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2025-06-10 23:31:57
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2025-06-10 23:31:42
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6837854ff36dbe5068b5d602
open-thoughts/OpenThoughts3-1.2M
open-thoughts
{"dataset_info": {"features": [{"name": "difficulty", "dtype": "int64"}, {"name": "source", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 59763369750, "num_examples": 1200000}], "download_size": 28188197544, "dataset_size": 59763369750}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "apache-2.0", "task_categories": ["text-generation"], "tags": ["reasoning", "mathematics", "code", "science"], "library_name": "datasets"}
false
null
2025-06-09T16:14:06
78
78
false
61bcf9d4eb38b30295efc2021227a63cc5bb34c8
paper | dataset | model [!NOTE] We have released a paper for OpenThoughts! See our paper here. OpenThoughts3-1.2M Open-source state-of-the-art reasoning dataset with 1.2M rows. 🚀 OpenThoughts3-1.2M is the third iteration in our line of OpenThoughts datasets, building on our previous OpenThoughts-114k and OpenThoughts2-1M. This time around, we scale even further and generate our dataset in a much more systematic way -- OpenThoughts3-1.2M is the result of a… See the full description on the dataset page: https://huggingface.co/datasets/open-thoughts/OpenThoughts3-1.2M.
5,794
5,794
[ "task_categories:text-generation", "license:apache-2.0", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2506.04178", "region:us", "reasoning", "mathematics", "code", "science" ]
2025-05-28T21:51:11
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
7,898
68
false
68ba7694e23014788dcc8ab5afe613824f45a05c
🧠 Awesome ChatGPT Prompts [CSV dataset] This is a Dataset Repository of Awesome ChatGPT Prompts View All Prompts on GitHub License CC-0
22,062
179,900
[ "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
683fa649ee7dce90f5aafa46
a-m-team/AM-DeepSeek-R1-0528-Distilled
a-m-team
{"task_categories": ["text-generation"], "language": ["en", "zh"], "tags": ["reasoning"], "size_categories": ["1M<n<10M"]}
false
null
2025-06-09T14:42:53
46
46
false
8d94d36259328de72f619f2d42ea3fd13098d007
📘 Dataset Summary This dataset is a high-quality reasoning corpus distilled from DeepSeek-R1-0528, an improved version of the DeepSeek-R1 large language model. Compared to its initial release, DeepSeek-R1-0528 demonstrates significant advances in reasoning, instruction following, and multi-turn dialogue. Motivated by these improvements, we collected and distilled a diverse set of 2.6 million queries across multiple domains, using DeepSeek-R1-0528 as the teacher. A notable… See the full description on the dataset page: https://huggingface.co/datasets/a-m-team/AM-DeepSeek-R1-0528-Distilled.
3,215
3,215
[ "task_categories:text-generation", "language:en", "language:zh", "size_categories:1M<n<10M", "region:us", "reasoning" ]
2025-06-04T01:50:01
null
null
683596e3bb729b5955ef0fac
yandex/yambda
yandex
{"license": "apache-2.0", "tags": ["recsys", "retrieval", "dataset"], "pretty_name": "Yambda-5B", "size_categories": ["1B<n<10B"], "configs": [{"config_name": "flat-50m", "data_files": ["flat/50m/multi_event.parquet"]}, {"config_name": "flat-500m", "data_files": ["flat/500m/multi_event.parquet"]}, {"config_name": "flat-5b", "data_files": ["flat/5b/multi_event.parquet"]}]}
false
null
2025-06-06T13:13:37
155
45
false
7ec47287e3a002eab8f9f9b64efaf4bed52ce44f
Yambda-5B — A Large-Scale Multi-modal Dataset for Ranking And Retrieval Industrial-scale music recommendation dataset with organic/recommendation interactions and audio embeddings 📌 Overview • 🔑 Key Features • 📊 Statistics • 📝 Format • 🏆 Benchmark • ⬇️ Download • ❓ FAQ Overview The Yambda-5B dataset is a large-scale open database comprising 4.79 billion user-item interactions collected from 1 million users and spanning 9.39 million tracks. The dataset includes… See the full description on the dataset page: https://huggingface.co/datasets/yandex/yambda.
42,766
42,766
[ "license:apache-2.0", "size_categories:1B<n<10B", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2505.22238", "region:us", "recsys", "retrieval", "dataset" ]
2025-05-27T10:41:39
null
null
68465f1ba516bd14fc146e1f
nvidia/Nemotron-Personas
nvidia
{"license": "cc-by-4.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["synthetic", "personas", "NVIDIA"], "size_categories": ["100K<n<1M"]}
false
null
2025-06-09T18:21:17
41
41
false
65887f26ae478d7d2df68438b6c10d58d037b76d
Nemotron-Personas: Synthetic Personas Aligned to Real-World Distributions A compound AI approach to personas grounded in real-world distributions Dataset Overview Nemotron-Personas is an open-source (CC BY 4.0) dataset of synthetically-generated personas grounded in real-world demographic, geographic and personality trait distributions to capture the diversity and richness of the population. It is the first dataset of its kind aligned with statistics for names… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Personas.
962
962
[ "task_categories:text-generation", "language:en", "license:cc-by-4.0", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "synthetic", "personas", "NVIDIA" ]
2025-06-09T04:12:11
null
null
68127daac6370caf375aadd5
Hcompany/WebClick
Hcompany
{"language": ["en"], "license": "apache-2.0", "task_categories": ["visual-document-retrieval"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "instruction", "dtype": "string"}, {"name": "bbox", "sequence": "float64"}, {"name": "bucket", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 334903619, "num_examples": 1639}], "download_size": 334903619, "dataset_size": 334903619}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "test*"}]}]}
false
null
2025-06-09T16:18:15
46
39
false
9482a7d5aaa8f4cd5d28d9ed0c8e0c48d20b1e4a
WebClick: A Multimodal Localization Benchmark for Web-Navigation Models We introduce WebClick, a high-quality benchmark dataset for evaluating navigation and localization capabilities of multimodal models and agents in Web environments. WebClick features 1,639 English-language web screenshots from over 100 websites paired with precisely annotated natural-language instructions and pixel-level click targets, in the same format as the widely-used screenspot benchmark.… See the full description on the dataset page: https://huggingface.co/datasets/Hcompany/WebClick.
4,235
4,241
[ "task_categories:visual-document-retrieval", "language:en", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2401.13919", "arxiv:2506.02865", "arxiv:2410.23218", "arxiv:2502.13923", "arxiv:2501.12326", "region:us" ]
2025-04-30T19:44:42
null
null
6820fb77b82e61bb50999662
open-r1/Mixture-of-Thoughts
open-r1
{"dataset_info": [{"config_name": "all", "features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "num_tokens", "dtype": "int64"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7062819826.825458, "num_examples": 349317}], "download_size": 3077653717, "dataset_size": 7062819826.825458}, {"config_name": "code", "features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "num_tokens", "dtype": "int64"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3872656251.3167396, "num_examples": 83070}], "download_size": 1613338604, "dataset_size": 3872656251.3167396}, {"config_name": "math", "features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "num_tokens", "dtype": "int64"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1599028646, "num_examples": 93733}], "download_size": 704448153, "dataset_size": 1599028646}, {"config_name": "science", "features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "num_tokens", "dtype": "int64"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1590765326, "num_examples": 172514}], "download_size": 674333812, "dataset_size": 1590765326}], "configs": [{"config_name": "all", "data_files": [{"split": "train", "path": "all/train-*"}]}, {"config_name": "code", "data_files": [{"split": "train", "path": "code/train-*"}]}, {"config_name": "math", "data_files": [{"split": "train", "path": "math/train-*"}]}, {"config_name": "science", "data_files": [{"split": "train", "path": "science/train-*"}]}], "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "Mixture of Thoughts", "size_categories": ["100K<n<1M"]}
false
null
2025-05-26T15:25:56
209
33
false
e55fa28006c0d0ec60fb3547520f775dd42d02cd
Dataset summary Mixture-of-Thoughts is a curated dataset of 350k verified reasoning traces distilled from DeepSeek-R1. The dataset spans tasks in mathematics, coding, and science, and is designed to teach language models to reason step-by-step. It was used in the Open R1 project to train OpenR1-Distill-7B, an SFT model that replicates the reasoning capabilities of deepseek-ai/DeepSeek-R1-Distill-Qwen-7B from the same base model. To load the dataset, run: from datasets import… See the full description on the dataset page: https://huggingface.co/datasets/open-r1/Mixture-of-Thoughts.
31,289
31,336
[ "task_categories:text-generation", "language:en", "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2504.21318", "arxiv:2505.00949", "region:us" ]
2025-05-11T19:33:11
null
null
67c92e867c6308c49ce2e98c
openbmb/Ultra-FineWeb
openbmb
{"configs": [{"config_name": "default", "data_files": [{"split": "en", "path": "data/ultrafineweb_en/*"}, {"split": "zh", "path": "data/ultrafineweb_zh/*"}], "features": [{"name": "content", "dtype": "string"}, {"name": "score", "dtype": "float"}, {"name": "source", "dtype": "string"}]}], "task_categories": ["text-generation"], "language": ["en", "zh"], "pretty_name": "Ultra-FineWeb", "size_categories": ["n>1T"], "license": "apache-2.0"}
false
null
2025-06-06T07:35:23
156
24
false
57df35e37806c5a5cfa7d1ce93b4b0fa10bb34c9
Ultra-FineWeb 📜 Technical Report 📚 Introduction Ultra-FineWeb is a large-scale, high-quality, and efficiently-filtered dataset. We use the proposed efficient verification-based high-quality filtering pipeline to the FineWeb and Chinese FineWeb datasets (source data from Chinese FineWeb-edu-v2, which includes IndustryCorpus2, MiChao, WuDao, SkyPile, WanJuan, ChineseWebText, TeleChat, and CCI3), resulting in the creation of higher-quality Ultra-FineWeb-en… See the full description on the dataset page: https://huggingface.co/datasets/openbmb/Ultra-FineWeb.
35,937
36,530
[ "task_categories:text-generation", "language:en", "language:zh", "license:apache-2.0", "size_categories:1B<n<10B", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2505.05427", "arxiv:2412.04315", "region:us" ]
2025-03-06T05:11:34
null
null
67335bb8f014ee49558ef3fe
PleIAs/common_corpus
PleIAs
{"language": ["en", "fr", "de", "it", "es", "la", "nl", "pl"]}
false
null
2025-06-10T21:40:08
282
19
false
4584307d242e0428cc5222436224767963639269
Common Corpus Full data paper Common Corpus is the largest open and permissible licensed text dataset, comprising 2 trillion tokens (1,998,647,168,282 tokens). It is a diverse dataset, consisting of books, newspapers, scientific articles, government and legal documents, code, and more. Common Corpus has been created by Pleias in association with several partners and contributed in-kind to Current AI initiative. Common Corpus differs from existing open datasets in that it is:… See the full description on the dataset page: https://huggingface.co/datasets/PleIAs/common_corpus.
238,004
482,205
[ "language:en", "language:fr", "language:de", "language:it", "language:es", "language:la", "language:nl", "language:pl", "size_categories:100M<n<1B", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2506.01732", "arxiv:2410.22587", "region:us" ]
2024-11-12T13:44:24
null
null
67f7c881fbae10699039f168
common-pile/comma_v0.1_training_dataset
common-pile
null
false
null
2025-06-06T20:22:29
21
18
false
5afc546db324e7f39f297ba757c9a60547151e7c
Comma v0.1 dataset This repository contains the dataset used to train Comma v0.1-1T and Comma v0.1-2T. It is a slightly modified and consolidated version of the Common Pile v0.1 "filtered" data. If you are looknig for the raw Common Pile v0.1 data, please see this collection. You can learn more about Common Pile in our paper. Mixing rates and token counts The Comma v0.1 models were trained in two stages, a "main" stage and a "cooldown" stage. During each stage, we… See the full description on the dataset page: https://huggingface.co/datasets/common-pile/comma_v0.1_training_dataset.
7,997
12,454
[ "size_categories:100M<n<1B", "format:json", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "arxiv:2506.05209", "region:us" ]
2025-04-10T13:32:49
null
null
6822e8b5ddda5d39df42b951
miriad/miriad-5.8M
miriad
{"dataset_info": {"features": [{"name": "qa_id", "dtype": "string"}, {"name": "paper_id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "paper_url", "dtype": "string"}, {"name": "paper_title", "dtype": "string"}, {"name": "passage_text", "dtype": "string"}, {"name": "passage_position", "dtype": "string"}, {"name": "year", "dtype": "float64"}, {"name": "venue", "dtype": "string"}, {"name": "specialty", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 31604565083, "num_examples": 5821948}], "download_size": 7575545232, "dataset_size": 31604565083}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
false
null
2025-05-15T12:52:18
17
17
false
0bb476ef4acb7c7a5b799b227192c6b7da6253e6
null
418
462
[ "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
2025-05-13T06:37:41
null
null
68328c9f85ebf2e6b1c31d12
MiniMaxAI/SynLogic
MiniMaxAI
{"language": ["en", "zh"], "license": "mit", "task_categories": ["text-generation"], "tags": ["logical reasoning"], "configs": [{"config_name": "easy", "data_files": [{"split": "train", "path": "synlogic_easy/train.parquet"}, {"split": "validation", "path": "synlogic_easy/validation.parquet"}]}, {"config_name": "hard", "data_files": [{"split": "train", "path": "synlogic_hard/train.parquet"}, {"split": "validation", "path": "synlogic_hard/validation.parquet"}]}]}
false
null
2025-06-10T03:02:13
82
16
false
1f9f529cc5c6de6fe1cc7a018185ab4ed25366cb
SynLogic Dataset SynLogic is a comprehensive synthetic logical reasoning dataset designed to enhance logical reasoning capabilities in Large Language Models (LLMs) through reinforcement learning with verifiable rewards. 🐙 GitHub Repo: https://github.com/MiniMax-AI/SynLogic 📜 Paper (arXiv): https://arxiv.org/abs/2505.19641 Dataset Description SynLogic contains 35 diverse logical reasoning tasks with automatic verification capabilities, making it ideal for… See the full description on the dataset page: https://huggingface.co/datasets/MiniMaxAI/SynLogic.
1,152
1,152
[ "task_categories:text-generation", "language:en", "language:zh", "license:mit", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2505.19641", "region:us", "logical reasoning" ]
2025-05-25T03:21:03
null
null
66212f29fb07c3e05ad0432e
HuggingFaceFW/fineweb
HuggingFaceFW
{"license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "FineWeb", "size_categories": ["n>1T"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/*/*"}]}, {"config_name": "sample-10BT", "data_files": [{"split": "train", "path": "sample/10BT/*"}]}, {"config_name": "sample-100BT", "data_files": [{"split": "train", "path": "sample/100BT/*"}]}, {"config_name": "sample-350BT", "data_files": [{"split": "train", "path": "sample/350BT/*"}]}, {"config_name": "CC-MAIN-2024-51", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-51/*"}]}, {"config_name": "CC-MAIN-2024-46", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-46/*"}]}, {"config_name": "CC-MAIN-2024-42", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-42/*"}]}, {"config_name": "CC-MAIN-2024-38", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-38/*"}]}, {"config_name": "CC-MAIN-2024-33", "data_files": [{"split": 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false
null
2025-01-31T14:10:44
2,191
14
false
0f039043b23fe1d4eed300b504aa4b4a68f1c7ba
🍷 FineWeb 15 trillion tokens of the finest data the 🌐 web has to offer What is it? The 🍷 FineWeb dataset consists of more than 15T tokens of cleaned and deduplicated english web data from CommonCrawl. The data processing pipeline is optimized for LLM performance and ran on the 🏭 datatrove library, our large scale data processing library. 🍷 FineWeb was originally meant to be a fully open replication of 🦅 RefinedWeb, with a release of the full dataset under… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb.
371,328
3,649,246
[ "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
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
366
13
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.
11,479
64,904
[ "language:en", "arxiv:2311.12983", "region:us" ]
2023-10-20T07:06:54
null
67ea026a0e7c42eb4b4da945
JokerJan/MMR-VBench
JokerJan
{"dataset_info": {"features": [{"name": "video", "dtype": "string"}, {"name": "videoType", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "options", "sequence": "string"}, {"name": "correctAnswer", "dtype": "string"}, {"name": "abilityType_L2", "dtype": "string"}, {"name": "abilityType_L3", "dtype": "string"}, {"name": "question_idx", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 1135911, "num_examples": 1257}], "download_size": 586803, "dataset_size": 1135911}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "task_categories": ["video-text-to-text"]}
false
null
2025-06-05T02:54:51
15
13
false
fded5eca0a342b7b50cd74218666aaa4af939cdd
MMR-V: Can MLLMs Think with Video? A Benchmark for Multimodal Deep Reasoning in Videos 📝 Paper | 💻 Code | 🏠 Homepage 👀 MMR-V Data Card ("Think with Video") The sequential structure of videos poses a challenge to the ability of multimodal large language models (MLLMs) to 🕵️locate multi-frame evidence and conduct multimodal reasoning. However, existing video benchmarks mainly focus on understanding tasks, which only require models to match frames… See the full description on the dataset page: https://huggingface.co/datasets/JokerJan/MMR-VBench.
1,529
1,559
[ "task_categories:video-text-to-text", "size_categories:1K<n<10K", "format:parquet", "modality:text", "modality:video", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2506.04141", "region:us" ]
2025-03-31T02:48:10
null
null
682ed8ed8c5999c451e8968f
Dataseeds/DataSeeds.AI-Sample-Dataset-DSD
Dataseeds
{"language": ["en"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["image-classification", "object-detection"], "tags": ["computer-vision", "photography", "annotations", "EXIF", "scene-understanding", "multimodal"], "dataset_info": {"features": [{"name": "image_id", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "image_title", "dtype": "string"}, {"name": "image_description", "dtype": "string"}, {"name": "scene_description", "dtype": "string"}, {"name": "all_labels", "sequence": "string"}, {"name": "segmented_objects", "sequence": "string"}, {"name": "segmentation_masks", "sequence": {"sequence": "float64"}}, {"name": "exif_make", "dtype": "string"}, {"name": "exif_model", "dtype": "string"}, {"name": "exif_f_number", "dtype": "string"}, {"name": "exif_exposure_time", "dtype": "string"}, {"name": "exif_exposure_mode", "dtype": "string"}, {"name": "exif_exposure_program", "dtype": "string"}, {"name": "exif_metering_mode", "dtype": "string"}, {"name": "exif_lens", "dtype": "string"}, {"name": "exif_focal_length", "dtype": "string"}, {"name": "exif_iso", "dtype": "string"}, {"name": "exif_date_original", "dtype": "string"}, {"name": "exif_software", "dtype": "string"}, {"name": "exif_orientation", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3735124734.561, "num_examples": 7069}, {"name": "validation", "num_bytes": 410656962, "num_examples": 771}], "download_size": 4166184032, "dataset_size": 4145781696.561}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}]}
false
null
2025-06-10T07:02:35
13
13
false
dc202e1d0400094ac664ad0bc77c5293cc21177c
DataSeeds.AI Sample Dataset (DSD) Dataset Summary The DataSeeds.AI Sample Dataset (DSD) is a high-fidelity, human-curated computer vision-ready dataset comprised of 7,840 peer-ranked, fully annotated photographic images, 350,000+ words of descriptive text, and comprehensive metadata. While the DSD is being released under an open source license, a sister dataset of over 10,000 fully annotated and segmented images is available for immediate commercial licensing, and the… See the full description on the dataset page: https://huggingface.co/datasets/Dataseeds/DataSeeds.AI-Sample-Dataset-DSD.
242
263
[ "task_categories:image-classification", "task_categories:object-detection", "language:en", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2506.05673", "region:us", "computer-vision", "photography", "annotations", "EXIF", "scene-understanding", "multimodal" ]
2025-05-22T07:57:33
null
null
6835ce29eac05bd2e0fc2803
microsoft/mediflow
microsoft
{"license": "cdla-permissive-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["clinical", "medical"], "size_categories": ["1M<n<10M"]}
false
null
2025-05-30T19:26:32
31
13
false
2464e1fb01adce9466bdaeaf670674862bca6508
MediFlow A large-scale synthetic instruction dataset of 2.5M rows (~700k unique instructions) for clinical natural language processing covering 14 task types and 98 fine-grained input clinical documents. t-SNE 2D Plot of MediFlow Embeddings by Task Types Dataset Splits mediflow: 2.5M instruction data for SFT alignment. mediflow_dpo: ~135k top-quality instructions with GPT-4o generated rejected_output for DPO alignment. Main Columns instruction:… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/mediflow.
3,084
3,084
[ "task_categories:text-generation", "language:en", "license:cdla-permissive-2.0", "size_categories:1M<n<10M", "format:json", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "arxiv:2505.10717", "region:us", "clinical", "medical" ]
2025-05-27T14:37:29
null
null
6842c81fe9598a4b0d5de03e
DeepMount00/italian_conversations
DeepMount00
{"language": ["it"], "license": "apache-2.0", "size_categories": ["1K<n<10K"]}
false
null
2025-06-07T12:51:50
12
12
false
af197cbabe51021eafc6ebbb30f264a8ba6533bc
📊 Panoramica del Dataset Nome: Dataset Conversazioni Italiane Strutturate Versione: 2.0 Lingua: Italiano 🇮🇹 Licenza: [Creative Commons Attribution 4.0 International License (CC BY 4.0)] 🎯 Finalità d'Uso Questo dataset è progettato per addestrare modelli linguistici a sostenere conversazioni approfondite e strutturate in italiano, con focus su argomentazioni complesse, analisi critica e discussioni multi-turno su tematiche di rilevanza sociale, politica, culturale ed economica. Include… See the full description on the dataset page: https://huggingface.co/datasets/DeepMount00/italian_conversations.
59
59
[ "language:it", "license:apache-2.0", "size_categories:1K<n<10K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
2025-06-06T10:51:11
null
null
6843ee960b94933522e9eeb9
thivux/phoaudiobook
thivux
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "text", "dtype": "string"}, {"name": "speaker", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 401319194100.855, "num_examples": 1042919}, {"name": "validation", "num_bytes": 52676843, "num_examples": 141}, {"name": "test", "num_bytes": 142910421, "num_examples": 383}], "download_size": 167165459930, "dataset_size": 401514781364.855}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "task_categories": ["text-to-speech"], "language": ["vi"], "pretty_name": "PhoAudiobook", "extra_gated_prompt": "User agrees (1) to use PhoAudiobook for research or educational purposes only, (2) to not distribute PhoAudiobook or part of PhoAudiobook in any original or modified form, (3) and to cite our [ACL 2025 paper](https://arxiv.org/abs/2506.01322) whenever PhoAudiobook is employed to help produce published results.\n"}
false
null
2025-06-09T03:26:06
11
11
false
ca3e9a4b445353e58ea43eb56e6c30a9188cf110
PhoAudiobook: A high-quality zero-shot TTS dataset for Vietnamese PhoAudiobook is a high-quality and large-scale Vietnamese speech dataset curated for zero-shot text-to-speech. Details of the dataset construction and experimental results can be found in our ACL 2025 paper: @inproceedings{vu2025zeroshottexttospeechvietnamese, title={Zero-Shot Text-to-Speech for Vietnamese}, author={Thi Vu and Linh The Nguyen and Dat Quoc Nguyen}, year={2025}… See the full description on the dataset page: https://huggingface.co/datasets/thivux/phoaudiobook.
1,215
1,215
[ "task_categories:text-to-speech", "language:vi", "size_categories:1M<n<10M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2506.01322", "region:us" ]
2025-06-07T07:47:34
null
null
6819a9a97c36c576e9c34e1f
bigai-nlco/ReflectionEvo
bigai-nlco
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false
null
2025-06-04T06:30:41
11
10
false
0c62b14e748de2104120951ebcf6c899105c558a
Github Repo for ReflectEvo: https://github.com/bigai-nlco/ReflectEvo Arxiv Paper for ReflectEvo: https://arxiv.org/abs/2505.16475
653
674
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2025-05-06T06:18:17
null
null
6831657644303aa0e664f7a2
boltuix/emotions-dataset
boltuix
{"license": "mit", "language": ["en"], "tags": ["emotions", "nlp", "sentiment-analysis", "emotion-classification", "machine-learning", "data-science", "artificial-intelligence", "chatbot", "mental-health", "social-media", "text-analysis", "deep-learning", "ai-research", "human-computer-interaction", "empathetic-ai", "psychology", "big-data", "natural-language-processing", "dataset", "text-mining", "ai-innovation", "emotional-intelligence"], "pretty_name": "Emotions Dataset", "size_categories": ["10K<n<100K"]}
false
null
2025-05-25T15:41:59
14
10
false
4f18710cc8e9526bbf6177d7627f3269bbf56a79
🌟 Emotions Dataset — Infuse Your AI with Human Feelings! 😊😢😡 Tap into the Soul of Human Emotions 💖The Emotions Dataset is your key to unlocking emotional intelligence in AI. With 131,306 text entries labeled across 13 vivid emotions 😊😢😡, this dataset empowers you to build empathetic chatbots 🤖, mental health tools 🩺, social media analyzers 📱, and more! The Emotions Dataset is a carefully curated collection designed to elevate emotion classification, sentiment… See the full description on the dataset page: https://huggingface.co/datasets/boltuix/emotions-dataset.
291
302
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2025-05-24T06:21:42
null
null
683a35c76d1a968a658e4c15
allenai/reward-bench-2
allenai
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false
null
2025-06-04T08:53:38
18
10
false
7ff08853b0d5686e79b13fda8677024f566a104a
Code | Leaderboard | Results | Paper RewardBench 2 Evaluation Dataset Card The RewardBench 2 evaluation dataset is the new version of RewardBench that is based on unseen human data and designed to be substantially more difficult! RewardBench 2 evaluates capabilities of reward models over the following categories: Factuality (NEW!): Tests the ability of RMs to detect hallucinations and other basic errors in completions. Precise Instruction Following (NEW!): Tests the ability of RMs… See the full description on the dataset page: https://huggingface.co/datasets/allenai/reward-bench-2.
886
886
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2025-05-30T22:48:39
null
null
6655eb19d17e141dcb546ed5
HuggingFaceFW/fineweb-edu
HuggingFaceFW
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false
null
2025-01-31T15:56:54
693
9
false
4863ab07d7520451e6f73e2912ad8bfee7d97c11
📚 FineWeb-Edu 1.3 trillion tokens of the finest educational data the 🌐 web has to offer Paper: https://arxiv.org/abs/2406.17557 What is it? 📚 FineWeb-Edu dataset consists of 1.3T tokens and 5.4T tokens (FineWeb-Edu-score-2) of educational web pages filtered from 🍷 FineWeb dataset. This is the 1.3 trillion version. To enhance FineWeb's quality, we developed an educational quality classifier using annotations generated by LLama3-70B-Instruct. We then… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu.
128,759
3,582,770
[ "task_categories:text-generation", "language:en", "license:odc-by", "size_categories:1B<n<10B", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2406.17557", "arxiv:2404.14219", "arxiv:2401.10020", "arxiv:2109.07445", "doi:10.57967/hf/2497", "region:us" ]
2024-05-28T14:32:57
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
79
9
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.
1,069
4,400
[ "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
682f3c7f855225dd954bf66b
snorkelai/Multi-Turn-Insurance-Underwriting
snorkelai
{"language": ["en"], "size_categories": ["n<1K"], "license": "apache-2.0", "tags": ["legal"]}
false
null
2025-05-29T14:58:57
20
9
false
03b973c183f43a51e050a555e9365034fe381543
Dataset Card for Multi-Turn-Insurance-Underwriting Dataset Summary This dataset includes traces and associated metadata from multi-turn interactions between a commercial underwriter and AI assistant. We built the system in langgraph with model context protocol and ReAct agents. In each sample, the underwriter has a specific task to solve related to a recent application for insurance by a small business. We created a diverse sample dataset covering 6 distinct types of… See the full description on the dataset page: https://huggingface.co/datasets/snorkelai/Multi-Turn-Insurance-Underwriting.
1,782
1,782
[ "language:en", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "legal" ]
2025-05-22T15:02:23
null
null
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