIA is made of more than 450 non-trivial question with an unambiguous answer, requiring different levels of tooling and autonomy to solve. It… See the full description on the dataset page: https://huggingface.co/datasets/gaia-benchmark/GAIA. | 9,706 | 34,694 | [
"language:en",
"arxiv:2311.12983",
"region:us"
] | 2023-10-20T07:06:54 | null | |
66a53dc7d40a13036c5f2ebe | mlabonne/FineTome-100k | mlabonne | {"dataset_info": {"features": [{"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "source", "dtype": "string"}, {"name": "score", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 239650960.7474458, "num_examples": 100000}], "download_size": 116531415, "dataset_size": 239650960.7474458}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2024-07-29T09:52:30 | 187 | 9 | false | c2343c1372ff31f51aa21248db18bffa3193efdb |
FineTome-100k
The FineTome dataset is a subset of arcee-ai/The-Tome (without arcee-ai/qwen2-72b-magpie-en), re-filtered using HuggingFaceFW/fineweb-edu-classifier.
It was made for my article "Fine-tune Llama 3.1 Ultra-Efficiently with Unsloth".
| 17,358 | 81,896 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-07-27T18:34:47 | null | null |
67954a35c16b74e280f72f15 | ServiceNow-AI/R1-Distill-SFT | ServiceNow-AI | {"license": "cc-by-nc-sa-4.0", "configs": [{"config_name": "v0", "data_files": [{"split": "train", "path": "v0/train-*"}]}, {"config_name": "v1", "data_files": [{"split": "train", "path": "v1/train-*"}]}], "dataset_info": [{"config_name": "v0", "features": [{"name": "id", "dtype": "string"}, {"name": "reannotated_assistant_content", "dtype": "string"}, {"name": "problem", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "verified", "dtype": "null"}, {"name": "quality_metrics", "dtype": "null"}], "splits": [{"name": "train", "num_bytes": 1279431141, "num_examples": 171647}], "download_size": 554111459, "dataset_size": 1279431141}, {"config_name": "v1", "features": [{"name": "id", "dtype": "string"}, {"name": "reannotated_assistant_content", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "reannotated_messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "source_dataset", "dtype": "string"}, {"name": "verified", "dtype": "null"}, {"name": "quality_metrics", "dtype": "null"}], "splits": [{"name": "train", "num_bytes": 25783989151, "num_examples": 1679162}], "download_size": 11128580062, "dataset_size": 25783989151}]} | false | null | 2025-02-08T22:46:58 | 286 | 9 | false | 16e851e107d928b9069dcce428a2d3d7154e5353 |
🔉 𝗦𝗟𝗔𝗠 𝗹𝗮𝗯 - 𝗥𝟭-𝗗𝗶𝘀𝘁𝗶𝗹𝗹-𝗦𝗙𝗧 Dataset
Lewis Tunstall, Ed Beeching, Loubna Ben Allal, Clem Delangue 🤗 and others at Hugging Face announced today that they are - 𝗼𝗽𝗲𝗻𝗹𝘆 𝗿𝗲𝗽𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗥𝟭 🔥
We at 𝗦𝗟𝗔𝗠 𝗹𝗮𝗯 (ServiceNow Language Models) have been cooking up something as well.
Inspired by Open-r1, we have decided to open source the data stage-by-stage to support the open source community.
𝗕𝗼𝗼𝗸𝗺𝗮𝗿𝗸 this page!
KEY DETAILS:
⚗️ Distilled… See the full description on the dataset page: https://huggingface.co/datasets/ServiceNow-AI/R1-Distill-SFT. | 3,816 | 10,882 | [
"license:cc-by-nc-sa-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-01-25T20:31:49 | null | null |
67e1a2e3c28a090230c35bc0 | Armewer/Solara-Executor | Armewer | {"license": "mit", "task_categories": ["text-classification"], "language": ["en", "de", "es", "ar", "zh", "sw", "el", "sv"], "tags": ["code"], "pretty_name": "Free Solara Executor 2025", "size_categories": ["100K<n<1M"]} | false | null | 2025-03-24T19:07:17 | 9 | 9 | false | abb4176d777a88c8cb0ca9b8cb2f568b9c9ec045 |
CLICK HERE TO DOWNLOAD
Requirements:
Windows 10/11
Features:
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This method requires the Internet at the time of activation.
Freeze Trial
Freeze 30-day trial period, you can use this option in the script to lock this trial period for the lifetime so that you wont have to reset the trial again and your trial wont expire.
This method requires the Internet at the time of… See the full description on the dataset page: https://huggingface.co/datasets/Armewer/Solara-Executor. | 0 | 0 | [
"task_categories:text-classification",
"language:en",
"language:de",
"language:es",
"language:ar",
"language:zh",
"language:sw",
"language:el",
"language:sv",
"license:mit",
"size_categories:100K<n<1M",
"region:us",
"code"
] | 2025-03-24T18:22:27 | null | null |
621ffdd236468d709f181e5e | cais/mmlu | cais | {"annotations_creators": ["no-annotation"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["multiple-choice-qa"], "paperswithcode_id": "mmlu", "pretty_name": "Measuring Massive Multitask Language Understanding", "language_bcp47": ["en-US"], "dataset_info": [{"config_name": "abstract_algebra", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 49618.6654322746, "num_examples": 100}, {"name": "validation", "num_bytes": 5485.515349444808, "num_examples": 11}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], 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"splits": [{"name": "train", "num_bytes": 161000625, "num_examples": 99842}], "download_size": 47518592, "dataset_size": 161000625}, {"config_name": "business_ethics", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", "num_bytes": 49618.6654322746, "num_examples": 100}, {"name": "validation", "num_bytes": 5485.515349444808, "num_examples": 11}, {"name": "dev", "num_bytes": 2199.1754385964914, "num_examples": 5}], "download_size": 31619, "dataset_size": 57303.3562203159}, {"config_name": "clinical_knowledge", "features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "test", 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Dataset Card for MMLU
Dataset Summary
Measuring Massive Multitask Language Understanding by Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021).
This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge. The test spans subjects in the humanities, social sciences, hard sciences, and other areas that are important for some people to learn. This covers 57… See the full description on the dataset page: https://huggingface.co/datasets/cais/mmlu. | 144,320 | 37,161,595 | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
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"library:polars",
"arxiv:2009.03300",
"arxiv:2005.00700",
"arxiv:2005.14165",
"arxiv:2008.02275",
"region:us"
] | 2022-03-02T23:29:22 | mmlu | null |
67a404bc8c6d42c5ec097433 | Anthropic/EconomicIndex | Anthropic | {"license": "mit", "pretty_name": "EconomicIndex", "tags": ["text"], "viewer": true, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "onet_task_mappings.csv"}]}]} | false | null | 2025-02-10T19:28:32 | 199 | 8 | false | 218b35116baa43c55beffe61f243bd81f5f84cf8 |
Overview
This directory contains O*NET task mapping and automation vs. augmentation data from "Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations." The data and provided analysis are described below.
Please see our blog post and paper for further visualizations and complete analysis.
Data
SOC_Structure.csv - Standard Occupational Classification (SOC) system hierarchy from the U.S. Department of Labor O*NET database… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/EconomicIndex. | 2,831 | 8,085 | [
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"text"
] | 2025-02-06T00:39:24 | null | null |
67bc34742281367a6b4a5bb7 | jmhb/microvqa | jmhb | {"language": ["en"], "license": "cc-by-sa-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["visual-question-answering", "multiple-choice"], "pretty_name": "MicroVQA", "dataset_info": {"features": [{"name": "key_question", "dtype": "int64"}, {"name": "key_image", "dtype": "int64"}, {"name": "images_list", "sequence": "image"}, {"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "correct_index", "dtype": "int64"}, {"name": "correct_answer", "dtype": "string"}, {"name": "question_0", "dtype": "string"}, {"name": "answer_0", "dtype": "string"}, {"name": "comments_0", "dtype": "string"}, {"name": "incorrect_answer_0", "dtype": "string"}, {"name": "question_1", "dtype": "string"}, {"name": "choices_1", "sequence": "string"}, {"name": "correct_index_1", "dtype": "int64"}, {"name": "question_2", "dtype": "string"}, {"name": "choices_2", "sequence": "string"}, {"name": "correct_index_2", "dtype": "int64"}, {"name": "question_3", "dtype": "string"}, {"name": "choices_3", "sequence": "string"}, {"name": "correct_index_3", "dtype": "int64"}, {"name": "task", "dtype": "int64"}, {"name": "task_str", "dtype": "string"}, {"name": "context_image_generation", "dtype": "string"}, {"name": "context_motivation", "dtype": "string"}, {"name": "images_source", "dtype": "string"}, {"name": "image_caption", "dtype": "string"}, {"name": "key_person", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2354666072.188, "num_examples": 1042}], "download_size": 462156805, "dataset_size": 2354666072.188}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["biology", "biomedical", "microscopy", "pathology", "vision-language", "question-answering", "scientific-research"]} | false | null | 2025-03-18T17:04:39 | 11 | 8 | false | e50938e2fd7299310896df749f74b5f6cc528ca5 | MicroVQA: A Multimodal Reasoning Benchmark for Microscopy-Based Scientific Research (CVPR 2025)
🌐 Homepage / blog •
📝 arXiv •
🤗 HF Dataset •
💻 Code •
🏛 CC-BY-SA-4.0
MicroVQA is expert-curated benchmark for multimodal reasoning for microscopy-based scientific research, proposed in the paper MicroVQA: A Multimodal Reasoning Benchmark for Microscopy-Based Scientific Research.
Paper abstract
Scientific research demands sophisticated reasoning over multimodal… See the full description on the dataset page: https://huggingface.co/datasets/jmhb/microvqa. | 798 | 798 | [
"task_categories:visual-question-answering",
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"arxiv:2503.13399",
"region:us",
"biology",
"biomedical",
"microscopy",
"pathology",
"vision-language",
"question-answering",
"scientific-research"
] | 2025-02-24T08:57:24 | null | null |
67cbdbee416daf2ed9475ea4 | SmallDoge/SmallThoughts | SmallDoge | {"dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "system_prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 207497599, "num_examples": 50000}, {"name": "test", "num_bytes": 4533192, "num_examples": 1000}], "download_size": 82841801, "dataset_size": 212030791}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "language": ["en"], "tags": ["biology", "code", "chemistry", "synthetic"], "size_categories": ["10K<n<100K"]} | false | null | 2025-03-14T13:21:53 | 44 | 8 | false | e7b425e9e659c3827af4c89cbe10e080fea3f038 |
SmallThoughts
Open synthetic reasoning dataset, covering math, science, code, and puzzles.
To address the issue of the existing DeepSeek R1 distilled data being too long, this dataset constrains the reasoning trajectory to be more precise and concise while retaining the reflective nature.
We also open-sourced the pipeline code for distilled data here, with just one command you can generate your own dataset.
How to use
You can load… See the full description on the dataset page: https://huggingface.co/datasets/SmallDoge/SmallThoughts. | 4,247 | 4,247 | [
"task_categories:question-answering",
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"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"biology",
"code",
"chemistry",
"synthetic"
] | 2025-03-08T05:55:58 | null | null |
67cf999e2d0993b445bfd594 | Gen-Verse/WideRange4D | Gen-Verse | {"task_categories": ["image-to-video"], "tags": ["4d-reconstruction", "gaussian-splatting"], "license": "unknown"} | false | null | 2025-03-24T10:30:38 | 9 | 8 | false | a7b22362610394ac5a6adb6bc67b94cdf2589a85 |
WideRange4D: Enabling High-Quality 4D Reconstruction with Wide-Range Movements and Scenes
Ling Yang1*, Kaixin Zhu1*, Juanxi Tian1*, Bohan Zeng1*, Mingbao Lin3, Hongjuan Pei2, Wentao Zhang1‡, Shuicheng Yan3‡
1 Peking University 2 University of the Chinese Academy of Sciences 3 National University of Singapore
* Equal Contributions. ‡ Corresponding Author.
Github Page
arXiv Paper
Example
@article{yang2025widerange4d,
title={WideRange4D: Enabling… See the full description on the dataset page: https://huggingface.co/datasets/Gen-Verse/WideRange4D. | 9,888 | 9,888 | [
"task_categories:image-to-video",
"license:unknown",
"size_categories:10K<n<100K",
"modality:video",
"library:datasets",
"library:mlcroissant",
"arxiv:2503.13435",
"region:us",
"4d-reconstruction",
"gaussian-splatting"
] | 2025-03-11T02:02:06 | null | null |
67d3331f02ca9341dcb6b5be | nvidia/PhysicalAI-SmartSpaces | nvidia | null | false | null | 2025-03-19T06:53:43 | 8 | 8 | false | 55cf1bbb586f889274787ccc436a11debb2fecde |
Physical AI Smart Spaces Dataset
Overview
Comprehensive, annotated dataset for multi-camera tracking and 2D/3D object detection. This dataset is synthetically generated with Omniverse.
This dataset consists of over 250 hours of video from across nearly 1,500 cameras from indoor scenes in warehouses, hospitals, retail, and more. The dataset is time synchronized for tracking humans across multiple cameras using feature representation and no personal data.… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-SmartSpaces. | 1,962 | 1,962 | [
"arxiv:2404.09432",
"arxiv:2412.00692",
"region:us"
] | 2025-03-13T19:33:51 | null | null |
6564cf8ec9611f7e11423ff4 | b3x0m/Chinese-H-Novels | b3x0m | {"language": ["zh"], "size_categories": ["1B<n<10B"], "task_categories": ["text-classification", "summarization", "token-classification", "text2text-generation", "question-answering", "text-generation", "fill-mask", "sentence-similarity"], "pretty_name": "H-novel-corpus", "tags": ["art"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 95784400372, "num_examples": 934354429}], "download_size": 60873072258, "dataset_size": 95784400372}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2024-07-12T02:32:57 | 200 | 7 | false | 16258fb735f019d2d0100960ec739b6dabc3db77 | Update 12/07/2024: convert to parquet to download easier.
Chinese 18+ novels corpus, use at your own risk, you and only you are responsible for every choice you make.
(͡ ° ͜ʖ ͡ °)
tags: socks, garter belt, foot fetish, ntr, netori.....
Thanks Moleys/Numeron for the dataset donation.
| 2,598 | 8,242 | [
"task_categories:text-classification",
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"art"
] | 2023-11-27T17:19:10 | null | null |
660e7b9b4636ce2b0e77b699 | mozilla-foundation/common_voice_17_0 | mozilla-foundation | {"pretty_name": "Common Voice Corpus 17.0", "annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["ab", "af", "am", "ar", "as", "ast", "az", "ba", "bas", "be", "bg", "bn", "br", "ca", "ckb", "cnh", "cs", "cv", "cy", "da", "de", "dv", "dyu", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gl", "gn", "ha", "he", "hi", "hsb", "ht", "hu", "hy", "ia", "id", "ig", "is", "it", "ja", "ka", "kab", "kk", "kmr", "ko", "ky", "lg", "lij", "lo", "lt", "ltg", "lv", "mdf", "mhr", "mk", "ml", "mn", "mr", "mrj", "mt", "myv", "nan", "ne", "nhi", "nl", "nn", "nso", "oc", "or", "os", "pa", "pl", "ps", "pt", "quy", "rm", "ro", "ru", "rw", "sah", "sat", "sc", "sk", "skr", "sl", "sq", "sr", "sv", "sw", "ta", "te", "th", "ti", "tig", "tk", "tok", "tr", "tt", "tw", "ug", "uk", "ur", "uz", "vi", "vot", "yi", "yo", "yue", "zgh", "zh", "zu", "zza"], "language_bcp47": ["zh-CN", "zh-HK", "zh-TW", "sv-SE", "rm-sursilv", "rm-vallader", "pa-IN", "nn-NO", "ne-NP", "nan-tw", "hy-AM", "ga-IE", "fy-NL"], "license": ["cc0-1.0"], "multilinguality": ["multilingual"], "source_datasets": ["extended|common_voice"], "paperswithcode_id": "common-voice", "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."} | false | null | 2024-06-16T13:50:23 | 239 | 7 | false | b10d53980ef166bc24ce3358471c1970d7e6b5ec |
Dataset Card for Common Voice Corpus 17.0
Dataset Summary
The Common Voice dataset consists of a unique MP3 and corresponding text file.
Many of the 31175 recorded hours in the dataset also include demographic metadata like age, sex, and accent
that can help improve the accuracy of speech recognition engines.
The dataset currently consists of 20408 validated hours in 124 languages, but more voices and languages are always added.
Take a look at the Languages page to… See the full description on the dataset page: https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0. | 36,256 | 442,172 | [
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"library:datasets",
"library:mlcroissant",
"arxiv:1912.06670",
"region:us"
] | 2024-04-04T10:06:19 | common-voice | @inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
} |
6664c00380c533842fb0c680 | lmg-anon/vntl-leaderboard | lmg-anon | {"language": ["en", "ja"], "tags": ["benchmark", "leaderboard"], "task_categories": ["translation"], "pretty_name": "vntl-leaderboard", "size_categories": ["n<1K"], "configs": [{"config_name": "leaderboard", "data_files": "leaderboard.jsonl"}]} | false | null | 2025-01-02T16:34:32 | 34 | 7 | false | cf3d232d77458394857dbf8411de95fd3a894aef |
VNTL Leaderboard
The VNTL leaderboard ranks Large Language Models (LLMs) based on their performance in translating Japanese Visual Novels into English. Please be aware that the current results are preliminary and subject to change as new models are evaluated, or changes are done in the evaluation script.
Comparison with Established Translation Tools
For comparison, this table shows the scores for established translation tools. These include both widely available… See the full description on the dataset page: https://huggingface.co/datasets/lmg-anon/vntl-leaderboard. | 3,297 | 8,378 | [
"task_categories:translation",
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"library:mlcroissant",
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"benchmark",
"leaderboard"
] | 2024-06-08T20:33:07 | null | null |
66c84764a47b2d6c582bbb02 | amphion/Emilia-Dataset | amphion | {"license": "cc-by-4.0", "task_categories": ["text-to-speech", "automatic-speech-recognition"], "language": ["zh", "en", "ja", "fr", "de", "ko"], "pretty_name": "Emilia", "size_categories": ["10M<n<100M"], "extra_gated_prompt": "Terms of Access: The researcher has requested permission to use the Emilia dataset, the Emilia-Pipe preprocessing pipeline, and the Emilia-Yodas dataset. In exchange for such permission, the researcher hereby agrees to the following terms and conditions:\n1. The researcher shall use the Emilia dataset under the CC-BY-NC license and\n the Emilia-YODAS dataset under the CC-BY license.\n2. The authors make no representations or warranties regarding the datasets,\n including but not limited to warranties of non-infringement or fitness for\n a particular purpose.\n3. The researcher accepts full responsibility for their use of the datasets and\n shall defend and indemnify the authors of Emilia, Emilia-Pipe, and\n Emilia-Yodas, including their employees, trustees, officers, and agents,\n against any and all claims arising from the researcher's use of the datasets,\n including but not limited to the researcher's use of any copies of copyrighted\n content that they may create from the datasets.\n4. The researcher may provide research associates and colleagues with access\n to the datasets, provided that they first agree to be bound by these terms\n and conditions.\n5. The authors reserve the right to terminate the researcher's access to the\n datasets at any time.\n6. If the researcher is employed by a for-profit, commercial entity, the\n researcher's employer shall also be bound by these terms and conditions,\n and the researcher hereby represents that they are fully authorized to enter\n into this agreement on behalf of such employer.", "extra_gated_fields": {"Name": "text", "Email": "text", "Affiliation": "text", "Position": "text", "Your Supervisor/manager/director": "text", "I agree to the Terms of Access": "checkbox"}} | false | null | 2025-02-28T05:41:37 | 276 | 7 | false | d7f2f7340a6385696f3766c8049fa920a4707c07 |
Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation
This is the official repository 👑 for the Emilia dataset and the source code for the Emilia-Pipe speech data preprocessing pipeline.
News 🔥
2025/02/26: The Emilia-Large dataset, featuring over 200,000 hours of data, is now available!!! Emilia-Large combines the original 101k-hour Emilia dataset (licensed under CC BY-NC 4.0) with the brand-new 114k-hour Emilia-YODAS… See the full description on the dataset page: https://huggingface.co/datasets/amphion/Emilia-Dataset. | 128,458 | 338,497 | [
"task_categories:text-to-speech",
"task_categories:automatic-speech-recognition",
"language:zh",
"language:en",
"language:ja",
"language:fr",
"language:de",
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"library:webdataset",
"library:mlcroissant",
"arxiv:2407.05361",
"arxiv:2501.15907",
"region:us"
] | 2024-08-23T08:25:08 | 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 | 661 | 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. | 40,505 | 140,880 | [
"license:apache-2.0",
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"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"curator",
"synthetic"
] | 2025-01-27T20:02:16 | null | null |
67d421a66af8d6a03083eb69 | GeneralReasoning/GeneralThought-430K | GeneralReasoning | {"language": ["en"], "license": "mit"} | false | null | 2025-03-14T13:04:04 | 21 | 7 | false | 9f2b46abdf8e3ba2faf650541242d4bd8ac22892 |
GeneralThought-430K
Thought wants to be free
Open reasoning data from the General Reasoning resource for March 14 2025.
The dataset contains questions, reference answers, reasoning traces, final answers and other metadata from several popular reasoning models including DeepSeek-R1, DeepSeek-R1-Zero, OpenThoughts-32B, LIMO, deepseek-r1-distill-llama-70b, DeepHermes-3-Llama-3-8B-Previewand DeepScaleR-1.5B-Preview. We also include final answers from o3-mini-2025-01-31… See the full description on the dataset page: https://huggingface.co/datasets/GeneralReasoning/GeneralThought-430K. | 1,116 | 1,116 | [
"language:en",
"license:mit",
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"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-14T12:31:34 | null | null |
67d9fd082ad0bffeb5bbc771 | HuggingFaceTB/issues-kaggle-notebooks | HuggingFaceTB | {"dataset_info": [{"config_name": "issues", "features": [{"name": "repo_name", "dtype": "string"}, {"name": "issue_id", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 30986711842, "num_examples": 15549682}], "download_size": 16370074732, "dataset_size": 30986711842}, {"config_name": "kaggle", "features": [{"name": "file_id", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5209133899, "num_examples": 580195}], "download_size": 2222724371, "dataset_size": 5209133899}], "configs": [{"config_name": "issues", "data_files": [{"split": "train", "path": "issues/train-*"}]}, {"config_name": "kaggle", "data_files": [{"split": "train", "path": "kaggle/train-*"}]}]} | false | null | 2025-03-19T20:00:18 | 7 | 7 | false | ef882ad1ed8274340e8fc9bac087c903f2f75396 |
GitHub Issues & Kaggle Notebooks
Description
GitHub Issues & Kaggle Notebooks is a collection of two code datasets intended for language models training, they are sourced from GitHub issues and notebooks in Kaggle platform. These datasets are a modified part of the StarCoder2 model training corpus, precisely the bigcode/StarCoder2-Extras dataset. We reformat the samples to remove StarCoder2's special tokens and use natural text to delimit comments in issues and display… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/issues-kaggle-notebooks. | 105 | 105 | [
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2402.19173",
"region:us"
] | 2025-03-18T23:08:56 | null | null |
621ffdd236468d709f182a80 | allenai/c4 | allenai | {"pretty_name": "C4", "annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["af", "am", "ar", "az", "be", "bg", "bn", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "he", "hi", "hmn", "ht", "hu", "hy", "id", "ig", "is", "it", "iw", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lb", "lo", "lt", "lv", "mg", "mi", "mk", "ml", "mn", "mr", "ms", "mt", "my", "ne", "nl", "no", "ny", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "sm", "sn", "so", "sq", "sr", "st", "su", "sv", "sw", "ta", "te", "tg", "th", "tr", "uk", "und", "ur", "uz", "vi", "xh", "yi", "yo", "zh", "zu"], "language_bcp47": ["bg-Latn", "el-Latn", "hi-Latn", "ja-Latn", "ru-Latn", "zh-Latn"], "license": ["odc-by"], "multilinguality": ["multilingual"], "size_categories": ["n<1K", "1K<n<10K", "10K<n<100K", "100K<n<1M", 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{"config_name": "ne", "data_files": [{"split": "train", "path": "multilingual/c4-ne.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-ne-validation.*.json.gz"}]}, {"config_name": "nl", "data_files": [{"split": "train", "path": "multilingual/c4-nl.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-nl-validation.*.json.gz"}]}, {"config_name": "no", "data_files": [{"split": "train", "path": "multilingual/c4-no.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-no-validation.*.json.gz"}]}, {"config_name": "ny", "data_files": [{"split": "train", "path": "multilingual/c4-ny.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-ny-validation.*.json.gz"}]}, {"config_name": "pa", "data_files": [{"split": "train", "path": "multilingual/c4-pa.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-pa-validation.*.json.gz"}]}, {"config_name": "pl", "data_files": [{"split": "train", "path": "multilingual/c4-pl.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-pl-validation.*.json.gz"}]}, {"config_name": "ps", "data_files": [{"split": "train", "path": "multilingual/c4-ps.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-ps-validation.*.json.gz"}]}, {"config_name": "pt", "data_files": [{"split": "train", "path": "multilingual/c4-pt.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-pt-validation.*.json.gz"}]}, {"config_name": "ro", "data_files": [{"split": "train", "path": "multilingual/c4-ro.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-ro-validation.*.json.gz"}]}, {"config_name": "ru", "data_files": [{"split": "train", "path": "multilingual/c4-ru.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-ru-validation.*.json.gz"}]}, {"config_name": "ru-Latn", "data_files": [{"split": "train", "path": "multilingual/c4-ru-Latn.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-ru-Latn-validation.*.json.gz"}]}, {"config_name": "sd", "data_files": [{"split": "train", "path": "multilingual/c4-sd.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sd-validation.*.json.gz"}]}, {"config_name": "si", "data_files": [{"split": "train", "path": "multilingual/c4-si.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-si-validation.*.json.gz"}]}, {"config_name": "sk", "data_files": [{"split": "train", "path": "multilingual/c4-sk.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sk-validation.*.json.gz"}]}, {"config_name": "sl", "data_files": [{"split": "train", "path": "multilingual/c4-sl.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sl-validation.*.json.gz"}]}, {"config_name": "sm", "data_files": [{"split": "train", "path": "multilingual/c4-sm.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sm-validation.*.json.gz"}]}, {"config_name": "sn", "data_files": [{"split": "train", "path": "multilingual/c4-sn.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sn-validation.*.json.gz"}]}, {"config_name": "so", "data_files": [{"split": "train", "path": "multilingual/c4-so.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-so-validation.*.json.gz"}]}, {"config_name": "sq", "data_files": [{"split": "train", "path": "multilingual/c4-sq.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sq-validation.*.json.gz"}]}, {"config_name": "sr", "data_files": [{"split": "train", "path": "multilingual/c4-sr.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sr-validation.*.json.gz"}]}, {"config_name": "st", "data_files": [{"split": "train", "path": "multilingual/c4-st.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-st-validation.*.json.gz"}]}, {"config_name": "su", "data_files": [{"split": "train", "path": "multilingual/c4-su.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-su-validation.*.json.gz"}]}, {"config_name": "sv", "data_files": [{"split": "train", "path": "multilingual/c4-sv.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sv-validation.*.json.gz"}]}, {"config_name": "sw", "data_files": [{"split": "train", "path": "multilingual/c4-sw.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-sw-validation.*.json.gz"}]}, {"config_name": "ta", "data_files": [{"split": "train", "path": "multilingual/c4-ta.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-ta-validation.*.json.gz"}]}, {"config_name": "te", "data_files": [{"split": "train", "path": "multilingual/c4-te.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-te-validation.*.json.gz"}]}, {"config_name": "tg", "data_files": [{"split": "train", "path": "multilingual/c4-tg.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-tg-validation.*.json.gz"}]}, {"config_name": "th", "data_files": [{"split": "train", "path": "multilingual/c4-th.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-th-validation.*.json.gz"}]}, {"config_name": "tr", "data_files": [{"split": "train", "path": "multilingual/c4-tr.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-tr-validation.*.json.gz"}]}, {"config_name": "uk", "data_files": [{"split": "train", "path": "multilingual/c4-uk.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-uk-validation.*.json.gz"}]}, {"config_name": "und", "data_files": [{"split": "train", "path": "multilingual/c4-und.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-und-validation.*.json.gz"}]}, {"config_name": "ur", "data_files": [{"split": "train", "path": "multilingual/c4-ur.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-ur-validation.*.json.gz"}]}, {"config_name": "uz", "data_files": [{"split": "train", "path": "multilingual/c4-uz.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-uz-validation.*.json.gz"}]}, {"config_name": "vi", "data_files": [{"split": "train", "path": "multilingual/c4-vi.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-vi-validation.*.json.gz"}]}, {"config_name": "xh", "data_files": [{"split": "train", "path": "multilingual/c4-xh.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-xh-validation.*.json.gz"}]}, {"config_name": "yi", "data_files": [{"split": "train", "path": "multilingual/c4-yi.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-yi-validation.*.json.gz"}]}, {"config_name": "yo", "data_files": [{"split": "train", "path": "multilingual/c4-yo.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-yo-validation.*.json.gz"}]}, {"config_name": "zh", "data_files": [{"split": "train", "path": "multilingual/c4-zh.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-zh-validation.*.json.gz"}]}, {"config_name": "zh-Latn", "data_files": [{"split": "train", "path": "multilingual/c4-zh-Latn.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-zh-Latn-validation.*.json.gz"}]}, {"config_name": "zu", "data_files": [{"split": "train", "path": "multilingual/c4-zu.*.json.gz"}, {"split": "validation", "path": "multilingual/c4-zu-validation.*.json.gz"}]}]} | false | null | 2024-01-09T19:14:03 | 390 | 6 | false | 1588ec454efa1a09f29cd18ddd04fe05fc8653a2 |
C4
Dataset Summary
A colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: "https://commoncrawl.org".
This is the processed version of Google's C4 dataset
We prepared five variants of the data: en, en.noclean, en.noblocklist, realnewslike, and multilingual (mC4).
For reference, these are the sizes of the variants:
en: 305GB
en.noclean: 2.3TB
en.noblocklist: 380GB
realnewslike: 15GB
multilingual (mC4): 9.7TB (108 subsets, one… See the full description on the dataset page: https://huggingface.co/datasets/allenai/c4. | 411,910 | 5,355,225 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:multilingual",
"source_datasets:original",
"language:af",
"language:am",
"language:ar",
"language:az",
"language:be",
"language:bg",
"language:bn",
"language:ca",
"language:ceb",
"language:co",
"language:cs",
"language:cy",
"language:da",
"language:de",
"language:el",
"language:en",
"language:eo",
"language:es",
"language:et",
"language:eu",
"language:fa",
"language:fi",
"language:fil",
"language:fr",
"language:fy",
"language:ga",
"language:gd",
"language:gl",
"language:gu",
"language:ha",
"language:haw",
"language:he",
"language:hi",
"language:hmn",
"language:ht",
"language:hu",
"language:hy",
"language:id",
"language:ig",
"language:is",
"language:it",
"language:iw",
"language:ja",
"language:jv",
"language:ka",
"language:kk",
"language:km",
"language:kn",
"language:ko",
"language:ku",
"language:ky",
"language:la",
"language:lb",
"language:lo",
"language:lt",
"language:lv",
"language:mg",
"language:mi",
"language:mk",
"language:ml",
"language:mn",
"language:mr",
"language:ms",
"language:mt",
"language:my",
"language:ne",
"language:nl",
"language:no",
"language:ny",
"language:pa",
"language:pl",
"language:ps",
"language:pt",
"language:ro",
"language:ru",
"language:sd",
"language:si",
"language:sk",
"language:sl",
"language:sm",
"language:sn",
"language:so",
"language:sq",
"language:sr",
"language:st",
"language:su",
"language:sv",
"language:sw",
"language:ta",
"language:te",
"language:tg",
"language:th",
"language:tr",
"language:uk",
"language:und",
"language:ur",
"language:uz",
"language:vi",
"language:xh",
"language:yi",
"language:yo",
"language:zh",
"language:zu",
"license:odc-by",
"size_categories:10B<n<100B",
"modality:text",
"arxiv:1910.10683",
"region:us"
] | 2022-03-02T23:29:22 | c4 | null |
647de2bd5214d172cbb8541e | ccmusic-database/timbre_range | ccmusic-database | {"license": "mit", "task_categories": ["audio-classification"], "language": ["zh", "en"], "tags": ["music", "art"], "pretty_name": "Timbre and Range Dataset", "size_categories": ["1K<n<10K"], "dataset_info": [{"config_name": "timbre", "features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 44100}}}, {"name": "mel", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Base", "1": "Split", "2": "Short"}}}}, {"name": "score1", "dtype": "float64"}, {"name": "score2", "dtype": "float64"}, {"name": "avg_score", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 213644, "num_examples": 537}, {"name": "validation", "num_bytes": 26664, "num_examples": 67}, {"name": "test", "num_bytes": 27088, "num_examples": 68}], "download_size": 595425921, "dataset_size": 267396}, {"config_name": "range", "features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 44100}}}, {"name": "mel", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Narrow", "1": "Moderate", "2": "Wide"}}}}], "splits": [{"name": "train", "num_bytes": 210052, "num_examples": 580}, {"name": "validation", "num_bytes": 26462, "num_examples": 73}, {"name": "test", "num_bytes": 26400, "num_examples": 73}], "download_size": 65309164, "dataset_size": 262914}], "configs": [{"config_name": "timbre", "data_files": [{"split": "train", "path": "timbre/train/data-*.arrow"}, {"split": "validation", "path": "timbre/validation/data-*.arrow"}, {"split": "test", "path": "timbre/test/data-*.arrow"}]}, {"config_name": "range", "data_files": [{"split": "train", "path": "range/train/data-*.arrow"}, {"split": "validation", "path": "range/validation/data-*.arrow"}, {"split": "test", "path": "range/test/data-*.arrow"}]}]} | false | null | 2025-02-16T03:24:49 | 9 | 6 | false | 242afee6bc5d2361e9afa0e4d57daa5a9ec9799e |
Dataset Card for Timbre and Range Dataset
Dataset Summary
The timbre dataset contains acapella singing audio of 9 singers, as well as cut single-note audio, totaling 775 clips (.wav format)
The vocal range dataset includes several up and down chromatic scales audio clips of several vocals, as well as the cut single-note audio clips (.wav format).
Supported Tasks and Leaderboards
Audio classification
Languages
Chinese, English
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/ccmusic-database/timbre_range. | 857 | 1,030 | [
"task_categories:audio-classification",
"language:zh",
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"format:arrow",
"modality:audio",
"modality:image",
"library:datasets",
"library:mlcroissant",
"region:us",
"music",
"art"
] | 2023-06-05T13:27:25 | null | null |
66299f1f4f9d8e75f2a8a6b0 | simon3000/genshin-voice | simon3000 | {"language": ["zh", "en", "ja", "ko"], "task_categories": ["audio-classification", "automatic-speech-recognition", "text-to-speech"], "pretty_name": "Genshin Voice", "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "transcription", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "speaker", "dtype": "string"}, {"name": "speaker_type", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "inGameFilename", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 264598217401.752, "num_examples": 463383}], "download_size": 227704444125, "dataset_size": 264598217401.752}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2024-08-30T08:36:05 | 89 | 6 | false | ffe17e2e7938508a73255ec294b0ded17ed071f1 |
Genshin Voice
Genshin Voice is a dataset of voice lines from the popular game Genshin Impact.
Hugging Face 🤗 Genshin-Voice
Last update at 2024-08-30
463383 wavs
20231 without speaker (4%)
24819 without transcription (5%)
602 without inGameFilename (0%)
Dataset Details
Dataset Description
The dataset contains voice lines from the game's characters in multiple languages, including Chinese, English, Japanese, and Korean.
The voice lines are spoken… See the full description on the dataset page: https://huggingface.co/datasets/simon3000/genshin-voice. | 5,084 | 23,423 | [
"task_categories:audio-classification",
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"language:zh",
"language:en",
"language:ja",
"language:ko",
"size_categories:100K<n<1M",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-04-25T00:09:03 | null | null |
6763bd205297513b0f262714 | unitreerobotics/LAFAN1_Retargeting_Dataset | unitreerobotics | {"task_categories": ["robotics"]} | false | null | 2025-02-08T09:17:08 | 58 | 6 | false | f5d470a1583ab9a1592a4f56d104ad01c363009f |
LAFAN1 Retargeting Dataset
To make the motion of humanoid robots more natural, we retargeted LAFAN1 motion capture data to Unitree's humanoid robots, supporting three models: H1, H1_2, and G1. This retargeting was achieved through numerical optimization based on Interaction Mesh and IK, considering end-effector pose constraints, as well as joint position and velocity constraints, to prevent foot slippage. It is important to note that the retargeting only accounted for kinematic… See the full description on the dataset page: https://huggingface.co/datasets/unitreerobotics/LAFAN1_Retargeting_Dataset. | 923 | 2,141 | [
"task_categories:robotics",
"modality:3d",
"region:us"
] | 2024-12-19T06:28:48 | null | null |
67aa648e91e6f5eb545e854e | allenai/olmOCR-mix-0225 | allenai | {"license": "odc-by", "configs": [{"config_name": "00_documents", "data_files": [{"split": "train_s2pdf", "path": ["train-s2pdf.parquet"]}, {"split": "eval_s2pdf", "path": ["eval-s2pdf.parquet"]}]}, {"config_name": "01_books", "data_files": [{"split": "train_iabooks", "path": ["train-iabooks.parquet"]}, {"split": "eval_iabooks", "path": ["eval-iabooks.parquet"]}]}]} | false | null | 2025-02-25T09:36:14 | 98 | 6 | false | a602926844ed47c43439627fd16d3de45b39e494 |
olmOCR-mix-0225
olmOCR-mix-0225 is a dataset of ~250,000 PDF pages which have been OCRed into plain-text in a natural reading order using gpt-4o-2024-08-06 and a special
prompting strategy that preserves any born-digital content from each page.
This dataset can be used to train, fine-tune, or evaluate your own OCR document pipeline.
Quick links:
📃 Paper
🤗 Model
🛠️ Code
🎮 Demo
Data Mix
Table 1: Training set composition by source
Source
Unique… See the full description on the dataset page: https://huggingface.co/datasets/allenai/olmOCR-mix-0225. | 5,473 | 5,475 | [
"license:odc-by",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-02-10T20:41:50 | null | null |
67d759e251343ff3a8db190b | kyutai/Babillage | kyutai | {"license": "cc-by-4.0", "task_categories": ["visual-question-answering"], "language": ["en"], "modalities": ["text", "speech", "images"], "pretty_name": "Babillage: A benchmark for Vision Speech Models", "size_categories": ["100K<n<1M"], "configs": [{"config_name": "coco", "data_files": [{"split": "validation", "path": "coco/validation-*"}, {"split": "test", "path": "coco/test-*"}]}, {"config_name": "ocrvqa", "data_files": [{"split": "validation", "path": "ocrvqa/validation-*"}, {"split": "test", "path": "ocrvqa/test-*"}]}, {"config_name": "vqav2", "data_files": [{"split": "validation", "path": "vqav2/validation-*"}]}], "dataset_info": [{"config_name": "coco", "features": [{"name": "sample_id", "dtype": "int64"}, {"name": "image_id", "dtype": "int64"}, {"name": "question_audio", "dtype": "audio"}, {"name": "question_transcript", "sequence": "string"}, {"name": "question_alignment", "sequence": {"sequence": "float64"}}, {"name": "answer_audio", "dtype": "audio"}, {"name": "answer_transcript", "sequence": "string"}, {"name": "answer_alignment", "sequence": {"sequence": "float64"}}], "splits": [{"name": "validation", "num_bytes": 1898180096.65, "num_examples": 25010}, {"name": "test", "num_bytes": 1891287801.34, "num_examples": 25010}], "download_size": 3758302082, "dataset_size": 3789467897.99}, {"config_name": "ocrvqa", "features": [{"name": "sample_id", "dtype": "int64"}, {"name": "image_id", "dtype": "int64"}, {"name": "question_audio", "dtype": "audio"}, {"name": "question_transcript", "sequence": "string"}, {"name": "question_alignment", "sequence": {"sequence": "float64"}}, {"name": "answer_audio", "dtype": "audio"}, {"name": "answer_transcript", "sequence": "string"}, {"name": "answer_alignment", "sequence": {"sequence": "float64"}}], "splits": [{"name": "validation", "num_bytes": 6338684773.64, "num_examples": 100032}, {"name": "test", "num_bytes": 6437178649.248, "num_examples": 100424}], "download_size": 12653608704, "dataset_size": 12775863422.888}, {"config_name": "vqav2", "features": [{"name": "sample_id", "dtype": "int64"}, {"name": "question_audio", "dtype": "audio"}, {"name": "question_transcript", "sequence": "string"}, {"name": "question_alignment", "sequence": {"sequence": "float64"}}], "splits": [{"name": "validation", "num_bytes": 4815870675.172, "num_examples": 214354}], "download_size": 4775599520, "dataset_size": 4815870675.172}]} | false | null | 2025-03-21T11:47:30 | 6 | 6 | false | 3ede8566da87763fffe3e0a1d9b78cd76bf5b85d |
Babillage
Babillage is a multimodal benchmark dataset introduced along with MoshiVis (Project Page | arXiv), containing three common vision-language benchmarks converted in spoken form, for the evaluation of Vision Speech Models.
For each benchmark (COCO-Captions, OCR-VQA, VQAv2), we first reformat the text question-answer pairs into a more conversational dialogue, and then convert them using a text-to-speech pipeline, using a
consistent synthetic voice for the answer (assistant)… See the full description on the dataset page: https://huggingface.co/datasets/kyutai/Babillage. | 76 | 76 | [
"task_categories:visual-question-answering",
"language:en",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2503.15633",
"region:us"
] | 2025-03-16T23:08:18 | null | null |
67d930020983992037e692ea | showlab/ImpossibleVideos | showlab | {"language": ["en"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["text-to-video", "video-text-to-text"]} | false | null | 2025-03-21T05:13:15 | 6 | 6 | false | ab2beefcf9a0f3128c90f564b6c4ef93bf889d57 |
Impossible Videos
Zechen Bai *
Hai Ci *
Mike Zheng Shou
Show Lab, National University of Singapore
🤔 What are impossible videos?
Impossible videos refer to videos displaying counterfactual and anti-realityscenes that are impossible in real world.
Please visit our website to find more examples.
💡 Why we interested in impossible videos?
Impossible videos can be a touch stone for advanced video models.
As an out-of-real-world-distribution data, it… See the full description on the dataset page: https://huggingface.co/datasets/showlab/ImpossibleVideos. | 292 | 292 | [
"task_categories:text-to-video",
"task_categories:video-text-to-text",
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"arxiv:2503.14378",
"region:us"
] | 2025-03-18T08:34:10 | null | null |
621ffdd236468d709f181f09 | Skylion007/openwebtext | Skylion007 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": ["cc0-1.0"], "multilinguality": ["monolingual"], "pretty_name": "OpenWebText", "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "paperswithcode_id": "openwebtext", "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "config_name": "plain_text", "splits": [{"name": "train", "num_bytes": 39769491688, "num_examples": 8013769}], "download_size": 12880189440, "dataset_size": 39769491688}} | false | null | 2024-05-17T17:56:27 | 409 | 5 | false | f3808c30e817981b845ec549c43e82bb467d8144 | An open-source replication of the WebText dataset from OpenAI. | 89,474 | 4,502,769 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"size_categories:1M<n<10M",
"region:us"
] | 2022-03-02T23:29:22 | openwebtext | @misc{Gokaslan2019OpenWeb,
title={OpenWebText Corpus},
author={Aaron Gokaslan*, Vanya Cohen*, Ellie Pavlick, Stefanie Tellex},
howpublished{\\url{http://Skylion007.github.io/OpenWebTextCorpus}},
year={2019}
} |
627007d3becab9e2dcf15a40 | ILSVRC/imagenet-1k | ILSVRC | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["other"], "license_details": "imagenet-agreement", "multilinguality": ["monolingual"], "paperswithcode_id": "imagenet-1k-1", "pretty_name": "ImageNet", "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["image-classification"], "task_ids": ["multi-class-image-classification"], "extra_gated_prompt": "By clicking on \u201cAccess repository\u201d below, you also agree to ImageNet Terms of Access:\n[RESEARCHER_FULLNAME] (the \"Researcher\") has requested permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n1. Researcher shall use the Database only for non-commercial research and educational purposes.\n2. Princeton University, Stanford University and Hugging Face make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.\n3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, Stanford University and Hugging Face, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database.\n4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.\n5. Princeton University, Stanford University and Hugging Face reserve the right to terminate Researcher's access to the Database at any time.\n6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.\n7. The law of the State of New Jersey shall apply to all disputes under this agreement.", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "tench, Tinca tinca", "1": "goldfish, Carassius auratus", "2": "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias", "3": "tiger shark, Galeocerdo cuvieri", "4": "hammerhead, hammerhead shark", "5": "electric ray, crampfish, numbfish, torpedo", "6": "stingray", "7": "cock", "8": "hen", "9": "ostrich, Struthio camelus", "10": "brambling, Fringilla montifringilla", "11": "goldfinch, Carduelis carduelis", "12": "house finch, linnet, Carpodacus mexicanus", "13": "junco, snowbird", "14": "indigo bunting, indigo finch, indigo bird, Passerina cyanea", "15": "robin, American robin, Turdus migratorius", "16": "bulbul", "17": "jay", "18": "magpie", "19": "chickadee", "20": "water ouzel, dipper", "21": "kite", "22": "bald eagle, American eagle, Haliaeetus leucocephalus", "23": "vulture", "24": "great grey owl, great gray owl, Strix nebulosa", "25": "European fire salamander, Salamandra salamandra", "26": "common newt, Triturus vulgaris", "27": "eft", "28": "spotted salamander, Ambystoma maculatum", "29": "axolotl, mud puppy, Ambystoma mexicanum", "30": "bullfrog, Rana catesbeiana", "31": "tree frog, tree-frog", "32": "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui", "33": "loggerhead, loggerhead turtle, Caretta caretta", "34": "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea", "35": "mud turtle", "36": "terrapin", "37": "box turtle, box tortoise", "38": "banded gecko", "39": "common iguana, iguana, Iguana iguana", "40": "American chameleon, anole, Anolis carolinensis", "41": "whiptail, whiptail lizard", "42": "agama", "43": "frilled lizard, Chlamydosaurus kingi", "44": "alligator lizard", "45": "Gila monster, Heloderma suspectum", "46": "green lizard, Lacerta viridis", "47": "African chameleon, Chamaeleo chamaeleon", "48": "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis", "49": "African crocodile, Nile crocodile, Crocodylus niloticus", "50": "American alligator, Alligator mississipiensis", "51": "triceratops", "52": "thunder snake, worm snake, Carphophis amoenus", "53": "ringneck snake, ring-necked snake, ring snake", "54": "hognose snake, puff adder, sand viper", "55": "green snake, grass snake", "56": "king snake, kingsnake", "57": "garter snake, grass snake", "58": "water snake", "59": "vine snake", "60": "night snake, Hypsiglena torquata", "61": "boa constrictor, Constrictor constrictor", "62": "rock python, rock snake, Python sebae", "63": "Indian cobra, Naja naja", "64": "green mamba", "65": "sea snake", "66": "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus", "67": "diamondback, diamondback rattlesnake, Crotalus adamanteus", "68": "sidewinder, horned rattlesnake, Crotalus cerastes", "69": "trilobite", "70": "harvestman, daddy longlegs, Phalangium opilio", "71": "scorpion", "72": "black and gold garden spider, Argiope aurantia", "73": "barn spider, Araneus cavaticus", "74": "garden spider, Aranea diademata", "75": "black widow, Latrodectus mactans", "76": "tarantula", "77": "wolf spider, hunting spider", "78": "tick", "79": "centipede", "80": "black grouse", "81": "ptarmigan", "82": "ruffed grouse, partridge, Bonasa umbellus", "83": "prairie chicken, prairie grouse, prairie fowl", "84": "peacock", "85": "quail", "86": "partridge", "87": "African grey, African gray, Psittacus erithacus", "88": "macaw", "89": "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita", "90": "lorikeet", "91": "coucal", "92": "bee eater", "93": "hornbill", "94": "hummingbird", "95": "jacamar", "96": "toucan", "97": "drake", "98": "red-breasted merganser, Mergus serrator", "99": "goose", "100": "black swan, Cygnus atratus", "101": "tusker", "102": "echidna, spiny anteater, anteater", "103": "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus", "104": "wallaby, brush kangaroo", "105": "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus", "106": "wombat", "107": "jellyfish", "108": "sea anemone, anemone", "109": "brain coral", "110": "flatworm, platyhelminth", "111": "nematode, nematode worm, roundworm", "112": "conch", "113": "snail", "114": "slug", "115": "sea slug, nudibranch", "116": "chiton, coat-of-mail shell, sea cradle, polyplacophore", "117": "chambered nautilus, pearly nautilus, nautilus", "118": "Dungeness crab, Cancer magister", "119": "rock crab, Cancer irroratus", "120": "fiddler crab", "121": "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica", "122": "American lobster, Northern lobster, Maine lobster, Homarus americanus", "123": "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "124": "crayfish, crawfish, crawdad, crawdaddy", "125": "hermit crab", "126": "isopod", "127": "white stork, Ciconia ciconia", "128": "black stork, Ciconia nigra", "129": "spoonbill", "130": "flamingo", "131": "little blue heron, Egretta caerulea", "132": "American egret, great white heron, Egretta albus", "133": "bittern", "134": "crane", "135": "limpkin, Aramus pictus", "136": "European gallinule, Porphyrio porphyrio", "137": "American coot, marsh hen, mud hen, water hen, Fulica americana", "138": "bustard", "139": "ruddy turnstone, Arenaria interpres", "140": "red-backed sandpiper, dunlin, Erolia alpina", "141": "redshank, Tringa totanus", "142": "dowitcher", "143": "oystercatcher, oyster catcher", "144": "pelican", "145": "king penguin, Aptenodytes patagonica", "146": "albatross, mollymawk", "147": "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus", "148": "killer whale, killer, orca, grampus, sea wolf, Orcinus orca", "149": "dugong, Dugong dugon", "150": "sea lion", "151": "Chihuahua", "152": "Japanese spaniel", "153": "Maltese dog, Maltese terrier, Maltese", "154": "Pekinese, Pekingese, Peke", "155": "Shih-Tzu", "156": "Blenheim spaniel", "157": "papillon", "158": "toy terrier", "159": "Rhodesian ridgeback", "160": "Afghan hound, Afghan", "161": "basset, basset hound", "162": "beagle", "163": "bloodhound, sleuthhound", "164": "bluetick", "165": "black-and-tan coonhound", "166": "Walker hound, Walker foxhound", "167": "English foxhound", "168": "redbone", "169": "borzoi, Russian wolfhound", "170": "Irish wolfhound", "171": "Italian greyhound", "172": "whippet", "173": "Ibizan hound, Ibizan Podenco", "174": "Norwegian elkhound, elkhound", "175": "otterhound, otter hound", "176": "Saluki, gazelle hound", "177": "Scottish deerhound, deerhound", "178": "Weimaraner", "179": "Staffordshire bullterrier, Staffordshire bull terrier", "180": "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier", "181": "Bedlington terrier", "182": "Border terrier", "183": "Kerry blue terrier", "184": "Irish terrier", "185": "Norfolk terrier", "186": "Norwich terrier", "187": "Yorkshire terrier", "188": "wire-haired fox terrier", "189": "Lakeland terrier", "190": "Sealyham terrier, Sealyham", "191": "Airedale, Airedale terrier", "192": "cairn, cairn terrier", "193": "Australian terrier", "194": "Dandie Dinmont, Dandie Dinmont terrier", "195": "Boston bull, Boston terrier", "196": "miniature schnauzer", "197": "giant schnauzer", "198": "standard schnauzer", "199": "Scotch terrier, Scottish terrier, Scottie", "200": "Tibetan terrier, chrysanthemum dog", "201": "silky terrier, Sydney silky", "202": "soft-coated wheaten terrier", "203": "West Highland white terrier", "204": "Lhasa, Lhasa apso", "205": "flat-coated retriever", "206": "curly-coated retriever", "207": "golden retriever", "208": "Labrador retriever", "209": "Chesapeake Bay retriever", "210": "German short-haired pointer", "211": "vizsla, Hungarian pointer", "212": "English setter", "213": "Irish setter, red setter", "214": "Gordon setter", "215": "Brittany spaniel", "216": "clumber, clumber spaniel", "217": "English springer, English springer spaniel", "218": "Welsh springer spaniel", "219": "cocker spaniel, English cocker spaniel, cocker", "220": "Sussex spaniel", "221": "Irish water spaniel", "222": "kuvasz", "223": "schipperke", "224": "groenendael", "225": "malinois", "226": "briard", "227": "kelpie", "228": "komondor", "229": "Old English sheepdog, bobtail", "230": "Shetland sheepdog, Shetland sheep dog, Shetland", "231": "collie", "232": "Border collie", "233": "Bouvier des Flandres, Bouviers des Flandres", "234": "Rottweiler", "235": "German shepherd, German shepherd dog, German police dog, alsatian", "236": "Doberman, Doberman pinscher", "237": "miniature pinscher", "238": "Greater Swiss Mountain dog", "239": "Bernese mountain dog", "240": "Appenzeller", "241": "EntleBucher", "242": "boxer", "243": "bull mastiff", "244": "Tibetan mastiff", "245": "French bulldog", "246": "Great Dane", "247": "Saint Bernard, St Bernard", "248": "Eskimo dog, husky", "249": "malamute, malemute, Alaskan malamute", "250": "Siberian husky", "251": "dalmatian, coach dog, carriage dog", "252": "affenpinscher, monkey pinscher, monkey dog", "253": "basenji", "254": "pug, pug-dog", "255": "Leonberg", "256": "Newfoundland, Newfoundland dog", "257": "Great Pyrenees", "258": "Samoyed, Samoyede", "259": "Pomeranian", "260": "chow, chow chow", "261": "keeshond", "262": "Brabancon griffon", "263": "Pembroke, Pembroke Welsh corgi", "264": "Cardigan, Cardigan Welsh corgi", "265": "toy poodle", "266": "miniature poodle", "267": "standard poodle", "268": "Mexican hairless", "269": "timber wolf, grey wolf, gray wolf, Canis lupus", "270": "white wolf, Arctic wolf, Canis lupus tundrarum", "271": "red wolf, maned wolf, Canis rufus, Canis niger", "272": "coyote, prairie wolf, brush wolf, Canis latrans", "273": "dingo, warrigal, warragal, Canis dingo", "274": "dhole, Cuon alpinus", "275": "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus", "276": "hyena, hyaena", "277": "red fox, Vulpes vulpes", "278": "kit fox, Vulpes macrotis", "279": "Arctic fox, white fox, Alopex lagopus", "280": "grey fox, gray fox, Urocyon cinereoargenteus", "281": "tabby, tabby cat", "282": "tiger cat", "283": "Persian cat", "284": "Siamese cat, Siamese", "285": "Egyptian cat", "286": "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor", "287": "lynx, catamount", "288": "leopard, Panthera pardus", "289": "snow leopard, ounce, Panthera uncia", "290": "jaguar, panther, Panthera onca, Felis onca", "291": "lion, king of beasts, Panthera leo", "292": "tiger, Panthera tigris", "293": "cheetah, chetah, Acinonyx jubatus", "294": "brown bear, bruin, Ursus arctos", "295": "American black bear, black bear, Ursus americanus, Euarctos americanus", "296": "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus", "297": "sloth bear, Melursus ursinus, Ursus ursinus", "298": "mongoose", "299": "meerkat, mierkat", "300": "tiger beetle", "301": "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "302": "ground beetle, carabid beetle", "303": "long-horned beetle, longicorn, longicorn beetle", "304": "leaf beetle, chrysomelid", "305": "dung beetle", "306": "rhinoceros beetle", "307": "weevil", "308": "fly", "309": "bee", "310": "ant, emmet, pismire", "311": "grasshopper, hopper", "312": "cricket", "313": "walking stick, walkingstick, stick insect", "314": "cockroach, roach", "315": "mantis, mantid", "316": "cicada, cicala", "317": "leafhopper", "318": "lacewing, lacewing fly", "319": "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "320": "damselfly", "321": "admiral", "322": "ringlet, ringlet butterfly", "323": "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus", "324": "cabbage butterfly", "325": "sulphur butterfly, sulfur butterfly", "326": "lycaenid, lycaenid butterfly", "327": "starfish, sea star", "328": "sea urchin", "329": "sea cucumber, holothurian", "330": "wood rabbit, cottontail, cottontail rabbit", "331": "hare", "332": "Angora, Angora rabbit", "333": "hamster", "334": "porcupine, hedgehog", "335": "fox squirrel, eastern fox squirrel, Sciurus niger", "336": "marmot", "337": "beaver", "338": "guinea pig, Cavia cobaya", "339": "sorrel", "340": "zebra", "341": "hog, pig, grunter, squealer, Sus scrofa", "342": "wild boar, boar, Sus scrofa", "343": "warthog", "344": "hippopotamus, hippo, river horse, Hippopotamus amphibius", "345": "ox", "346": "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis", "347": "bison", "348": "ram, tup", "349": "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis", "350": "ibex, Capra ibex", "351": "hartebeest", "352": "impala, Aepyceros melampus", "353": "gazelle", "354": "Arabian camel, dromedary, Camelus dromedarius", "355": "llama", "356": "weasel", "357": "mink", "358": "polecat, fitch, foulmart, foumart, Mustela putorius", "359": "black-footed ferret, ferret, Mustela nigripes", "360": "otter", "361": "skunk, polecat, wood pussy", "362": "badger", "363": "armadillo", "364": "three-toed sloth, ai, Bradypus tridactylus", "365": "orangutan, orang, orangutang, Pongo pygmaeus", "366": "gorilla, Gorilla gorilla", "367": "chimpanzee, chimp, Pan troglodytes", "368": "gibbon, Hylobates lar", "369": "siamang, Hylobates syndactylus, Symphalangus syndactylus", "370": "guenon, guenon monkey", "371": "patas, hussar monkey, Erythrocebus patas", "372": "baboon", "373": "macaque", "374": "langur", "375": "colobus, colobus monkey", "376": "proboscis monkey, Nasalis larvatus", "377": "marmoset", "378": "capuchin, ringtail, Cebus capucinus", "379": "howler monkey, howler", "380": "titi, titi monkey", "381": "spider monkey, Ateles geoffroyi", "382": "squirrel monkey, Saimiri sciureus", "383": "Madagascar cat, ring-tailed lemur, Lemur catta", "384": "indri, indris, Indri indri, Indri brevicaudatus", "385": "Indian elephant, Elephas maximus", "386": "African elephant, Loxodonta africana", "387": "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens", "388": "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca", "389": "barracouta, snoek", "390": "eel", "391": "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch", "392": "rock beauty, Holocanthus tricolor", "393": "anemone fish", "394": "sturgeon", "395": "gar, garfish, garpike, billfish, Lepisosteus osseus", "396": "lionfish", "397": "puffer, pufferfish, blowfish, globefish", "398": "abacus", "399": "abaya", "400": "academic gown, academic robe, judge's robe", "401": "accordion, piano accordion, squeeze box", "402": "acoustic guitar", "403": "aircraft carrier, carrier, flattop, attack aircraft carrier", "404": "airliner", "405": "airship, dirigible", "406": "altar", "407": "ambulance", "408": "amphibian, amphibious vehicle", "409": "analog clock", "410": "apiary, bee house", "411": "apron", "412": "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "413": "assault rifle, assault gun", "414": "backpack, back pack, knapsack, packsack, rucksack, haversack", "415": "bakery, bakeshop, bakehouse", "416": "balance beam, beam", "417": "balloon", "418": "ballpoint, ballpoint pen, ballpen, Biro", "419": "Band Aid", "420": "banjo", "421": "bannister, banister, balustrade, balusters, handrail", "422": "barbell", "423": "barber chair", "424": "barbershop", "425": "barn", "426": "barometer", "427": "barrel, cask", "428": "barrow, garden cart, lawn cart, wheelbarrow", "429": "baseball", "430": "basketball", "431": "bassinet", "432": "bassoon", "433": "bathing cap, swimming cap", "434": "bath towel", "435": "bathtub, bathing tub, bath, tub", "436": "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "437": "beacon, lighthouse, beacon light, pharos", "438": "beaker", "439": "bearskin, busby, shako", "440": "beer bottle", "441": "beer glass", "442": "bell cote, bell cot", "443": "bib", "444": "bicycle-built-for-two, tandem bicycle, tandem", "445": "bikini, two-piece", "446": "binder, ring-binder", "447": "binoculars, field glasses, opera glasses", "448": "birdhouse", "449": "boathouse", "450": "bobsled, bobsleigh, bob", "451": "bolo tie, bolo, bola tie, bola", "452": "bonnet, poke bonnet", "453": "bookcase", "454": "bookshop, bookstore, bookstall", "455": "bottlecap", "456": "bow", "457": "bow tie, bow-tie, bowtie", "458": "brass, memorial tablet, plaque", "459": "brassiere, bra, bandeau", "460": "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "461": "breastplate, aegis, egis", "462": "broom", "463": "bucket, pail", "464": "buckle", "465": "bulletproof vest", "466": "bullet train, bullet", "467": "butcher shop, meat market", "468": "cab, hack, taxi, taxicab", "469": "caldron, cauldron", "470": "candle, taper, wax light", "471": "cannon", "472": "canoe", "473": "can opener, tin opener", "474": "cardigan", "475": "car mirror", "476": "carousel, carrousel, merry-go-round, roundabout, whirligig", "477": "carpenter's kit, tool kit", "478": "carton", "479": "car wheel", "480": "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM", "481": "cassette", "482": "cassette player", "483": "castle", "484": "catamaran", "485": "CD player", "486": "cello, violoncello", "487": "cellular telephone, cellular phone, cellphone, cell, mobile phone", "488": "chain", "489": "chainlink fence", "490": "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "491": "chain saw, chainsaw", "492": "chest", "493": "chiffonier, commode", "494": "chime, bell, gong", "495": "china cabinet, china closet", "496": "Christmas stocking", "497": "church, church building", "498": "cinema, movie theater, movie theatre, movie house, picture palace", "499": "cleaver, meat cleaver, chopper", "500": "cliff dwelling", "501": "cloak", "502": "clog, geta, patten, sabot", "503": "cocktail shaker", "504": "coffee mug", "505": "coffeepot", "506": "coil, spiral, volute, whorl, helix", "507": "combination lock", "508": "computer keyboard, keypad", "509": "confectionery, confectionary, candy store", "510": "container ship, containership, container vessel", "511": "convertible", "512": "corkscrew, bottle screw", "513": "cornet, horn, trumpet, trump", "514": "cowboy boot", "515": "cowboy hat, ten-gallon hat", "516": "cradle", "517": "crane2", "518": "crash helmet", "519": "crate", "520": "crib, cot", "521": "Crock Pot", "522": "croquet ball", "523": "crutch", "524": "cuirass", "525": "dam, dike, dyke", "526": "desk", "527": "desktop computer", "528": "dial telephone, dial phone", "529": "diaper, nappy, napkin", "530": "digital clock", "531": "digital watch", "532": "dining table, board", "533": "dishrag, dishcloth", "534": "dishwasher, dish washer, dishwashing machine", "535": "disk brake, disc brake", "536": "dock, dockage, docking facility", "537": "dogsled, dog sled, dog sleigh", "538": "dome", "539": "doormat, welcome mat", "540": "drilling platform, offshore rig", "541": "drum, membranophone, tympan", "542": "drumstick", "543": "dumbbell", "544": "Dutch oven", "545": "electric fan, blower", "546": "electric guitar", "547": "electric locomotive", "548": "entertainment center", "549": "envelope", "550": "espresso maker", "551": "face powder", "552": "feather boa, boa", "553": "file, file cabinet, filing cabinet", "554": "fireboat", "555": "fire engine, fire truck", "556": "fire screen, fireguard", "557": "flagpole, flagstaff", "558": "flute, transverse flute", "559": "folding chair", "560": "football helmet", "561": "forklift", "562": "fountain", "563": "fountain pen", "564": "four-poster", "565": "freight car", "566": "French horn, horn", "567": "frying pan, frypan, skillet", "568": "fur coat", "569": "garbage truck, dustcart", "570": "gasmask, respirator, gas helmet", "571": "gas pump, gasoline pump, petrol pump, island dispenser", "572": "goblet", "573": "go-kart", "574": "golf ball", "575": "golfcart, golf cart", "576": "gondola", "577": "gong, tam-tam", "578": "gown", "579": "grand piano, grand", "580": "greenhouse, nursery, glasshouse", "581": "grille, radiator grille", "582": "grocery store, grocery, food market, market", "583": "guillotine", "584": "hair slide", "585": "hair spray", "586": "half track", "587": "hammer", "588": "hamper", "589": "hand blower, blow dryer, blow drier, hair dryer, hair drier", "590": "hand-held computer, hand-held microcomputer", "591": "handkerchief, hankie, hanky, hankey", "592": "hard disc, hard disk, fixed disk", "593": "harmonica, mouth organ, harp, mouth harp", "594": "harp", "595": "harvester, reaper", "596": "hatchet", "597": "holster", "598": "home theater, home theatre", "599": "honeycomb", "600": "hook, claw", "601": "hoopskirt, crinoline", "602": "horizontal bar, high bar", "603": "horse cart, horse-cart", "604": "hourglass", "605": "iPod", "606": "iron, smoothing iron", "607": "jack-o'-lantern", "608": "jean, blue jean, denim", "609": "jeep, landrover", "610": "jersey, T-shirt, tee shirt", "611": "jigsaw puzzle", "612": "jinrikisha, ricksha, rickshaw", "613": "joystick", "614": "kimono", "615": "knee pad", "616": "knot", "617": "lab coat, laboratory coat", "618": "ladle", "619": "lampshade, lamp shade", "620": "laptop, laptop computer", "621": "lawn mower, mower", "622": "lens cap, lens cover", "623": "letter opener, paper knife, paperknife", "624": "library", "625": "lifeboat", "626": "lighter, light, igniter, ignitor", "627": "limousine, limo", "628": "liner, ocean liner", "629": "lipstick, lip rouge", "630": "Loafer", "631": "lotion", "632": "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "633": "loupe, jeweler's loupe", "634": "lumbermill, sawmill", "635": "magnetic compass", "636": "mailbag, postbag", "637": "mailbox, letter box", "638": "maillot", "639": "maillot, tank suit", "640": "manhole cover", "641": "maraca", "642": "marimba, xylophone", "643": "mask", "644": "matchstick", "645": "maypole", "646": "maze, labyrinth", "647": "measuring cup", "648": "medicine chest, medicine cabinet", "649": "megalith, megalithic structure", "650": "microphone, mike", "651": "microwave, microwave oven", "652": "military uniform", "653": "milk can", "654": "minibus", "655": "miniskirt, mini", "656": "minivan", "657": "missile", "658": "mitten", "659": "mixing bowl", "660": "mobile home, manufactured home", "661": "Model T", "662": "modem", "663": "monastery", "664": "monitor", "665": "moped", "666": "mortar", "667": "mortarboard", "668": "mosque", "669": "mosquito net", "670": "motor scooter, scooter", "671": "mountain bike, all-terrain bike, off-roader", "672": "mountain tent", "673": "mouse, computer mouse", "674": "mousetrap", "675": "moving van", "676": "muzzle", "677": "nail", "678": "neck brace", "679": "necklace", "680": "nipple", "681": "notebook, notebook computer", "682": "obelisk", "683": "oboe, hautboy, hautbois", "684": "ocarina, sweet potato", "685": "odometer, hodometer, mileometer, milometer", "686": "oil filter", "687": "organ, pipe organ", "688": "oscilloscope, scope, cathode-ray oscilloscope, CRO", "689": "overskirt", "690": "oxcart", "691": "oxygen mask", "692": "packet", "693": "paddle, boat paddle", "694": "paddlewheel, paddle wheel", "695": "padlock", "696": "paintbrush", "697": "pajama, pyjama, pj's, jammies", "698": "palace", "699": "panpipe, pandean pipe, syrinx", "700": "paper towel", "701": "parachute, chute", "702": "parallel bars, bars", "703": "park bench", "704": "parking meter", "705": "passenger car, coach, carriage", "706": "patio, terrace", "707": "pay-phone, pay-station", "708": "pedestal, plinth, footstall", "709": "pencil box, pencil case", "710": "pencil sharpener", "711": "perfume, essence", "712": "Petri dish", "713": "photocopier", "714": "pick, plectrum, plectron", "715": "pickelhaube", "716": "picket fence, paling", "717": "pickup, pickup truck", "718": "pier", "719": "piggy bank, penny bank", "720": "pill bottle", "721": "pillow", "722": "ping-pong ball", "723": "pinwheel", "724": "pirate, pirate ship", "725": "pitcher, ewer", "726": "plane, carpenter's plane, woodworking plane", "727": "planetarium", "728": "plastic bag", "729": "plate rack", "730": "plow, plough", "731": "plunger, plumber's helper", "732": "Polaroid camera, Polaroid Land camera", "733": "pole", "734": "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria", "735": "poncho", "736": "pool table, billiard table, snooker table", "737": "pop bottle, soda bottle", "738": "pot, flowerpot", "739": "potter's wheel", "740": "power drill", "741": "prayer rug, prayer mat", "742": "printer", "743": "prison, prison house", "744": "projectile, missile", "745": "projector", "746": "puck, hockey puck", "747": "punching bag, punch bag, punching ball, punchball", "748": "purse", "749": "quill, quill pen", "750": "quilt, comforter, comfort, puff", "751": "racer, race car, racing car", "752": "racket, racquet", "753": "radiator", "754": "radio, wireless", "755": "radio telescope, radio reflector", "756": "rain barrel", "757": "recreational vehicle, RV, R.V.", "758": "reel", "759": "reflex camera", "760": "refrigerator, icebox", "761": "remote control, remote", "762": "restaurant, eating house, eating place, eatery", "763": "revolver, six-gun, six-shooter", "764": "rifle", "765": "rocking chair, rocker", "766": "rotisserie", "767": "rubber eraser, rubber, pencil eraser", "768": "rugby ball", "769": "rule, ruler", "770": "running shoe", "771": "safe", "772": "safety pin", "773": "saltshaker, salt shaker", "774": "sandal", "775": "sarong", "776": "sax, saxophone", "777": "scabbard", "778": "scale, weighing machine", "779": "school bus", "780": "schooner", "781": "scoreboard", "782": "screen, CRT screen", "783": "screw", "784": "screwdriver", "785": "seat belt, seatbelt", "786": "sewing machine", "787": "shield, buckler", "788": "shoe shop, shoe-shop, shoe store", "789": "shoji", "790": "shopping basket", "791": "shopping cart", "792": "shovel", "793": "shower cap", "794": "shower curtain", "795": "ski", "796": "ski mask", "797": "sleeping bag", "798": "slide rule, slipstick", "799": "sliding door", "800": "slot, one-armed bandit", "801": "snorkel", "802": "snowmobile", "803": "snowplow, snowplough", "804": "soap dispenser", "805": "soccer ball", "806": "sock", "807": "solar dish, solar collector, solar furnace", "808": "sombrero", "809": "soup bowl", "810": "space bar", "811": "space heater", "812": "space shuttle", "813": "spatula", "814": "speedboat", "815": "spider web, spider's web", "816": "spindle", "817": "sports car, sport car", "818": "spotlight, spot", "819": "stage", "820": "steam locomotive", "821": "steel arch bridge", "822": "steel drum", "823": "stethoscope", "824": "stole", "825": "stone wall", "826": "stopwatch, stop watch", "827": "stove", "828": "strainer", "829": "streetcar, tram, tramcar, trolley, trolley car", "830": "stretcher", "831": "studio couch, day bed", "832": "stupa, tope", "833": "submarine, pigboat, sub, U-boat", "834": "suit, suit of clothes", "835": "sundial", "836": "sunglass", "837": "sunglasses, dark glasses, shades", "838": "sunscreen, sunblock, sun blocker", "839": "suspension bridge", "840": "swab, swob, mop", "841": "sweatshirt", "842": "swimming trunks, bathing trunks", "843": "swing", "844": "switch, electric switch, electrical switch", "845": "syringe", "846": "table lamp", "847": "tank, army tank, armored combat vehicle, armoured combat vehicle", "848": "tape player", "849": "teapot", "850": "teddy, teddy bear", "851": "television, television system", "852": "tennis ball", "853": "thatch, thatched roof", "854": "theater curtain, theatre curtain", "855": "thimble", "856": "thresher, thrasher, threshing machine", "857": "throne", "858": "tile roof", "859": "toaster", "860": "tobacco shop, tobacconist shop, tobacconist", "861": "toilet seat", "862": "torch", "863": "totem pole", "864": "tow truck, tow car, wrecker", "865": "toyshop", "866": "tractor", "867": "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "868": "tray", "869": "trench coat", "870": "tricycle, trike, velocipede", "871": "trimaran", "872": "tripod", "873": "triumphal arch", "874": "trolleybus, trolley coach, trackless trolley", "875": "trombone", "876": "tub, vat", "877": "turnstile", "878": "typewriter keyboard", "879": "umbrella", "880": "unicycle, monocycle", "881": "upright, upright piano", "882": "vacuum, vacuum cleaner", "883": "vase", "884": "vault", "885": "velvet", "886": "vending machine", "887": "vestment", "888": "viaduct", "889": "violin, fiddle", "890": "volleyball", "891": "waffle iron", "892": "wall clock", "893": "wallet, billfold, notecase, pocketbook", "894": "wardrobe, closet, press", "895": "warplane, military plane", "896": "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "897": "washer, automatic washer, washing machine", "898": "water bottle", "899": "water jug", "900": "water tower", "901": "whiskey jug", "902": "whistle", "903": "wig", "904": "window screen", "905": "window shade", "906": "Windsor tie", "907": "wine bottle", "908": "wing", "909": "wok", "910": "wooden spoon", "911": "wool, woolen, woollen", "912": "worm fence, snake fence, snake-rail fence, Virginia fence", "913": "wreck", "914": "yawl", "915": "yurt", "916": "web site, website, internet site, site", "917": "comic book", "918": "crossword puzzle, crossword", "919": "street sign", "920": "traffic light, traffic signal, stoplight", "921": "book jacket, dust cover, dust jacket, dust wrapper", "922": "menu", "923": "plate", "924": "guacamole", "925": "consomme", "926": "hot pot, hotpot", "927": "trifle", "928": "ice cream, icecream", "929": "ice lolly, lolly, lollipop, popsicle", "930": "French loaf", "931": "bagel, beigel", "932": "pretzel", "933": "cheeseburger", "934": "hotdog, hot dog, red hot", "935": "mashed potato", "936": "head cabbage", "937": "broccoli", "938": "cauliflower", "939": "zucchini, courgette", "940": "spaghetti squash", "941": "acorn squash", "942": "butternut squash", "943": "cucumber, cuke", "944": "artichoke, globe artichoke", "945": "bell pepper", "946": "cardoon", "947": "mushroom", "948": "Granny Smith", "949": "strawberry", "950": "orange", "951": "lemon", "952": "fig", "953": "pineapple, ananas", "954": "banana", "955": "jackfruit, jak, jack", "956": "custard apple", "957": "pomegranate", "958": "hay", "959": "carbonara", "960": "chocolate sauce, chocolate syrup", "961": "dough", "962": "meat loaf, meatloaf", "963": "pizza, pizza pie", "964": "potpie", "965": "burrito", "966": "red wine", "967": "espresso", "968": "cup", "969": "eggnog", "970": "alp", "971": "bubble", "972": "cliff, drop, drop-off", "973": "coral reef", "974": "geyser", "975": "lakeside, lakeshore", "976": "promontory, headland, head, foreland", "977": "sandbar, sand bar", "978": "seashore, coast, seacoast, sea-coast", "979": "valley, vale", "980": "volcano", "981": "ballplayer, baseball player", "982": "groom, bridegroom", "983": "scuba diver", "984": "rapeseed", "985": "daisy", "986": "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", "987": "corn", "988": "acorn", "989": "hip, rose hip, rosehip", "990": "buckeye, horse chestnut, conker", "991": "coral fungus", "992": "agaric", "993": "gyromitra", "994": "stinkhorn, carrion fungus", "995": "earthstar", "996": "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa", "997": "bolete", "998": "ear, spike, capitulum", "999": "toilet tissue, toilet paper, bathroom tissue"}}}}], "splits": [{"name": "test", "num_bytes": 13613661561, "num_examples": 100000}, {"name": "train", "num_bytes": 146956944242, "num_examples": 1281167}, {"name": "validation", "num_bytes": 6709003386, "num_examples": 50000}], "download_size": 166009941208, "dataset_size": 167279609189}} | false | null | 2024-07-16T13:30:57 | 481 | 5 | false | 4603483700ee984ea9debe3ddbfdeae86f6489eb | ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated. In its completion, ImageNet hopes to offer tens of millions of cleanly sorted images for most of the concepts in the WordNet hierarchy. ImageNet 2012 is the most commonly used subset of ImageNet. This dataset spans 1000 object classes and contains 1,281,167 training images, 50,000 validation images and 100,000 test images | 34,238 | 1,042,001 | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:other",
"size_categories:1M<n<10M",
"arxiv:1409.0575",
"arxiv:1912.07726",
"arxiv:1811.12231",
"arxiv:2109.13228",
"region:us"
] | 2022-05-02T16:33:23 | imagenet-1k-1 | @article{imagenet15russakovsky,
Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
Title = { {ImageNet Large Scale Visual Recognition Challenge} },
Year = {2015},
journal = {International Journal of Computer Vision (IJCV)},
doi = {10.1007/s11263-015-0816-y},
volume={115},
number={3},
pages={211-252}
} |
62fd4ff64723285c5e151be0 | allenai/real-toxicity-prompts | allenai | {"language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["image-generation"], "task_ids": ["text-generation"], "pretty_name": "Real Toxicity Prompts"} | false | null | 2022-09-30T14:23:19 | 77 | 5 | false | f21629712ffd6a3d13a54fd2807ccd521c55ef74 |
Dataset Card for Real Toxicity Prompts
Dataset Summary
RealToxicityPrompts is a dataset of 100k sentence snippets from the web for researchers to further address the risk of neural toxic degeneration in models.
Languages
English
Dataset Structure
Data Instances
Each instance represents a prompt and its metadata:
{
"filename":"0766186-bc7f2a64cb271f5f56cf6f25570cd9ed.txt",
"begin":340,
"end":564… See the full description on the dataset page: https://huggingface.co/datasets/allenai/real-toxicity-prompts. | 3,170 | 83,500 | [
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2009.11462",
"doi:10.57967/hf/0002",
"region:us"
] | 2022-08-17T20:30:46 | null | null |
639244f571c51c43091df168 | Anthropic/hh-rlhf | Anthropic | {"license": "mit", "tags": ["human-feedback"]} | false | null | 2023-05-26T18:47:34 | 1,298 | 5 | 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. | 13,263 | 1,558,457 | [
"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 |
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 | 661 | 5 | 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. | 23,332 | 613,392 | [
"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 |
64257a4df3f2e17bb6be878b | bigcode/starcoderdata | bigcode | {"annotations_creators": [], "language_creators": ["crowdsourced", "expert-generated"], "language": ["code"], "license": ["other"], "multilinguality": ["multilingual"], "pretty_name": "The-Stack", "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["text-generation"], "extra_gated_prompt": "## Terms of Use for The Stack\n\nThe Stack dataset is a collection of source code in over 300 programming languages. We ask that you read and acknowledge the following points before using the dataset:\n1. The Stack is a collection of source code from repositories with various licenses. Any use of all or part of the code gathered in The Stack must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point.\n2. The Stack is regularly updated to enact validated data removal requests. By clicking on \"Access repository\", you agree to update your own version of The Stack to the most recent usable version specified by the maintainers in [the following thread](https://huggingface.co/datasets/bigcode/the-stack/discussions/7). If you have questions about dataset versions and allowed uses, please also ask them in the dataset\u2019s [community discussions](https://huggingface.co/datasets/bigcode/the-stack/discussions/new). We will also notify users via email when the latest usable version changes.\n3. To host, share, or otherwise provide access to The Stack dataset, you must include [these Terms of Use](https://huggingface.co/datasets/bigcode/the-stack#terms-of-use-for-the-stack) and require users to agree to it.\n\nBy clicking on \"Access repository\" below, you accept that your contact information (email address and username) can be shared with the dataset maintainers as well.\n ", "extra_gated_fields": {"Email": "text", "I have read the License and agree with its terms": "checkbox"}} | false | null | 2023-05-16T10:05:48 | 425 | 5 | false | 9fc30b578cedaec69e47302df72cf00feed7c8c4 |
StarCoder Training Dataset
Dataset description
This is the dataset used for training StarCoder and StarCoderBase. It contains 783GB of code in 86 programming languages, and includes 54GB GitHub Issues + 13GB Jupyter notebooks in scripts and text-code pairs,
and 32GB of GitHub commits, which is approximately 250 Billion tokens.
Dataset creation
The creation and filtering of The Stack is explained in the original dataset, we additionally decontaminate… See the full description on the dataset page: https://huggingface.co/datasets/bigcode/starcoderdata. | 5,043 | 154,941 | [
"task_categories:text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"language:code",
"license:other",
"size_categories:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2023-03-30T12:02:21 | null | null |
6524d963d4b61d080792ee2b | princeton-nlp/SWE-bench | princeton-nlp | {"dataset_info": {"features": [{"name": "repo", "dtype": "string"}, {"name": "instance_id", "dtype": "string"}, {"name": "base_commit", "dtype": "string"}, {"name": "patch", "dtype": "string"}, {"name": "test_patch", "dtype": "string"}, {"name": "problem_statement", "dtype": "string"}, {"name": "hints_text", "dtype": "string"}, {"name": "created_at", "dtype": "string"}, {"name": "version", "dtype": "string"}, {"name": "FAIL_TO_PASS", "dtype": "string"}, {"name": "PASS_TO_PASS", "dtype": "string"}, {"name": "environment_setup_commit", "dtype": "string"}], "splits": [{"name": "dev", "num_bytes": 4783179, "num_examples": 225}, {"name": "test", "num_bytes": 44121927, "num_examples": 2294}, {"name": "train", "num_bytes": 367610377, "num_examples": 19008}], "download_size": 120086340, "dataset_size": 416515483}, "configs": [{"config_name": "default", "data_files": [{"split": "dev", "path": "data/dev-*"}, {"split": "test", "path": "data/test-*"}, {"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-03-03T05:28:08 | 105 | 5 | false | e48e2bd1e9fecd5bbd641e9414ac59da9f2e69f6 |
Dataset Summary
SWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. The dataset collects 2,294 Issue-Pull Request pairs from 12 popular Python repositories. Evaluation is performed by unit test verification using post-PR behavior as the reference solution.
The dataset was released as part of SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
Want to run inference now?
This dataset only contains the problem_statement… See the full description on the dataset page: https://huggingface.co/datasets/princeton-nlp/SWE-bench. | 53,602 | 1,272,692 | [
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"library:datasets",
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"library:mlcroissant",
"library:polars",
"arxiv:2310.06770",
"region:us"
] | 2023-10-10T04:56:03 | null | null |
656523d6bfb751371817c448 | Idavidrein/gpqa | Idavidrein | {"license": "cc-by-4.0", "viewer": true, "extra_gated_prompt": "You agree to NOT reveal examples from this dataset in plain text or images online, to reduce the risk of leakage into foundation model training corpora.", "extra_gated_fields": {"I accept these terms": "checkbox"}, "configs": [{"config_name": "gpqa_extended", "data_files": "gpqa_extended.csv"}, {"config_name": "gpqa_main", "data_files": "gpqa_main.csv"}, {"config_name": "gpqa_diamond", "data_files": "gpqa_diamond.csv"}, {"config_name": "gpqa_experts", "data_files": "gpqa_experts.csv"}], "task_categories": ["question-answering", "text-generation"], "language": ["en"], "tags": ["open-domain-qa", "open-book-qa", "multiple-choice-qa"], "pretty_name": "GPQA", "size_categories": ["n<1K"]} | false | null | 2024-03-28T21:38:55 | 150 | 5 | false | 90b8e5be2b1d3d2dbfe016cdab47981150600c4a |
Dataset Card for GPQA
GPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy, despite spending >30m with full access to Google.
We request that you do not reveal examples from this dataset in plain text or images online, to reduce the risk of leakage into foundation… See the full description on the dataset page: https://huggingface.co/datasets/Idavidrein/gpqa. | 63,567 | 695,377 | [
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
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"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2311.12022",
"region:us",
"open-domain-qa",
"open-book-qa",
"multiple-choice-qa"
] | 2023-11-27T23:18:46 | null | null |
661823b590a8b6724f1c6534 | HuggingFaceM4/the_cauldron | HuggingFaceM4 | {"dataset_info": [{"config_name": "ai2d", "features": [{"name": "images", "sequence": "image"}, {"name": "texts", "list": [{"name": "user", "dtype": "string"}, {"name": "assistant", "dtype": "string"}, {"name": "source", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 435362437.84770346, "num_examples": 2434}], "download_size": 438136609, "dataset_size": 435362437.84770346}, {"config_name": "aokvqa", "features": [{"name": "images", "sequence": "image"}, {"name": "texts", "list": [{"name": "user", "dtype": "string"}, {"name": "assistant", "dtype": "string"}, {"name": "source", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 871997710, "num_examples": 16539}], "download_size": 893265070, "dataset_size": 871997710}, {"config_name": "chart2text", "features": [{"name": "images", "sequence": "image"}, {"name": "texts", "list": [{"name": "user", "dtype": "string"}, {"name": "assistant", 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[{"name": "user", "dtype": "string"}, {"name": "assistant", "dtype": "string"}, {"name": "source", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 107489763, "num_examples": 2157}], "download_size": 107576214, "dataset_size": 107489763}, {"config_name": "websight", "features": [{"name": "images", "sequence": "image"}, {"name": "texts", "list": [{"name": "user", "dtype": "string"}, {"name": "assistant", "dtype": "string"}, {"name": "source", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 2011365901, "num_examples": 10000}], "download_size": 1601222161, "dataset_size": 2011365901}], "configs": [{"config_name": "ai2d", "data_files": [{"split": "train", "path": "ai2d/train-*"}]}, {"config_name": "aokvqa", "data_files": [{"split": "train", "path": "aokvqa/train-*"}]}, {"config_name": "chart2text", "data_files": [{"split": "train", "path": "chart2text/train-*"}]}, {"config_name": "chartqa", "data_files": [{"split": "train", "path": "chartqa/train-*"}]}, {"config_name": "clevr", "data_files": [{"split": "train", "path": "clevr/train-*"}]}, {"config_name": "clevr_math", "data_files": [{"split": "train", "path": "clevr_math/train-*"}]}, {"config_name": "cocoqa", "data_files": [{"split": "train", "path": "cocoqa/train-*"}]}, {"config_name": "datikz", "data_files": [{"split": "train", "path": "datikz/train-*"}]}, {"config_name": "diagram_image_to_text", "data_files": [{"split": "train", "path": "diagram_image_to_text/train-*"}]}, {"config_name": "docvqa", "data_files": [{"split": "train", "path": "docvqa/train-*"}]}, {"config_name": "dvqa", "data_files": [{"split": "train", "path": "dvqa/train-*"}]}, {"config_name": "figureqa", "data_files": [{"split": "train", "path": "figureqa/train-*"}]}, {"config_name": "finqa", "data_files": [{"split": "train", "path": "finqa/train-*"}]}, {"config_name": "geomverse", "data_files": [{"split": "train", "path": "geomverse/train-*"}]}, {"config_name": "hateful_memes", "data_files": [{"split": "train", "path": "hateful_memes/train-*"}]}, {"config_name": "hitab", "data_files": [{"split": "train", "path": "hitab/train-*"}]}, {"config_name": "iam", "data_files": [{"split": "train", "path": "iam/train-*"}]}, {"config_name": "iconqa", "data_files": [{"split": "train", "path": "iconqa/train-*"}]}, {"config_name": "infographic_vqa", "data_files": [{"split": "train", "path": "infographic_vqa/train-*"}]}, {"config_name": "intergps", "data_files": [{"split": "train", "path": "intergps/train-*"}]}, {"config_name": "localized_narratives", "data_files": [{"split": "train", "path": "localized_narratives/train-*"}]}, {"config_name": "mapqa", "data_files": [{"split": "train", "path": "mapqa/train-*"}]}, {"config_name": "mimic_cgd", "data_files": [{"split": "train", "path": "mimic_cgd/train-*"}]}, {"config_name": "multihiertt", "data_files": [{"split": "train", "path": "multihiertt/train-*"}]}, {"config_name": "nlvr2", "data_files": [{"split": "train", "path": "nlvr2/train-*"}]}, {"config_name": "ocrvqa", "data_files": [{"split": "train", "path": "ocrvqa/train-*"}]}, {"config_name": "okvqa", "data_files": [{"split": "train", "path": "okvqa/train-*"}]}, {"config_name": "plotqa", "data_files": [{"split": "train", "path": "plotqa/train-*"}]}, {"config_name": "raven", "data_files": [{"split": "train", "path": "raven/train-*"}]}, {"config_name": "rendered_text", "data_files": [{"split": "train", "path": "rendered_text/train-*"}]}, {"config_name": "robut_sqa", "data_files": [{"split": "train", "path": "robut_sqa/train-*"}]}, {"config_name": "robut_wikisql", "data_files": [{"split": "train", "path": "robut_wikisql/train-*"}]}, {"config_name": "robut_wtq", "data_files": [{"split": "train", "path": "robut_wtq/train-*"}]}, {"config_name": "scienceqa", "data_files": [{"split": "train", "path": "scienceqa/train-*"}]}, {"config_name": "screen2words", "data_files": [{"split": "train", "path": "screen2words/train-*"}]}, {"config_name": "spot_the_diff", "data_files": [{"split": "train", "path": "spot_the_diff/train-*"}]}, {"config_name": "st_vqa", "data_files": [{"split": "train", "path": "st_vqa/train-*"}]}, {"config_name": "tabmwp", "data_files": [{"split": "train", "path": "tabmwp/train-*"}]}, {"config_name": "tallyqa", "data_files": [{"split": "train", "path": "tallyqa/train-*"}]}, {"config_name": "tat_qa", "data_files": [{"split": "train", "path": "tat_qa/train-*"}]}, {"config_name": "textcaps", "data_files": [{"split": "train", "path": "textcaps/train-*"}]}, {"config_name": "textvqa", "data_files": [{"split": "train", "path": "textvqa/train-*"}]}, {"config_name": "tqa", "data_files": [{"split": "train", "path": "tqa/train-*"}]}, {"config_name": "vistext", "data_files": [{"split": "train", "path": "vistext/train-*"}]}, {"config_name": "visual7w", "data_files": [{"split": "train", "path": "visual7w/train-*"}]}, {"config_name": "visualmrc", "data_files": [{"split": "train", "path": "visualmrc/train-*"}]}, {"config_name": "vqarad", "data_files": [{"split": "train", "path": "vqarad/train-*"}]}, {"config_name": "vqav2", "data_files": [{"split": "train", "path": "vqav2/train-*"}]}, {"config_name": "vsr", "data_files": [{"split": "train", "path": "vsr/train-*"}]}, {"config_name": "websight", "data_files": [{"split": "train", "path": "websight/train-*"}]}]} | false | null | 2024-05-06T13:37:52 | 386 | 5 | false | 847a98a779b1652d65111daf20c972dfcd333605 |
Dataset Card for The Cauldron
Dataset description
The Cauldron is part of the Idefics2 release.
It is a massive collection of 50 vision-language datasets (training sets only) that were used for the fine-tuning of the vision-language model Idefics2.
Load the dataset
To load the dataset, install the library datasets with pip install datasets. Then,
from datasets import load_dataset
ds = load_dataset("HuggingFaceM4/the_cauldron", "ai2d")
to download… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceM4/the_cauldron. | 761,541 | 1,627,263 | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:1603.07396",
"arxiv:2206.01718",
"arxiv:2208.05358",
"arxiv:1612.06890",
"arxiv:2310.00367",
"arxiv:1710.07300",
"arxiv:2312.12241",
"arxiv:1912.03098",
"arxiv:2211.08545",
"arxiv:2306.05425",
"arxiv:1709.00103",
"arxiv:2003.12462",
"arxiv:1612.00837",
"arxiv:2205.00363",
"arxiv:2403.09029",
"arxiv:2405.02246",
"region:us"
] | 2024-04-11T17:53:57 | null | null |
66f8120ba90151b7d0e993ea | philschmid/amazon-product-descriptions-vlm | philschmid | {"language": ["en"], "license": "cc-by-nc-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["image-to-text"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "Uniq Id", "dtype": "string"}, {"name": "Product Name", "dtype": "string"}, {"name": "Category", "dtype": "string"}, {"name": "Selling Price", "dtype": "string"}, {"name": "Model Number", "dtype": "string"}, {"name": "About Product", "dtype": "string"}, {"name": "Product Specification", "dtype": "string"}, {"name": "Technical Details", "dtype": "string"}, {"name": "Shipping Weight", "dtype": "string"}, {"name": "Variants", "dtype": "string"}, {"name": "Product Url", "dtype": "string"}, {"name": "Is Amazon Seller", "dtype": "string"}, {"name": "description", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 49065172.22633136, "num_examples": 1345}], "download_size": 47605703, "dataset_size": 49065172.22633136}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2024-09-30T10:39:25 | 15 | 5 | false | f08a021c69c51d6894cfe39206448e7785d6156b |
Amazon Multimodal Product dataset
This is a modfied and slim verison of bprateek/amazon_product_description helpful to get started training multimodal LLMs.
The description field was generated used Gemini Flash.
| 1,227 | 9,284 | [
"task_categories:image-to-text",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-09-28T14:26:19 | null | null |
67374c18c32c765810f748f6 | HuggingFaceH4/MATH-500 | HuggingFaceH4 | {"task_categories": ["text-generation"], "language": ["en"], "pretty_name": "MATH-500"} | false | null | 2024-11-15T13:36:00 | 132 | 5 | false | ff5b20257d8185524591543f8ff5993951537bb8 |
Dataset Card for MATH-500
This dataset contains a subset of 500 problems from the MATH benchmark that OpenAI created in their Let's Verify Step by Step paper. See their GitHub repo for the source file: https://github.com/openai/prm800k/tree/main?tab=readme-ov-file#math-splits
| 58,150 | 102,400 | [
"task_categories:text-generation",
"language:en",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2024-11-15T13:26:48 | null | null |
67806c6743a58ab7b52ef7ec | Josephgflowers/Finance-Instruct-500k | Josephgflowers | {"license": "apache-2.0", "tags": ["finance", "fine-tuning", "conversational-ai", "named-entity-recognition", "sentiment-analysis", "topic-classification", "rag", "multilingual", "lightweight-llm"]} | false | null | 2025-03-01T19:24:42 | 47 | 5 | false | 379407b4708ededdf48cd33d1e1cffda45cc56f4 |
Finance-Instruct-500k Dataset
Overview
Finance-Instruct-500k is a comprehensive and meticulously curated dataset designed to train advanced language models for financial tasks, reasoning, and multi-turn conversations. Combining data from numerous high-quality financial datasets, this corpus provides over 500,000 entries, offering unparalleled depth and versatility for finance-related instruction tuning and fine-tuning.
The dataset includes content tailored for financial… See the full description on the dataset page: https://huggingface.co/datasets/Josephgflowers/Finance-Instruct-500k. | 1,154 | 1,869 | [
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"finance",
"fine-tuning",
"conversational-ai",
"named-entity-recognition",
"sentiment-analysis",
"topic-classification",
"rag",
"multilingual",
"lightweight-llm"
] | 2025-01-10T00:40:07 | 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"]} | false | null | 2025-03-06T22:23:34 | 156 | 5 | false | 65148ae21b6c0cc3c362aab1b202cd51a47cdd67 |
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. | 6,840 | 6,842 | [
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
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"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 |
67cd6c25b770987b3f80af97 | a-m-team/AM-DeepSeek-R1-Distilled-1.4M | a-m-team | {"license": "cc-by-nc-4.0", "task_categories": ["text-generation"], "language": ["zh", "en"], "tags": ["code", "math", "reasoning", "thinking", "deepseek-r1", "distill"], "size_categories": ["1M<n<10M"]} | false | null | 2025-03-10T18:31:04 | 24 | 5 | false | b3447a25c09f5b67817c0ea01a1d4844fba68884 | AM-DeepSeek-R1-Distilled-1.4M is a large-scale general reasoning task dataset composed of
high-quality and challenging reasoning problems. These problems are collected from numerous
open-source datasets, semantically deduplicated, and cleaned to eliminate test set contamination.
All responses in the dataset are distilled from the reasoning model (mostly DeepSeek-R1) and have undergone
rigorous verification: mathematical problems are validated through answer checking, code
problems via… See the full description on the dataset page: https://huggingface.co/datasets/a-m-team/AM-DeepSeek-R1-Distilled-1.4M. | 770 | 770 | [
"task_categories:text-generation",
"language:zh",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:1M<n<10M",
"region:us",
"code",
"math",
"reasoning",
"thinking",
"deepseek-r1",
"distill"
] | 2025-03-09T10:23:33 | null | null |
67d1c681fb5fa9c6f7814681 | nvidia/PhysicalAI-Robotics-Manipulation-Kitchen | nvidia | {"license": "cc-by-4.0", "task_categories": ["robotics"]} | false | null | 2025-03-18T17:29:55 | 5 | 5 | false | fb889006efa0c781315511c9b5f2aa5b4f3a0a8a |
PhysicalAI Robotics Manipulation in the Kitchen
Dataset Description:
PhysicalAI-Robotics-Manipulation-Kitchen is a dataset of automatic generated motions of robots performing operations such as opening and closing cabinets, drawers, dishwashers and fridges. The dataset was generated in IsaacSim leveraging reasoning algorithms and optimization-based motion planning to find solutions to the tasks automatically [1, 3]. The dataset includes a bimanual manipulator built with… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-Manipulation-Kitchen. | 933 | 933 | [
"task_categories:robotics",
"license:cc-by-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2025-03-12T17:38:09 | null | null |
67d1c6ef5e3f34422f94901f | nvidia/PhysicalAI-Robotics-Manipulation-SingleArm | nvidia | {"license": "cc-by-4.0"} | false | null | 2025-03-18T17:43:02 | 5 | 5 | false | 3f0944b2b593c37ea2fb4287692f07cb7d493b7b |
Dataset Description:
PhysicalAI-Robotics-Manipulation-SingeArm is a collection of datasets of automatic generated motions of a Franka Panda robot performing operations such as block stacking, opening cabinets and drawers. The dataset was generated in IsaacSim leveraging task and motion planning algorithms to find solutions to the tasks automatically [1, 3]. The environments are table-top scenes where the object layouts and asset textures are procedurally generated [2].This dataset… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-Manipulation-SingleArm. | 1,205 | 1,205 | [
"license:cc-by-4.0",
"region:us"
] | 2025-03-12T17:39:59 | null | null |
67db4720ba9a4529ea70e5f9 | microsoft/ChatBench | microsoft | {"license": "cc-by-4.0", "language": ["en"]} | false | null | 2025-03-21T14:47:34 | 5 | 5 | false | 04705ece07a5f07426f134dcf3f6f047deb5876d |
Dataset Card for ChatBench
This is the dataset from the paper, "ChatBench: From Static Benchmarks to Human-AI Evaluation", by Serina Chang, Ashton Anderson, and Jake Hofman.
Data Summary
ChatBench contains data from our user study on Prolific and our automated AI-alone experiments, enabling comparison of AI-alone, user-AI, and user-alone answers for the same set of MMLU benchmark questions (Hendrycks et al., 2021).
User study. Our user study consists of two phases. In… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/ChatBench. | 30 | 30 | [
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2025-03-19T22:37:20 | 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 | 334 | 4 | 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. | 14,806 | 349,386 | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
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"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 |
621ffdd236468d709f181e3f | nyu-mll/glue | nyu-mll | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["acceptability-classification", "natural-language-inference", "semantic-similarity-scoring", "sentiment-classification", "text-scoring"], "paperswithcode_id": "glue", "pretty_name": "GLUE (General Language Understanding Evaluation benchmark)", "config_names": ["ax", "cola", "mnli", "mnli_matched", "mnli_mismatched", "mrpc", "qnli", "qqp", "rte", "sst2", "stsb", "wnli"], "tags": ["qa-nli", "coreference-nli", "paraphrase-identification"], "dataset_info": [{"config_name": "ax", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": 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"qnli/train-*"}, {"split": "validation", "path": "qnli/validation-*"}, {"split": "test", "path": "qnli/test-*"}]}, {"config_name": "qqp", "data_files": [{"split": "train", "path": "qqp/train-*"}, {"split": "validation", "path": "qqp/validation-*"}, {"split": "test", "path": "qqp/test-*"}]}, {"config_name": "rte", "data_files": [{"split": "train", "path": "rte/train-*"}, {"split": "validation", "path": "rte/validation-*"}, {"split": "test", "path": "rte/test-*"}]}, {"config_name": "sst2", "data_files": [{"split": "train", "path": "sst2/train-*"}, {"split": "validation", "path": "sst2/validation-*"}, {"split": "test", "path": "sst2/test-*"}]}, {"config_name": "stsb", "data_files": [{"split": "train", "path": "stsb/train-*"}, {"split": "validation", "path": "stsb/validation-*"}, {"split": "test", "path": "stsb/test-*"}]}, {"config_name": "wnli", "data_files": [{"split": "train", "path": "wnli/train-*"}, {"split": "validation", "path": "wnli/validation-*"}, {"split": "test", "path": 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"natural_language_inference", "splits": {"train_split": "train", "eval_split": "validation"}, "col_mapping": {"sentence1": "text1", "sentence2": "text2", "label": "target"}}, {"config": "mnli", "task": "text-classification", "task_id": "natural_language_inference", "splits": {"train_split": "train", "eval_split": "validation_matched"}, "col_mapping": {"premise": "text1", "hypothesis": "text2", "label": "target"}}, {"config": "mnli_mismatched", "task": "text-classification", "task_id": "natural_language_inference", "splits": {"train_split": "train", "eval_split": "validation"}, "col_mapping": {"premise": "text1", "hypothesis": "text2", "label": "target"}}, {"config": "mnli_matched", "task": "text-classification", "task_id": "natural_language_inference", "splits": {"train_split": "train", "eval_split": "validation"}, "col_mapping": {"premise": "text1", "hypothesis": "text2", "label": "target"}}, {"config": "qnli", "task": "text-classification", "task_id": "natural_language_inference", "splits": {"train_split": "train", "eval_split": "validation"}, "col_mapping": {"question": "text1", "sentence": "text2", "label": "target"}}, {"config": "rte", "task": "text-classification", "task_id": "natural_language_inference", "splits": {"train_split": "train", "eval_split": "validation"}, "col_mapping": {"sentence1": "text1", "sentence2": "text2", "label": "target"}}, {"config": "wnli", "task": "text-classification", "task_id": "natural_language_inference", "splits": {"train_split": "train", "eval_split": "validation"}, "col_mapping": {"sentence1": "text1", "sentence2": "text2", "label": "target"}}]} | false | null | 2024-01-30T07:41:18 | 403 | 4 | false | bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c |
Dataset Card for GLUE
Dataset Summary
GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems.
Supported Tasks and Leaderboards
The leaderboard for the GLUE benchmark can be found at this address. It comprises the following tasks:
ax
A manually-curated evaluation dataset for fine-grained… See the full description on the dataset page: https://huggingface.co/datasets/nyu-mll/glue. | 190,406 | 36,294,076 | [
"task_categories:text-classification",
"task_ids:acceptability-classification",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"task_ids:sentiment-classification",
"task_ids:text-scoring",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:other",
"size_categories:1M<n<10M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:1804.07461",
"region:us",
"qa-nli",
"coreference-nli",
"paraphrase-identification"
] | 2022-03-02T23:29:22 | glue | null |
621ffdd236468d709f181e41 | google-research-datasets/go_emotions | google-research-datasets | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M", "10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification", "multi-label-classification"], "paperswithcode_id": "goemotions", "pretty_name": "GoEmotions", "config_names": ["raw", "simplified"], "tags": ["emotion"], "dataset_info": [{"config_name": "raw", "features": [{"name": "text", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "subreddit", "dtype": "string"}, {"name": "link_id", "dtype": "string"}, {"name": "parent_id", "dtype": "string"}, {"name": "created_utc", "dtype": "float32"}, {"name": "rater_id", "dtype": "int32"}, {"name": "example_very_unclear", "dtype": "bool"}, {"name": "admiration", "dtype": "int32"}, {"name": "amusement", "dtype": "int32"}, {"name": "anger", "dtype": "int32"}, {"name": "annoyance", "dtype": "int32"}, {"name": "approval", "dtype": "int32"}, {"name": "caring", "dtype": "int32"}, {"name": "confusion", "dtype": "int32"}, {"name": "curiosity", "dtype": "int32"}, {"name": "desire", "dtype": "int32"}, {"name": "disappointment", "dtype": "int32"}, {"name": "disapproval", "dtype": "int32"}, {"name": "disgust", "dtype": "int32"}, {"name": "embarrassment", "dtype": "int32"}, {"name": "excitement", "dtype": "int32"}, {"name": "fear", "dtype": "int32"}, {"name": "gratitude", "dtype": "int32"}, {"name": "grief", "dtype": "int32"}, {"name": "joy", "dtype": "int32"}, {"name": "love", "dtype": "int32"}, {"name": "nervousness", "dtype": "int32"}, {"name": "optimism", "dtype": "int32"}, {"name": "pride", "dtype": "int32"}, {"name": "realization", "dtype": "int32"}, {"name": "relief", "dtype": "int32"}, {"name": "remorse", "dtype": "int32"}, {"name": "sadness", "dtype": "int32"}, {"name": "surprise", "dtype": "int32"}, {"name": "neutral", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 55343102, "num_examples": 211225}], "download_size": 24828322, "dataset_size": 55343102}, {"config_name": "simplified", "features": [{"name": "text", "dtype": "string"}, {"name": "labels", "sequence": {"class_label": {"names": {"0": "admiration", "1": "amusement", "2": "anger", "3": "annoyance", "4": "approval", "5": "caring", "6": "confusion", "7": "curiosity", "8": "desire", "9": "disappointment", "10": "disapproval", "11": "disgust", "12": "embarrassment", "13": "excitement", "14": "fear", "15": "gratitude", "16": "grief", "17": "joy", "18": "love", "19": "nervousness", "20": "optimism", "21": "pride", "22": "realization", "23": "relief", "24": "remorse", "25": "sadness", "26": "surprise", "27": "neutral"}}}}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4224138, "num_examples": 43410}, {"name": "validation", "num_bytes": 527119, "num_examples": 5426}, {"name": "test", "num_bytes": 524443, "num_examples": 5427}], "download_size": 3464371, "dataset_size": 5275700}], "configs": [{"config_name": "raw", "data_files": [{"split": "train", "path": "raw/train-*"}]}, {"config_name": "simplified", "data_files": [{"split": "train", "path": "simplified/train-*"}, {"split": "validation", "path": "simplified/validation-*"}, {"split": "test", "path": "simplified/test-*"}], "default": true}]} | false | null | 2024-01-04T11:56:51 | 193 | 4 | false | add492243ff905527e67aeb8b80c082af02207c3 |
Dataset Card for GoEmotions
Dataset Summary
The GoEmotions dataset contains 58k carefully curated Reddit comments labeled for 27 emotion categories or Neutral.
The raw data is included as well as the smaller, simplified version of the dataset with predefined train/val/test
splits.
Supported Tasks and Leaderboards
This dataset is intended for multi-class, multi-label emotion classification.
Languages
The data is in English.… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/go_emotions. | 10,142 | 262,979 | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2005.00547",
"region:us",
"emotion"
] | 2022-03-02T23:29:22 | goemotions | null |
621ffdd236468d709f181e77 | stanfordnlp/imdb | stanfordnlp | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "paperswithcode_id": "imdb-movie-reviews", "pretty_name": "IMDB", "dataset_info": {"config_name": "plain_text", "features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "neg", "1": "pos"}}}}], "splits": [{"name": "train", "num_bytes": 33432823, "num_examples": 25000}, {"name": "test", "num_bytes": 32650685, "num_examples": 25000}, {"name": "unsupervised", "num_bytes": 67106794, "num_examples": 50000}], "download_size": 83446840, "dataset_size": 133190302}, "configs": [{"config_name": "plain_text", "data_files": [{"split": "train", "path": "plain_text/train-*"}, {"split": "test", "path": "plain_text/test-*"}, {"split": "unsupervised", "path": "plain_text/unsupervised-*"}], "default": true}], "train-eval-index": [{"config": "plain_text", "task": "text-classification", "task_id": "binary_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 | 2024-01-04T12:09:45 | 291 | 4 | false | e6281661ce1c48d982bc483cf8a173c1bbeb5d31 |
Dataset Card for "imdb"
Dataset Summary
Large Movie Review Dataset.
This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed… See the full description on the dataset page: https://huggingface.co/datasets/stanfordnlp/imdb. | 114,935 | 6,836,441 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:expert-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"
] | 2022-03-02T23:29:22 | imdb-movie-reviews | null |
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{"config_name": "20231101.zh-classical", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14869227, "num_examples": 12708}], "download_size": 10098073, "dataset_size": 14869227}, {"config_name": "20231101.zh-min-nan", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 153672031, "num_examples": 432798}], "download_size": 37122048, "dataset_size": 153672031}, {"config_name": "20231101.zh-yue", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 109936351, "num_examples": 134140}], "download_size": 64950815, "dataset_size": 109936351}, {"config_name": "20231101.zu", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7088246, "num_examples": 11561}], "download_size": 3792429, "dataset_size": 7088246}], "language_bcp47": ["be-tarask", "en-simple"]} | false | null | 2024-01-09T09:40:51 | 764 | 4 | false | b04c8d1ceb2f5cd4588862100d08de323dccfbaa |
Dataset Card for Wikimedia Wikipedia
Dataset Summary
Wikipedia dataset containing cleaned articles of all languages.
The dataset is built from the Wikipedia dumps (https://dumps.wikimedia.org/)
with one subset per language, each containing a single train split.
Each example contains the content of one full Wikipedia article with cleaning to strip
markdown and unwanted sections (references, etc.).
All language subsets have already been processed for recent dump… See the full description on the dataset page: https://huggingface.co/datasets/wikimedia/wikipedia. | 83,656 | 958,447 | [
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"language:za",
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"language:zu",
"license:cc-by-sa-3.0",
"license:gfdl",
"size_categories:10M<n<100M",
"format:parquet",
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"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2022-03-02T23:29:22 | null | null |
645e8da96320b0efe40ade7a | roneneldan/TinyStories | roneneldan | {"license": "cdla-sharing-1.0", "task_categories": ["text-generation"], "language": ["en"]} | false | null | 2024-08-12T13:27:26 | 634 | 4 | false | f54c09fd23315a6f9c86f9dc80f725de7d8f9c64 | Dataset containing synthetically generated (by GPT-3.5 and GPT-4) short stories that only use a small vocabulary.
Described in the following paper: https://arxiv.org/abs/2305.07759.
The models referred to in the paper were trained on TinyStories-train.txt (the file tinystories-valid.txt can be used for validation loss). These models can be found on Huggingface, at roneneldan/TinyStories-1M/3M/8M/28M/33M/1Layer-21M.
Additional resources:
tinystories_all_data.tar.gz - contains a superset of… See the full description on the dataset page: https://huggingface.co/datasets/roneneldan/TinyStories. | 23,701 | 582,508 | [
"task_categories:text-generation",
"language:en",
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"library:datasets",
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"library:mlcroissant",
"library:polars",
"arxiv:2305.07759",
"region:us"
] | 2023-05-12T19:04:09 | null | null |
64b67e1341d9fa8f906cfac4 | lmsys/chatbot_arena_conversations | lmsys | {"dataset_info": {"features": [{"name": "question_id", "dtype": "string"}, {"name": "model_a", "dtype": "string"}, {"name": "model_b", "dtype": "string"}, {"name": "winner", "dtype": "string"}, {"name": "judge", "dtype": "string"}, {"name": "conversation_a", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "conversation_b", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "turn", "dtype": "int64"}, {"name": "anony", "dtype": "bool"}, {"name": "language", "dtype": "string"}, {"name": "tstamp", "dtype": "float64"}, {"name": "openai_moderation", "struct": [{"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": "toxic_chat_tag", "struct": [{"name": "roberta-large", "struct": [{"name": "flagged", "dtype": "bool"}, {"name": "probability", "dtype": "float64"}]}, {"name": "t5-large", "struct": [{"name": "flagged", "dtype": "bool"}, {"name": "score", "dtype": "float64"}]}]}], "splits": [{"name": "train", "num_bytes": 81159839, "num_examples": 33000}], "download_size": 41572998, "dataset_size": 81159839}, "license": "cc", "task_categories": ["conversational"], "size_categories": ["10K<n<100K"], "extra_gated_prompt": "Disclaimers and Terms\n- This dataset contains conversations that may be considered unsafe, offensive, or upsetting. It is not intended for training dialogue agents without applying appropriate filtering measures. We are not responsible for any outputs of the models trained on this dataset.\n- Statements or opinions made in this dataset do not reflect the views of researchers or institutions involved in the data collection effort.\n- Users of this data are responsible for ensuring its appropriate use, which includes abiding by any applicable laws and regulations.\n- Users of this data should adhere to the terms of use for a specific model when using its direct outputs.\n- Users of this data agree to not attempt to determine the identity of individuals in this dataset."} | false | null | 2023-09-30T01:04:44 | 374 | 4 | false | 1b6335d42a1d2c7e34870c905d03ab964f7f2bd8 |
Chatbot Arena Conversations Dataset
This dataset contains 33K cleaned conversations with pairwise human preferences.
It is collected from 13K unique IP addresses on the Chatbot Arena from April to June 2023.
Each sample includes a question ID, two model names, their full conversation text in OpenAI API JSON format, the user vote, the anonymized user ID, the detected language tag, the OpenAI moderation API tag, the additional toxic tag, and the timestamp.
To ensure the safe… See the full description on the dataset page: https://huggingface.co/datasets/lmsys/chatbot_arena_conversations. | 1,296 | 105,551 | [
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"library:datasets",
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"library:mlcroissant",
"library:polars",
"arxiv:2306.05685",
"region:us"
] | 2023-07-18T11:57:07 | null | null |
64df8d51a7d1b71134c1fbdf | ticoAg/Chinese-medical-dialogue | ticoAg | {"license": "apache-2.0", "raw csv": "356 MB", "examples": 799743} | false | null | 2023-08-18T15:33:15 | 46 | 4 | false | a8569e1504bd818714cdcabdff668557ed269803 |
Note
process data from Chinese-medical-dialogue-data
单轮医患对话
raw data samples
department
title
ask
answer
心血管科
高血压患者能吃党参吗？
我有高血压这两天女婿来的时候给我拿了些党参泡水喝，您好高血压可以吃党参吗？
高血压病人可以口服党参的。党参有降血脂，降血压的作用，可以彻底消除血液中的垃圾，从而对冠心病以及心血管疾病的患者都有一定的稳定预防工作作用，因此平时口服党参能远离三高的危害。另外党参除了益气养血，降低中枢神经作用，调整消化系统功能，健脾补肺的功能。感谢您的进行咨询，期望我的解释对你有所帮助。
内分泌科
糖尿病还会进行遗传吗？
糖尿病有隔代遗传吗？我妈是糖尿病，很多年了，也没养好，我现在也是，我妹子也是，我儿子现在二十岁，没什么问题，但是以后会不会也得糖尿病啊，真是难过，我现在就已经开始让他控制点吃东西。… See the full description on the dataset page: https://huggingface.co/datasets/ticoAg/Chinese-medical-dialogue. | 566 | 1,387 | [
"license:apache-2.0",
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 2023-08-18T15:25:05 | 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 | 645 | 4 | 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. | 3,591 | 220,936 | [
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] | 2023-09-20T06:33:44 | null | null |
651fbfa3be34a5f2cf6871d1 | a686d380/h-corpus-2023 | a686d380 | {"viewer": false, "language": ["zh"]} | false | null | 2023-10-06T08:38:36 | 160 | 4 | false | 770d79e988706a68df8e2bc9dc37348e109ded59 | 经过清洗和去重过的H小说
共205,028篇文章，解压后17.0 GB
仅用于科学研究！
| 624 | 2,461 | [
"language:zh",
"region:us"
] | 2023-10-06T08:04:51 | null | null |
652c26161a3250bbfe6b96d0 | AI4Math/MathVista | AI4Math | {"annotations_creators": ["expert-generated", "found"], "language_creators": ["expert-generated", "found"], "language": ["en", "zh", "fa"], "license": "cc-by-sa-4.0", "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["multiple-choice", "question-answering", "visual-question-answering", "text-classification"], "task_ids": ["multiple-choice-qa", "closed-domain-qa", "open-domain-qa", "visual-question-answering", "multi-class-classification"], "paperswithcode_id": "mathvista", "pretty_name": "MathVista", "tags": ["multi-modal-qa", "math-qa", "figure-qa", "geometry-qa", "math-word-problem", "textbook-qa", "vqa", "arithmetic-reasoning", "statistical-reasoning", "algebraic-reasoning", "geometry-reasoning", "numeric-common-sense", "scientific-reasoning", "logical-reasoning", "geometry-diagram", "synthetic-scene", "chart", "plot", "scientific-figure", "table", "function-plot", "abstract-scene", "puzzle-test", "document-image", "medical-image", "mathematics", "science", "chemistry", "biology", "physics", "engineering", "natural-science"], "configs": [{"config_name": "default", "data_files": [{"split": "testmini", "path": "data/testmini-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "pid", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "string"}, {"name": "decoded_image", "dtype": "image"}, {"name": "choices", "sequence": "string"}, {"name": "unit", "dtype": "string"}, {"name": "precision", "dtype": "float64"}, {"name": "answer", "dtype": "string"}, {"name": "question_type", "dtype": "string"}, {"name": "answer_type", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "category", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "grade", "dtype": "string"}, {"name": "img_height", "dtype": "int64"}, {"name": "img_width", "dtype": "int64"}, {"name": "language", "dtype": "string"}, {"name": "skills", "sequence": "string"}, {"name": "source", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "task", "dtype": "string"}]}, {"name": "query", "dtype": "string"}], "splits": [{"name": "testmini", "num_bytes": 142635198, "num_examples": 1000}, {"name": "test", "num_bytes": 648291350.22, "num_examples": 5141}], "download_size": 885819490, "dataset_size": 790926548.22}} | false | null | 2024-02-11T23:09:05 | 139 | 4 | false | 2b6ad69445fbb5695c9b165475e8decdbeb97747 |
Dataset Card for MathVista
Dataset Description
Paper Information
Dataset Examples
Leaderboard
Dataset Usage
Data Downloading
Data Format
Data Visualization
Data Source
Automatic Evaluation
License
Citation
Dataset Description
MathVista is a consolidated Mathematical reasoning benchmark within Visual contexts. It consists of three newly created datasets, IQTest, FunctionQA, and PaperQA, which address the missing visual domains and are tailored to evaluate… See the full description on the dataset page: https://huggingface.co/datasets/AI4Math/MathVista. | 11,663 | 121,744 | [
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"medical-image",
"mathematics",
"science",
"chemistry",
"biology",
"physics",
"engineering",
"natural-science"
] | 2023-10-15T17:49:10 | mathvista | null |
652d102ebba7176a8bab4a90 | THUDM/AgentInstruct | THUDM | {"configs": [{"config_name": "default", "data_files": [{"split": "os", "path": "data/os-*"}, {"split": "db", "path": "data/db-*"}, {"split": "alfworld", "path": "data/alfworld-*"}, {"split": "webshop", "path": "data/webshop-*"}, {"split": "kg", "path": "data/kg-*"}, {"split": "mind2web", "path": "data/mind2web-*"}]}], "dataset_info": {"features": [{"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "loss", "dtype": "bool"}, {"name": "value", "dtype": "string"}]}, {"name": "id", "dtype": "string"}], "splits": [{"name": "os", "num_bytes": 660245, "num_examples": 195}, {"name": "db", "num_bytes": 1436655, "num_examples": 538}, {"name": "alfworld", "num_bytes": 1223363, "num_examples": 336}, {"name": "webshop", "num_bytes": 1602648, "num_examples": 351}, {"name": "kg", "num_bytes": 2960010, "num_examples": 324}, {"name": "mind2web", "num_bytes": 159590, "num_examples": 122}], "download_size": 1255385, "dataset_size": 8042511}, "language": ["en"], "pretty_name": "AgentInstruct"} | false | null | 2023-10-23T12:36:19 | 212 | 4 | false | e252cf78ced8a0ea5f62cfd591784cdbbddbac8a |
AgentInstruct Dataset
🤗 [Models] • 💻 [Github Repo] • 📌 [Project Page] • 📃 [Paper]
AgentInstruct is a meticulously curated dataset featuring 1,866 high-quality interactions, designed to enhance AI agents across six diverse real-world tasks, leveraging innovative methods like Task Derivation and Self-Instruct.
🔍 CoT - Harness the power of ReAct, offering detailed thought explanations for each action, ensuring an intricate understanding of the model's decision-making… See the full description on the dataset page: https://huggingface.co/datasets/THUDM/AgentInstruct. | 702 | 15,896 | [
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"arxiv:2310.12823",
"region:us"
] | 2023-10-16T10:27:58 | null | null |
657f287eaf1698aaac668af5 | Gourieff/ReActor | Gourieff | {"license": "mit", "viewer": false} | false | null | 2025-03-23T18:44:36 | 97 | 4 | false | fbe9819ac83edb92c620287a3155f198bf362987 |
ReActor Assets
The Fast and Simple Face Swap Extension
sd-webui-reactor
comfyui-reactor-node
Models
file
source
license
buffalo_l.zip
DeepInsight
codeformer-v0.1.0.pth
sczhou
GFPGANv1.3.pth
TencentARC
GFPGANv1.4.pth
TencentARC
GPEN-BFR-512.onnx
harisreedhar
RestoreFormer_PP.onnx
netrunner.exe
inswapper_128.onnx
DeepInsight
inswapper_128_fp16.onnx
Hillobar
| 128,620 | 688,311 | [
"license:mit",
"region:us"
] | 2023-12-17T16:57:34 | null | null |
65a3043fbfaec7e7ca07c57d | jtatman/python-code-dataset-500k | jtatman | {"dataset_info": {"features": [{"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "system", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 922266591, "num_examples": 559515}], "download_size": 346944286, "dataset_size": 922266591}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "mit", "task_categories": ["text-generation"], "tags": ["instructional", "python", "code"], "pretty_name": "github_python", "size_categories": ["100K<n<1M"]} | false | null | 2024-01-23T21:39:13 | 49 | 4 | false | 060ad3df88a6ba5f5546c622652290f38e73ceba |
Attention: This dataset is a summary and reformat pulled from github code.
You should make your own assumptions based on this.
In fact, there is another dataset I formed through parsing that addresses several points:
out of 500k python related items, most of them are python-ish, not pythonic
the majority of the items here contain excessive licensing inclusion of original code
the items here are sometimes not even python but have references
There's a whole lot of gpl summaries… See the full description on the dataset page: https://huggingface.co/datasets/jtatman/python-code-dataset-500k. | 1,401 | 6,145 | [
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"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"instructional",
"python",
"code"
] | 2024-01-13T21:44:31 | 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 | 506 | 4 | 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. | 6,573 | 42,320 | [
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"arxiv:2306.05685",
"region:us",
"synthetic",
"SQL",
"text-to-SQL",
"code"
] | 2024-03-21T16:04:08 | null | null |
663b7fd5a4152b77b637ba11 | TIGER-Lab/MMLU-Pro | TIGER-Lab | {"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering"], "pretty_name": "MMLU-Pro", "tags": ["evaluation"], "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "options", "sequence": "string"}, {"name": "answer", "dtype": "string"}, {"name": "answer_index", "dtype": "int64"}, {"name": "cot_content", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "src", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 61143, "num_examples": 70}, {"name": "test", "num_bytes": 8715104, "num_examples": 12032}], "download_size": 62884340, "dataset_size": 8776247}} | false | null | 2024-11-27T16:03:40 | 336 | 4 | false | 014c8cf520218fc7f1ecec95c691a83ae5b8ba14 |
MMLU-Pro Dataset
MMLU-Pro dataset is a more robust and challenging massive multi-task understanding dataset tailored to more rigorously benchmark large language models' capabilities. This dataset contains 12K complex questions across various disciplines.
|Github | 🏆Leaderboard | 📖Paper |
🚀 What's New
[2024.10.16] We have added Gemini-1.5-Flash-002, Gemini-1.5-Pro-002, Jamba-1.5-Large, Llama-3.1-Nemotron-70B-Instruct-HF and Ministral-8B-Instruct-2410 to our… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro. | 43,225 | 536,841 | [
"task_categories:question-answering",
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"arxiv:2406.01574",
"doi:10.57967/hf/2439",
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] | 2024-05-08T13:36:21 | null | null |
666513f121aa69e38699e6d3 | UCSC-VLAA/MedTrinity-25M | UCSC-VLAA | {"language": ["en"], "size_categories": ["10M<n<100M"], "task_categories": ["question-answering"], "dataset_info": [{"config_name": "25M_full", "features": [{"name": "id", "dtype": "string"}, {"name": "file_name", "dtype": "string"}, {"name": "caption", "dtype": "string"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 25234102586, "num_examples": 24760560}], "download_size": 7353330306, "dataset_size": 25234102586}, {"config_name": "default", "features": [{"name": "image", "dtype": "image"}, {"name": "id", "dtype": "string"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4781050841.25, "num_examples": 161630}], "download_size": 8300138103, "dataset_size": 4781050841.25}], "configs": [{"config_name": "25M_full", "data_files": [{"split": "train", "path": "25M_full/train-*"}]}, {"config_name": "25M_demo", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["medical"]} | false | null | 2024-10-11T00:47:43 | 133 | 4 | false | 89e5c684794e5c4cc1af9e8f1a7798af7c937dbf |
Tutorial of using Medtrinity-25M
MedTrinity-25M, a comprehensive, large-scale multimodal dataset for medicine, covering over 25 million images across 10 modalities, with multigranular annotations for more than 65 diseases. These enriched annotations encompass both global textual information, such as disease/lesion type, modality, region-specific descriptions, and inter-regional relationships, as well as detailed local annotations for regions of interest (ROIs), including… See the full description on the dataset page: https://huggingface.co/datasets/UCSC-VLAA/MedTrinity-25M. | 2,631 | 10,088 | [
"task_categories:question-answering",
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"modality:image",
"modality:text",
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"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2408.02900",
"region:us",
"medical"
] | 2024-06-09T02:31:13 | null | null |
666866f02ae95a428c6da904 | nkp37/OpenVid-1M | nkp37 | {"license": "cc-by-4.0", "task_categories": ["text-to-video"], "language": ["en"], "tags": ["text-to-video", "Video Generative Model Training", "Text-to-Video Diffusion Model Training", "prompts"], "pretty_name": "OpenVid-1M", "size_categories": ["1M<n<10M"]} | false | null | 2025-03-24T03:56:16 | 188 | 4 | false | 6559ef027c35492ae91db6cabb2126b9f15cd561 |
Summary
This is the dataset proposed in our paper [ICLR 2025] OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation.
OpenVid-1M is a high-quality text-to-video dataset designed for research institutions to enhance video quality, featuring high aesthetics, clarity, and resolution. It can be used for direct training or as a quality tuning complement to other video datasets.
All videos in the OpenVid-1M dataset have resolutions of at least 512×512.… See the full description on the dataset page: https://huggingface.co/datasets/nkp37/OpenVid-1M. | 27,124 | 196,366 | [
"task_categories:text-to-video",
"language:en",
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"region:us",
"text-to-video",
"Video Generative Model Training",
"Text-to-Video Diffusion Model Training",
"prompts"
] | 2024-06-11T15:02:08 | null | null |
667c231902ffd4993eef43a5 | joujiboi/japanese-anime-speech-v2 | joujiboi | {"language": ["ja"], "license": "gpl", "size_categories": ["100K<n<1M"], "task_categories": ["automatic-speech-recognition"], "pretty_name": "Japanese-Anime-Speech-V2", "dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "sfw", "num_bytes": 19174765803.112, "num_examples": 271788}, {"name": "nsfw", "num_bytes": 2864808426.209, "num_examples": 20849}], "download_size": 24379492733, "dataset_size": 22039574229.321}, "tags": ["japanese", "anime", "speech", "\u65e5\u672c\u8a9e", "audio-text", "asr", "whisper", "voice"], "configs": [{"config_name": "default", "data_files": [{"split": "sfw", "path": "data/sfw-*"}, {"split": "nsfw", "path": "data/nsfw-*"}]}]} | false | null | 2024-12-18T18:47:26 | 81 | 4 | false | 1dea3fb40e0b1a224c011bab3efcde55893bf742 |
Japanese Anime Speech Dataset V2
日本語はこちら
japanese-anime-speech-v2 is an audio-text dataset designed for training automatic speech recognition models.
The dataset comprises 292,637 audio clips and their corresponding transcriptions from various visual novels.
This dataset is not an expanded version of japanese-anime-speech-v1.
For that reason, much of the audio from japanese-anime-speech-v1 is not included in this dataset.
The goal of this dataset is to increase the accuracy of… See the full description on the dataset page: https://huggingface.co/datasets/joujiboi/japanese-anime-speech-v2. | 1,632 | 19,581 | [
"task_categories:automatic-speech-recognition",
"language:ja",
"license:gpl",
"size_categories:100K<n<1M",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us",
"japanese",
"anime",
"speech",
"日本語",
"audio-text",
"asr",
"whisper",
"voice"
] | 2024-06-26T14:18:01 | null | null |
66c582fe30010c0f2bba4176 | Team-ACE/ToolACE | Team-ACE | {"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en", "zh"], "tags": ["synthetic", "tools"], "size_categories": ["10K<n<100K"]} | false | null | 2024-09-04T02:37:59 | 64 | 4 | false | 6bda777c88d21e5a204703c1ee45597a8fa4f734 |
ToolACE
ToolACE is an automatic agentic pipeline designed to generate Accurate, Complex, and divErse tool-learning data.
ToolACE leverages a novel self-evolution synthesis process to curate a comprehensive API pool of 26,507 diverse APIs.
Dialogs are further generated through the interplay among multiple agents, guided by a formalized thinking process.
To ensure data accuracy, we implement a dual-layer verification system combining rule-based and model-based checks.
More… See the full description on the dataset page: https://huggingface.co/datasets/Team-ACE/ToolACE. | 768 | 3,627 | [
"task_categories:text-generation",
"language:en",
"language:zh",
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"arxiv:2409.00920",
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"synthetic",
"tools"
] | 2024-08-21T06:02:38 | null | null |
6741517a022caab2f6d0d92c | allenai/dolmino-mix-1124 | allenai | {"license": "odc-by", "task_categories": ["text-generation"], "pretty_name": "DOLMino Mix (November 2024)", "size_categories": ["100M<n<1B"], "language": ["en"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/**/*"}]}, {"config_name": "dclm", "data_files": [{"split": "train", "path": "data/dclm/**/*"}]}, {"config_name": "flan", "data_files": [{"split": "train", "path": "data/flan/*"}]}, {"config_name": "pes2o", "data_files": [{"split": "train", "path": "data/pes2o/*"}]}, {"config_name": "stackexchange", "data_files": [{"split": "train", "path": "data/stackexchange/*"}]}, {"config_name": "wiki", "data_files": [{"split": "train", "path": "data/wiki/*"}]}, {"config_name": "stackexchange", "data_files": [{"split": "train", "path": "data/stackexchange/*"}]}, {"config_name": "math", "data_files": [{"split": "train", "path": "data/math/**/*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "added", "dtype": "string"}, {"name": "created", "dtype": "string"}]}} | false | null | 2024-12-17T23:01:58 | 44 | 4 | false | d9a228da2db18ea90030c9b2698bedfa5ee315f4 |
DOLMino dataset mix for OLMo2 stage 2 annealing training.
Mixture of high-quality data used for the second stage of OLMo2 training.
Source Sizes
Name
Category
Tokens
Bytes (uncompressed)
Documents
License
DCLM
HQ Web Pages
752B
4.56TB
606M
CC-BY-4.0
Flan
HQ Web Pages
17.0B
98.2GB
57.3M
ODC-BY
Pes2o
STEM Papers
58.6B
413GB
38.8M
ODC-BY
Wiki
Encyclopedic
3.7B
16.2GB
6.17M
ODC-BY
StackExchange
CodeText
1.26B
7.72GB
2.48M
CC-BY-SA-{2.5, 3.0, 4.0}… See the full description on the dataset page: https://huggingface.co/datasets/allenai/dolmino-mix-1124. | 30,291 | 112,969 | [
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] | 2024-11-23T03:52:26 | null | null |
67449661149efb6edaa63b98 | HuggingFaceTB/finemath | HuggingFaceTB | {"license": "odc-by", "dataset_info": [{"config_name": "finemath-3plus", "features": [{"name": "url", "dtype": "string"}, {"name": "fetch_time", "dtype": "int64"}, {"name": "content_mime_type", "dtype": "string"}, {"name": "warc_filename", "dtype": "string"}, {"name": "warc_record_offset", "dtype": "int32"}, {"name": "warc_record_length", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "token_count", "dtype": "int32"}, {"name": "char_count", "dtype": "int32"}, {"name": "metadata", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}, {"name": "crawl", "dtype": "string"}, {"name": "snapshot_type", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "language_score", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 137764105388.93857, "num_examples": 21405610}], "download_size": 65039196945, "dataset_size": 137764105388.93857}, {"config_name": "finemath-4plus", "features": [{"name": "url", "dtype": "string"}, {"name": "fetch_time", "dtype": "int64"}, {"name": "content_mime_type", "dtype": "string"}, {"name": "warc_filename", "dtype": "string"}, {"name": "warc_record_offset", "dtype": "int32"}, {"name": "warc_record_length", "dtype": "int32"}, {"name": "text", "dtype": "string"}, {"name": "token_count", "dtype": "int32"}, {"name": "char_count", "dtype": "int32"}, {"name": "metadata", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}, {"name": "crawl", "dtype": "string"}, {"name": "snapshot_type", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "language_score", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 39101488149.09091, "num_examples": 6699493}], "download_size": 18365184633, "dataset_size": 39101488149.09091}, {"config_name": "infiwebmath-3plus", "features": [{"name": "url", "dtype": "string"}, {"name": "metadata", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}, {"name": "token_count", "dtype": "int64"}, {"name": "char_count", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 96485696853.10182, "num_examples": 13882669}], "download_size": 46808660851, "dataset_size": 96485696853.10182}, {"config_name": "infiwebmath-4plus", "features": [{"name": "url", "dtype": "string"}, {"name": "metadata", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "int_score", "dtype": "int64"}, {"name": "token_count", "dtype": "int64"}, {"name": "char_count", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 40002719500.1551, "num_examples": 6296212}], "download_size": 19234328998, "dataset_size": 40002719500.1551}], "configs": [{"config_name": "finemath-3plus", "data_files": [{"split": "train", "path": "finemath-3plus/train-*"}]}, {"config_name": "finemath-4plus", "data_files": [{"split": "train", "path": "finemath-4plus/train-*"}]}, {"config_name": "infiwebmath-3plus", "data_files": [{"split": "train", "path": "infiwebmath-3plus/train-*"}]}, {"config_name": "infiwebmath-4plus", "data_files": [{"split": "train", "path": "infiwebmath-4plus/train-*"}]}]} | false | null | 2025-02-06T10:31:11 | 296 | 4 | false | e92b25a616738fe95dc186b64dfb19f9c8525594 |
📐 FineMath
What is it?
📐 FineMath consists of 34B tokens (FineMath-3+) and 54B tokens (FineMath-3+ with InfiMM-WebMath-3+) of mathematical educational content filtered from CommonCrawl. To curate this dataset, we trained a mathematical content classifier using annotations generated by LLama-3.1-70B-Instruct. We used the classifier to retain only the most educational mathematics content, focusing on clear explanations and step-by-step problem solving rather than… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/finemath. | 10,382 | 69,486 | [
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"doi:10.57967/hf/3847",
"region:us"
] | 2024-11-25T15:23:13 | null | null |
674997be514ea5a281dc08f5 | CohereForAI/include-base-44 | CohereForAI | {"language": ["sq", "ar", "hy", "az", "be", "bn", "eu", "bg", "tr", "hr", "nl", "fa", "es", "et", "fi", "fr", "de", "el", "ka", "he", "hi", "hu", "id", "it", "ja", "kk", "ko", "lt", "ml", "ms", "ne", "pl", "pt", "ru", "ta", "tl", "te", "uk", "ur", "uz", "vi", "zh", "sr", "mk"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["text2text-generation", "multiple-choice"], "dataset_info": [{"config_name": "Albanian", "features": [{"name": "language", "dtype": "string"}, {"name": "country", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "regional_feature", "dtype": "string"}, {"name": "level", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "option_a", "dtype": "string"}, {"name": "option_b", "dtype": "string"}, {"name": "option_c", "dtype": "string"}, {"name": "option_d", "dtype": "string"}, {"name": 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"validation", "path": "Vietnamese/validation-*"}]}], "tags": ["chemistry", "biology", "legal", "music", "finance", "medical", "climate", "art", "code"]} | false | null | 2025-03-05T15:31:23 | 36 | 4 | false | 47b1aa788c242415709c42a870dd4d57cb70a878 |
INCLUDE-base (44 languages)
Dataset Description
Paper: http://arxiv.org/abs/2411.19799
Dataset Summary
INCLUDE is a comprehensive knowledge- and reasoning-centric benchmark across 44 languages that evaluates multilingual LLMs for performance in the actual language environments where they would be deployed.
It contains 22,637 4-option multiple-choice-questions (MCQ) extracted from academic and professional exams, covering 57 topics, including regional… See the full description on the dataset page: https://huggingface.co/datasets/CohereForAI/include-base-44. | 3,053 | 8,447 | [
"task_categories:text2text-generation",
"task_categories:multiple-choice",
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"language:ar",
"language:hy",
"language:az",
"language:be",
"language:bn",
"language:eu",
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"language:fa",
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"language:et",
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"language:fr",
"language:de",
"language:el",
"language:ka",
"language:he",
"language:hi",
"language:hu",
"language:id",
"language:it",
"language:ja",
"language:kk",
"language:ko",
"language:lt",
"language:ml",
"language:ms",
"language:ne",
"language:pl",
"language:pt",
"language:ru",
"language:ta",
"language:tl",
"language:te",
"language:uk",
"language:ur",
"language:uz",
"language:vi",
"language:zh",
"language:sr",
"language:mk",
"license:apache-2.0",
"size_categories:10K<n<100K",
"modality:text",
"arxiv:2411.19799",
"region:us",
"chemistry",
"biology",
"legal",
"music",
"finance",
"medical",
"climate",
"art",
"code"
] | 2024-11-29T10:30:22 | null | null |
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Dataset Summary
Global-MMLU 🌍 is a multilingual evaluation set spanning 42 languages, including English. This dataset combines machine translations for MMLU questions along with professional translations and crowd-sourced post-edits.
It also includes cultural sensitivity annotations for a subset of the questions (2850 questions per language) and classifies them as Culturally Sensitive (CS) 🗽 or Culturally Agnostic (CA) ⚖️. These annotations were collected as part of an open… See the full description on the dataset page: https://huggingface.co/datasets/CohereForAI/Global-MMLU. | 19,309 | 48,498 | [
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67511c0b9c31de7f912002f9 | Maxwell-Jia/AIME_2024 | Maxwell-Jia | {"license": "mit", "task_categories": ["text-generation"], "language": ["en"], "tags": ["explanation-generation"], "pretty_name": "AIME 2024 Dataset", "size_categories": ["n<1K"], "dataset_info": {"config_name": "default"}, "data_files": [{"split": "train", "path": "aime_2024_problems.parquet"}]} | false | null | 2025-02-18T06:39:19 | 32 | 4 | false | 8d88b2876a82a080e2f172cc9b25d0d9d2cb4792 |
AIME 2024 Dataset
Dataset Description
This dataset contains problems from the American Invitational Mathematics Examination (AIME) 2024. AIME is a prestigious high school mathematics competition known for its challenging mathematical problems.
Dataset Details
Format: JSONL
Size: 30 records
Source: AIME 2024 I & II
Language: English
Data Fields
Each record contains the following fields:
ID: Problem identifier (e.g., "2024-I-1" represents Problem 1… See the full description on the dataset page: https://huggingface.co/datasets/Maxwell-Jia/AIME_2024. | 8,787 | 13,617 | [
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676f70968756741d47c691df | FreedomIntelligence/medical-o1-verifiable-problem | FreedomIntelligence | {"license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "language": ["en"], "tags": ["medical", "biology"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "medical_o1_verifiable_problem.json"}]}]} | false | null | 2024-12-30T02:56:46 | 83 | 4 | false | 46d5175eb74fdef3516d51d52e8c40db04bbdf35 |
Introduction
This dataset features open-ended medical problems designed to improve LLMs' medical reasoning. Each entry includes a open-ended question and a ground-truth answer based on challenging medical exams. The verifiable answers enable checking LLM outputs, refining their reasoning processes.
For details, see our paper and GitHub repository.
Citation
If you find our data useful, please consider citing our work!… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-verifiable-problem. | 1,234 | 2,594 | [
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] | 2024-12-28T03:29:26 | null | null |
677d1ce4afd8031632cb203e | bigcode/starcoder2data-extras | bigcode | {"dataset_info": [{"config_name": "arxiv", "features": [{"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 89223183645, "num_examples": 1558306}], "download_size": 40911186876, "dataset_size": 89223183645}, {"config_name": "documentation", "features": [{"name": "project", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5421472234, "num_examples": 59733}], "download_size": 1853451922, "dataset_size": 5421472234}, {"config_name": "ir_cpp", "features": [{"name": "__index_level_0__", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 102081135272, "num_examples": 2916655}], "download_size": 26047978422, "dataset_size": 102081135272}, {"config_name": "ir_low_resource", 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StarCoder2 Extras
This is the dataset of extra sources (besides Stack v2 code data) used to train the StarCoder2 family of models. It contains the following subsets:
Kaggle (kaggle): Kaggle notebooks from Meta-Kaggle-Code dataset, converted to scripts and prefixed with information on the Kaggle datasets used in the notebook. The file headers have a similar format to Jupyter Structured but the code content is only one single script.
StackOverflow (stackoverflow): stackoverflow… See the full description on the dataset page: https://huggingface.co/datasets/bigcode/starcoder2data-extras. | 125 | 128 | [
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67980358a29a934c083067cf | AymanTarig/function-calling-v0.2-with-r1-cot | AymanTarig | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "query", "dtype": "string"}, {"name": "answers", "dtype": "string"}, {"name": "tools", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "r1-gen", "dtype": "string"}, {"name": "think", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 353121599.00783336, "num_examples": 57970}], "download_size": 140431591, "dataset_size": 353121599.00783336}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | false | null | 2025-02-03T00:21:27 | 36 | 4 | false | fd9f547c2c85bf83792a38c6c935405b0cef0e3f | This dataset is a modified version of Salesforce/xlam-function-calling-60k, incorporating reasoning chains generated by deepseek-ai/DeepSeek-R1-Distill-Llama-8B.
| 356 | 1,087 | [
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] | 2025-01-27T22:06:16 | null | null |
Subsets and Splits