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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 | [
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"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 | [
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] | 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 | [
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"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 | [
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"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 | [
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"arxiv:2410.23218",
"arxiv:2502.13923",
"arxiv:2501.12326",
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] | 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 | [
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"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",
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"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 | [
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"arxiv:2506.01732",
"arxiv:2410.22587",
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] | 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 | [
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] | 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 | [
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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 | [
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"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": 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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. | 371,328 | 3,649,246 | [
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"language:en",
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"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 | [
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"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",
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"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",
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"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 | [
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"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},
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6819a9a97c36c576e9c34e1f | bigai-nlco/ReflectionEvo | bigai-nlco | {"language": ["en"], "license": "mit", "task_categories": ["question-answering", "text-generation"], "size_categories": ["10K<n<100K"], "configs": [{"config_name": "Dpref", "data_files": [{"split": "train", "path": ["Dpref/Meta-Llama-3-8B-Instruct_bigbench.jsonl", "Dpref/Meta-Llama-3-8B-Instruct_logiqa.jsonl", "Dpref/Meta-Llama-3-8B-Instruct_math.jsonl", "Dpref/Meta-Llama-3-8B-Instruct_mbpp.jsonl", "Dpref/Mistral-7B-Instruct-v0.2_bigbench.jsonl", "Dpref/Mistral-7B-Instruct-v0.2_logiqa.jsonl", "Dpref/Mistral-7B-Instruct-v0.2_mbpp.jsonl", "Dpref/gemma-2-9b-it_bigbench.jsonl", "Dpref/gemma-2-9b-it_logiqa.jsonl", "Dpref/gemma-2-9b-it_math.jsonl", "Dpref/gemma-2-9b-it_mbpp.jsonl"]}]}, {"config_name": "D+-", "data_files": [{"split": "train", "path": ["D+-/Meta-Llama-3-8B-Instruct_bigbench.jsonl", "D+-/Meta-Llama-3-8B-Instruct_logiqa.jsonl", "D+-/Meta-Llama-3-8B-Instruct_math.jsonl", "D+-/Meta-Llama-3-8B-Instruct_mbpp.jsonl", "D+-/Mistral-7B-Instruct-v0.2_bigbench.jsonl", "D+-/Mistral-7B-Instruct-v0.2_logiqa.jsonl", "D+-/Mistral-7B-Instruct-v0.2_mbpp.jsonl", "D+-/gemma-2-9b-it_bigbench.jsonl", "D+-/gemma-2-9b-it_logiqa.jsonl", "D+-/gemma-2-9b-it_math.jsonl", "D+-/gemma-2-9b-it_mbpp.jsonl"]}]}, {"config_name": "D+", "data_files": [{"split": "train", "path": ["D+/Meta-Llama-3-8B-Instruct_bigbench.jsonl", "D+/Meta-Llama-3-8B-Instruct_logiqa.jsonl", "D+/Meta-Llama-3-8B-Instruct_math.jsonl", "D+/Meta-Llama-3-8B-Instruct_mbpp.jsonl", "D+/Mistral-7B-Instruct-v0.2_bigbench.jsonl", "D+/Mistral-7B-Instruct-v0.2_logiqa.jsonl", "D+/Mistral-7B-Instruct-v0.2_mbpp.jsonl", "D+/gemma-2-9b-it_bigbench.jsonl", "D+/gemma-2-9b-it_logiqa.jsonl", "D+/gemma-2-9b-it_math.jsonl", "D+/gemma-2-9b-it_mbpp.jsonl"]}]}]} | 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
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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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683a35c76d1a968a658e4c15 | allenai/reward-bench-2 | allenai | {"language": ["en"], "license": "odc-by", "size_categories": ["1K<n<10K"], "task_categories": ["question-answering"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "chosen", "sequence": "string"}, {"name": "rejected", "sequence": "string"}, {"name": "num_correct", "dtype": "int64"}, {"name": "num_incorrect", "dtype": "int64"}, {"name": "total_completions", "dtype": "int64"}, {"name": "models", "sequence": "string"}, {"name": "subset", "dtype": "string"}, {"name": "additional_metadata", "struct": [{"name": "category", "dtype": "string"}, {"name": "correct", "dtype": "string"}, {"name": "index", "dtype": "float64"}, {"name": "instruction_id_list", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "method", "dtype": "string"}, {"name": "models", "sequence": "string"}, {"name": "prompt_norm", "dtype": "string"}, {"name": "subcategory", "dtype": "string"}, {"name": "valid", "dtype": "float64"}]}], "splits": [{"name": "test", "num_bytes": 13772499, "num_examples": 1865}], "download_size": 6973189, "dataset_size": 13772499}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]} | 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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📚 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 | [
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"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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