artifact_type large_stringclasses 2
values | artifact_name large_stringlengths 5 123 | org large_stringlengths 2 42 | created_at large_stringdate 2022-03-02 00:00:00 2026-05-31 00:00:00 | last_modified large_stringdate 2020-07-16 00:00:00 2026-05-31 00:00:00 | languages listlengths 0 7.91k | license large_stringclasses 81
values | task_categories listlengths 0 47 | tags listlengths 2 7.92k | size_category large_stringclasses 11
values | downloads int64 0 262M | multilinguality listlengths 0 52 ⌀ | num_dataset_rows float64 0 194B ⌀ | disk_size float64 6 306,846B ⌀ | arxiv_ids listlengths 0 440 | readme large_stringlengths 0 13.4M |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
dataset | OleehyO/engiworld-exp | OleehyO | 2026-05-03 | 2026-05-21 | [] | null | [] | [
"size_categories:10K<n<100K",
"format:imagefolder",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 10K<n<100K | 17,992 | null | 56,822 | 49,911,557,158 | [] | |
dataset | gfissore/arxiv-abstracts-2021 | gfissore | 2022-03-02 | 2022-10-27 | [
"en"
] | cc0-1.0 | [
"summarization",
"text-retrieval",
"text2text-generation"
] | [
"task_categories:summarization",
"task_categories:text-retrieval",
"task_ids:explanation-generation",
"task_ids:text-simplification",
"task_ids:document-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"annotations_creators:no-annotation",
"language_creators:exper... | 1M<n<10M | 15,834 | [
"monolingual"
] | 1,999,486 | 939,922,245 | [
"1905.00075"
] |
# Dataset Card for arxiv-abstracts-2021
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [D... |
dataset | juliensimon/esa-exomars-tgo-observations | juliensimon | 2026-04-03 | 2026-05-26 | [
"en"
] | other | [
"tabular-classification"
] | [
"task_categories:tabular-classification",
"language:en",
"license:other",
"size_categories:10M<n<100M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"space",
"mars",
"exomars",
"tgo",
... | 10M<n<100M | 15,923 | null | 54,346,258 | 363,495,058 | [] |
# ESA ExoMars TGO Observations
<div align="center">
<img src="banner.jpg" alt="Exploring Jezero Crater on Mars (illustration)" width="400">
<p><em>Credit: NASA/JPL-Caltech</em></p>
</div>
*Part of a [dataset collection](https://huggingface.co/collections/juliensimon/space-probe-and-mission-datasets-69c3fe82d410a... |
dataset | Muesli1/dclm-baseline-1.0-llama3-tokenized-shuffled | Muesli1 | 2026-04-27 | 2026-04-28 | [
"en"
] | cc-by-4.0 | [] | [
"language:en",
"license:cc-by-4.0",
"modality:text",
"arxiv:2407.21783",
"arxiv:2406.11794",
"region:us",
"pretokenized",
"pretraining",
"llama3",
"text"
] | null | 16,470 | null | null | 6,864,025,431,419 | [
"2407.21783",
"2406.11794"
] |
### !! Note: this dataset is currently being uploaded and processed. The .bin files are intermediate files to allow shuffling. !!
## DCLM-Baseline Pretokenized (LLaMA 3.1, 8192 context)
This dataset is a pretokenized and globally shuffled version of DCLM-Baseline (mlfoundations/dclm-baseline-1.0), prepared for large... |
dataset | lincyaw/openrca2-v1-500 | lincyaw | 2026-05-03 | 2026-05-03 | [
"en"
] | mit | [] | [
"language:en",
"license:mit",
"size_categories:n<1K",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"root-cause-analysis",
"microservices",
"observability",
"benchmark"
] | n<1K | 15,963 | null | 1 | 3,601,592,213 | [] |
# OpenRCA2 v1 (500 cases, 2026-05-02 snapshot)
Curated 500-case RCA evaluation set built 2026-05-02 from a unified pool of older FSE/openrca2 train-ticket cases plus the most recent aegisctl `detector_success` runs across `train-ticket`, `hotel-reservation`, and `otel-demo`. Each case carries the full telemetry parqu... |
dataset | hotchpotch/mmarco-hard-negatives-reranker-filtered | hotchpotch | 2026-01-12 | 2026-01-12 | [] | null | [] | [
"size_categories:10M<n<100M",
"format:parquet",
"format:optimized-parquet",
"modality:tabular",
"modality:text",
"modality:timeseries",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us"
] | 10M<n<100M | 15,928 | null | 94,077,750 | 42,085,623,446 | [] |
# mMARCO Reranker-Filtered Hard Negatives (Multilingual)
## Overview
This dataset is built from [mMARCO](https://huggingface.co/datasets/unicamp-dl/mmarco) (multilingual MS MARCO) triplets for each language subset. For each (query, positive), hard negatives are bundled and then filtered using cross-encoder re-scoring... |
dataset | HuggingFaceH4/stack-exchange-preferences | HuggingFaceH4 | 2023-02-11 | 2023-03-08 | [
"en"
] | cc-by-sa-4.0 | [
"question-answering"
] | [
"task_categories:question-answering",
"language:en",
"license:cc-by-sa-4.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2112.00861",
"region:us",
"RLHF",
"preferences",
"human-feedba... | 10M<n<100M | 15,817 | null | 10,807,695 | 19,674,554,583 | [
"2112.00861"
] | # Dataset Card for H4 Stack Exchange Preferences Dataset
## Dataset Description
- **Homepage:** https://archive.org/details/stackexchange
- **Repository:** (private for now) https://github.com/huggingface/h4
- **Point of Contact:** Nathan Lambert, nathan@huggingface.co
- **Size of downloaded dataset:** 22.13 GB
- **N... |
dataset | Nithish2410/benchmark-bcplus | Nithish2410 | 2026-03-20 | 2026-03-20 | [] | null | [] | [
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | n<1K | 15,752 | null | 830 | 3,323,006,019 | [] | |
dataset | ashu010/agridrone-data | ashu010 | 2026-04-24 | 2026-04-24 | [
"en"
] | mit | [] | [
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"modality:image",
"modality:text",
"region:us",
"agriculture",
"crop-disease",
"wheat",
"rice",
"computer-vision",
"plant-pathology"
] | 10K<n<100K | 17,041 | null | null | 23,609,775,239 | [] |
# AgriDrone — Crop Disease Detection Dataset
Full dataset collection used for training the AgriDrone crop-disease
detection system (21-class YOLOv8n-cls classifier, 15 wheat + 6 rice
diseases). **75,010 images, 24 folders, 23.6 GB.**
Code repo: <https://github.com/Ashut0sh-mishra/agri-drone>
## Folder layout
### T... |
dataset | laion/eurospeech-enhanced-dacvae | laion | 2026-03-20 | 2026-05-05 | [] | cc-by-4.0 | [
"automatic-speech-recognition",
"text-to-speech"
] | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:webdataset",
"modality:audio",
"modality:text",
"library:datasets",
"library:webdataset",
"library:mlcroissant",
"region:us"
] | 1M<n<10M | 15,912 | null | 8,300 | 5,893,996,173,797 | [] |
# EuroSpeech parliamentary speech converted to DAC VAE latents
## Source
[disco-eth/EuroSpeech](https://huggingface.co/datasets/disco-eth/EuroSpeech)
## Format
Each tar shard (~2GB) contains samples with three files per sample:
```
{sample_key}.audio.flac # Original audio (FLAC, original sample rate)
{sample_... |
dataset | logipuz/logipuz-pools | logipuz | 2026-05-04 | 2026-05-04 | [
"en"
] | cc-by-4.0 | [
"question-answering"
] | [
"task_categories:question-answering",
"language:en",
"license:cc-by-4.0",
"size_categories:n<1K",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"synthetic",
"logic-puzzles",
"reasoning",
"... | n<1K | 17,257 | null | 25 | 2,200,529,387 | [] |
# LogiPuz Pools
Reusable core candidate pools for LogiPuz ZebraLogic-style puzzle generation.
This artifact contains theme-free, solver-checked core puzzle candidates for every shape from `2x2` through `6x6`. The pools are intended for dataset construction, support checks, and reproducible extensions of the LogiPuz ... |
dataset | vetonKlinakuRtechko1/MiniDataSetBR | vetonKlinakuRtechko1 | 2026-03-26 | 2026-03-26 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:csv",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 1K<n<10K | 15,937 | null | 3,800 | 69,286,993 | [] | |
dataset | AlienKevin/SWE-ZERO-12M-trajectories | AlienKevin | 2026-04-16 | 2026-05-14 | [
"en"
] | apache-2.0 | [
"text-generation"
] | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"swe-zero",
"code",
"agentic",
"pre-training"
] | 10M<n<100M | 15,682 | null | 12,290,800 | 35,972,855,768 | [] |
# SWE-ZERO 12M Trajectories
The largest agentic-coding trace dataset to date: **112 B tokens** of execution-free agentic trajectories covering **122 K pull requests**, **3 K repositories**, and **16 programming languages**.
## Motivation
Agentic mid-training has become a standard ingredient for frontier coding mode... |
dataset | TuringEnterprises/Open-MM-RL | TuringEnterprises | 2026-05-11 | 2026-05-13 | [
"en"
] | mit | [
"question-answering"
] | [
"task_categories:question-answering",
"language:en",
"license:mit",
"size_categories:n<1K",
"format:parquet",
"format:optimized-parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"chemistry",
"physics",... | n<1K | 15,702 | null | 40 | 31,062,538 | [] |
# Dataset Summary
**Open-MM-RL** is a multimodal STEM reasoning dataset covering **Physics, Mathematics, Biology, and Chemistry**. It is designed for problems that require models to interpret visual information and combine it with step-by-step analytical reasoning.
Explore the full Open-MM-RL dataset (3,000 tasks co... |
dataset | lockon/ToolACE | lockon | 2025-08-11 | 2024-09-04 | [
"en",
"zh"
] | apache-2.0 | [
"text-generation"
] | [
"task_categories:text-generation",
"language:en",
"language:zh",
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2409.00920",
"region:us",
"synthetic",
"tools"
] | 10K<n<100K | 15,678 | null | 11,300 | 37,159,073 | [
"2409.00920"
] | # 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, g... |
dataset | GPT-NL/GPT-NL_Public_Corpus | GPT-NL | 2025-11-18 | 2026-05-04 | [
"nl",
"en",
"de",
"fy",
"da"
] | cc-by-4.0 | [] | [
"language:nl",
"language:en",
"language:de",
"language:fy",
"language:da",
"license:cc-by-4.0",
"size_categories:100M<n<1B",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2604.00920",
"region:u... | 100M<n<1B | 15,795 | null | 302,078,553 | 948,136,604,439 | [
"2604.00920"
] |
# Dataset Card GPT-NL Public Corpus
The GPT-NL Public Corpus is the largest permissively licensed Dutch-language resource available for large language model pretraining. It consists of 29 curated collections totaling over 524 billion tokens, including 36B Dutch, 207B English, 232B code, and 48B German/Danish tokens. A... |
dataset | MathArena/hmmt_feb_2025 | MathArena | 2025-04-16 | 2026-05-15 | [
"en"
] | cc-by-nc-sa-4.0 | [] | [
"language:en",
"license:cc-by-nc-sa-4.0",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2605.00674",
"region:us"
] | n<1K | 15,662 | null | 30 | 13,790 | [
"2605.00674"
] |
### Homepage and repository
- **Homepage:** [https://matharena.ai/](https://matharena.ai/)
- **Repository:** [https://github.com/eth-sri/matharena](https://github.com/eth-sri/matharena)
### Dataset Summary
This dataset contains the questions from HMMT February 2025 used for the MathArena Leaderboard
### Data Field... |
dataset | TAUR-Lab/MuSR | TAUR-Lab | 2024-05-17 | 2024-05-21 | [
"en"
] | cc-by-4.0 | [
"question-answering"
] | [
"task_categories:question-answering",
"language:en",
"license:cc-by-4.0",
"size_categories:n<1K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2310.16049",
"region:us",
"reasoning",
"commonsense"
] | n<1K | 15,608 | null | 756 | 7,149,329 | [
"2310.16049"
] |
# MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning
### Creating murder mysteries that require multi-step reasoning with commonsense using ChatGPT!
By: Zayne Sprague, Xi Ye, Kaj Bostrom, Swarat Chaudhuri, and Greg Durrett.
View the dataset on our custom viewer and [project website](https://z... |
dataset | ServiceNow/WorkArena-Instances | ServiceNow | 2025-11-19 | 2026-01-24 | [] | null | [] | [
"size_categories:n<1K",
"modality:text",
"region:us"
] | n<1K | 15,498 | null | null | 5,130 | [] |
# ServiceNow Instances for WorkArena
This repository provides access to the ServiceNow instances used for the WorkArena benchmark.
Access is restricted.
Please complete the form above to request access.
---
## Usage Scope
Instances are provided **exclusively for benchmarking, evaluation, and research**. They m... |
dataset | uav-disaster-dataset/disasterview | uav-disaster-dataset | 2026-05-02 | 2026-05-04 | [] | cc-by-4.0 | [] | [
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:imagefolder",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 10K<n<100K | 16,843 | null | 19,841 | 22,312,191,419 | [] | |
dataset | mort666/cv_corpus_v22 | mort666 | 2025-12-15 | 2025-12-18 | [
"en",
"ru",
"th",
"uk"
] | cc0-1.0 | [
"automatic-speech-recognition"
] | [
"task_categories:automatic-speech-recognition",
"language:en",
"language:ru",
"language:th",
"language:uk",
"license:cc0-1.0",
"size_categories:1M<n<10M",
"modality:audio",
"modality:text",
"region:us",
"mozilla",
"foundation"
] | 1M<n<10M | 15,676 | null | 4,715,586 | 120,131,883,504 | [] | # Dataset Card for Common Voice Corpus 22.0
<!-- Provide a quick summary of the dataset. -->
This dataset is an unofficial version of the Mozilla Common Voice Corpus 22. It was downloaded and converted from the project's website https://commonvoice.mozilla.org/.
**NOTE: currently converting to parquet for convenienc... |
dataset | Avelina/smollm-corpus-cleaned | Avelina | 2025-02-26 | 2025-02-26 | [
"en"
] | odc-by | [
"text-generation"
] | [
"task_categories:text-generation",
"language:en",
"license:odc-by",
"size_categories:100M<n<1B",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"region:us"
] | 100M<n<1B | 16,173 | null | 1,126,714 | 337,521,278,616 | [] |
# SmolLM-Corpus: Now shuffled and sharded (and Cleaned)!
This is a version of the SmolLM-Corpus where the 3 subsets have been interleved, shuffled and sharded as 23698 `jsonl.zst` files for easy streaming!
The dataset is comprised of the `cosmopedia-v2` and `fineweb-edu-dedup` subsets from the original [SmolLM-Corpus... |
dataset | openbrain-anon/openbrain_v1_0 | openbrain-anon | 2026-05-03 | 2026-05-07 | [] | null | [] | [
"size_categories:n<1K",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | n<1K | 16,882 | null | 64 | 123,542,051,710 | [] |
# OpenBrain v1.0
OpenBrain v1.0 is a public-ready release of brain-extracted T1-weighted MRI
images, SynthStrip-derived brain masks, and automated whole-brain segmentation
labels.
## Release Contents
- Cases: 35,838
- Source OpenNeuro datasets: 607
- License: CC0
- Artifacts per case:
- `image.nii.gz`: revised br... |
dataset | argilla/ultrafeedback-binarized-preferences-cleaned | argilla | 2023-12-05 | 2023-12-11 | [
"en"
] | mit | [
"text-generation"
] | [
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"dpo",
"preference",
"ultrafeedback"
] | 10K<n<100K | 15,418 | null | 60,917 | 143,598,681 | [] |
# UltraFeedback - Binarized using the Average of Preference Ratings (Cleaned)
This dataset represents a new iteration on top of [`argilla/ultrafeedback-binarized-preferences`](https://huggingface.co/argilla/ultrafeedback-binarized-preferences),
and is the **recommended and preferred dataset by Argilla to use from now... |
dataset | nvidia/Llama-Nemotron-VLM-Dataset-v1 | nvidia | 2025-08-05 | 2025-10-22 | [] | cc-by-4.0 | [
"visual-question-answering",
"image-text-to-text",
"image-to-text"
] | [
"task_categories:visual-question-answering",
"task_categories:image-text-to-text",
"task_categories:image-to-text",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2501.14818"... | 1M<n<10M | 15,497 | null | 2,863,834 | 113,491,640,941 | [
"2501.14818",
"2502.04223"
] | # Llama-Nemotron-VLM-Dataset v1
## Versions
| Date | Commit | Changes |
|-------------|--------------|----------|
| 2025-08-11 | [bdb3899](https://huggingface.co/datasets/nvidia/Llama-Nemotron-VLM-Dataset-v1/commit/bdb3899d3f1bf7a9e5af663e3f5a30fcb3fef295) | Initial release |
| 2025-08-18 | [5abc7df](... |
dataset | Angelou0516/Mediastinal-Lymph-Node-SEG | Angelou0516 | 2026-05-07 | 2026-05-20 | [] | cc-by-4.0 | [
"image-segmentation"
] | [
"task_categories:image-segmentation",
"license:cc-by-4.0",
"size_categories:n<1K",
"format:parquet",
"format:optimized-parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"medical",
"ct",
"chest",
"med... | n<1K | 17,413 | null | 513 | 35,360,080,311 | [] |
# Mediastinal-Lymph-Node-SEG (LNQ 2023)
Mediastinal lymph node quantification dataset from the LNQ 2023 challenge:
513 chest CT scans with manual radiologist segmentations of mediastinal
lymph nodes, distributed under two annotation regimes — partial
(weakly-supervised) for training, and full (gold-standard) for eval... |
dataset | Thilak20/terminal-bench-2-leaderboard | Thilak20 | 2026-03-11 | 2026-03-11 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"modality:text",
"region:us"
] | null | 16,118 | null | null | 4,018,672,364 | [] |
# Terminal-Bench 2.0 Leaderboard Submissions
This repository accepts leaderboard submissions for [Terminal-Bench 2.0](https://terminal-bench.org).
## How to Submit
1. [Fork this repository](https://huggingface.co/docs/hub/en/repositories-next-steps#duplicating-with-the-git-history-fork)
2. Create a new bran... |
dataset | gaianet/trumpVSharris | gaianet | 2024-10-17 | 2024-10-18 | [] | null | [] | [
"size_categories:1K<n<10K",
"format:text",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 1K<n<10K | 15,254 | null | 3,746 | 2,549,855 | [] | |
dataset | agentica-org/DeepScaleR-Preview-Dataset | agentica-org | 2025-02-09 | 2025-02-10 | [
"en"
] | mit | [] | [
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10K<n<100K | 15,378 | null | 40,315 | 21,483,669 | [] |
## Data
Our training dataset consists of approximately 40,000 unique mathematics problem-answer pairs compiled from:
- AIME (American Invitational Mathematics Examination) problems (1984-2023)
- AMC (American Mathematics Competition) problems (prior to 2023)
- Omni-MATH dataset
- Still dataset
## Format
Each row i... |
dataset | Neo111x/decompile-dataset-large-asm | Neo111x | 2025-01-14 | 2025-01-14 | [] | null | [] | [
"size_categories:1M<n<10M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1M<n<10M | 15,370 | null | 9,027,508 | 7,759,293,202 | [] | |
dataset | NuTonic/sat-vl-sft-postprocessed-merged-v1 | NuTonic | 2026-04-30 | 2026-04-30 | [
"en"
] | other | [
"text-generation",
"image-text-to-text"
] | [
"task_categories:text-generation",
"task_categories:image-text-to-text",
"language:en",
"license:other",
"size_categories:100K<n<1M",
"format:json",
"modality:image",
"modality:text",
"modality:geospatial",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"reg... | 100K<n<1M | 17,734 | null | 807,678 | 5,736,879,445 | [] |
## Dataset Summary
`NuTonic/sat-bbox-metadata-sft-v1` is a **metadata-first, procedural VLM SFT dataset** built from an existing “sat-bbox” style dataset tree (Sentinel‑2 chips + per-tile JSON metadata sidecars, optionally paired Mapbox stills).
The goal is to create **high-signal, production-shaped supervision** fo... |
dataset | ShadenA/MathNet | ShadenA | 2026-04-23 | 2026-04-27 | [
"en",
"pt",
"es",
"fr",
"it",
"sr",
"sl",
"de",
"zh",
"ro",
"ko",
"nl",
"ru",
"mn",
"mk",
"pl",
"hu"
] | cc-by-4.0 | [
"question-answering",
"text-generation",
"image-to-text"
] | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:image-to-text",
"language:en",
"language:pt",
"language:es",
"language:fr",
"language:it",
"language:sr",
"language:sl",
"language:de",
"language:zh",
"language:ro",
"language:ko",
"language:nl",
... | 10K<n<100K | 15,365 | null | 55,634 | 738,145,122 | [
"2604.18584"
] |
<div align="center">
<img src="assets/title_w_logo_light.png" alt="MathNet" width="960"/>
<img src="assets/overview.png" alt="MathNet overview: large-scale multilingual data, high-quality solutions, diverse topics, and three evaluation tasks" width="100%"/>
<a href="https://arxiv.org/abs/2604.18584"><img alt="ICLR 2... |
dataset | joshmiao/gfmc_hyworld1.5_processed_160latents_16fps_action | joshmiao | 2026-03-31 | 2026-03-31 | [] | null | [] | [
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 1K<n<10K | 15,772 | null | 9,088 | 206,400,497,971 | [] | |
dataset | google/frames-benchmark | google | 2024-09-19 | 2024-10-15 | [
"en"
] | apache-2.0 | [
"text-classification",
"token-classification",
"table-question-answering",
"question-answering"
] | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:table-question-answering",
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"size_categories:n<1K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"li... | n<1K | 15,285 | null | 824 | 489,782 | [
"2409.12941"
] |
# FRAMES: Factuality, Retrieval, And reasoning MEasurement Set
FRAMES is a comprehensive evaluation dataset designed to test the capabilities of Retrieval-Augmented Generation (RAG) systems across factuality, retrieval accuracy, and reasoning.
Our paper with details and experiments is available on arXiv: [https://arx... |
dataset | GBaker/MedQA-USMLE-4-options | GBaker | 2023-01-24 | 2023-01-24 | [
"en"
] | cc-by-4.0 | [] | [
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2009.13081",
"region:us"
] | 10K<n<100K | 15,287 | null | 11,451 | 18,289,742 | [
"2009.13081"
] |
Original dataset introduced by Jin et al. in [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://paperswithcode.com/paper/what-disease-does-this-patient-have-a-large)
<h4>Citation information:</h4>
@article{jin2020disease,
title={What Disea... |
dataset | alsfna/rl_data | alsfna | 2026-04-17 | 2026-05-15 | [] | null | [] | [
"modality:image",
"modality:text",
"modality:video",
"region:us"
] | null | 15,958 | null | null | 42,748,511,380 | [] | |
dataset | SciCodePile/SciCode-Domain-Code | SciCodePile | 2026-03-13 | 2026-03-13 | [
"code"
] | apache-2.0 | [
"text-generation"
] | [
"task_categories:text-generation",
"language:code",
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"code",
"scientific-computing",
"domain-speci... | 1M<n<10M | 15,252 | null | 155,855 | 83,737,480,383 | [] |
# DATA1: Domain-Specific Code Dataset
## Dataset Overview
DATA1 is a large-scale domain-specific code dataset focusing on code samples from interdisciplinary fields such as biology, chemistry, materials science, and related areas. The dataset is collected and organized from GitHub repositories, covering 178 differen... |
dataset | cryptom/ceval-exam | cryptom | 2023-06-23 | 2023-06-24 | [
"zh"
] | cc-by-nc-sa-4.0 | [
"text-classification",
"multiple-choice",
"question-answering"
] | [
"task_categories:text-classification",
"task_categories:multiple-choice",
"task_categories:question-answering",
"language:zh",
"license:cc-by-nc-sa-4.0",
"size_categories:10K<n<100K",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:2305.08322",
"region:us"
] | 10K<n<100K | 15,240 | null | 27,896 | 3,019,148 | [
"2305.08322"
] |
C-Eval is a comprehensive Chinese evaluation suite for foundation models. It consists of 13948 multi-choice questions spanning 52 diverse disciplines and four difficulty levels. Please visit our [website](https://cevalbenchmark.com/) and [GitHub](https://github.com/SJTU-LIT/ceval/tree/main) or check our [paper](https:... |
dataset | BrentLab/mahendrawada_2025 | BrentLab | 2025-08-27 | 2026-05-20 | [
"en"
] | mit | [] | [
"language:en",
"license:mit",
"size_categories:100M<n<1B",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"biology",
"genomics",
"yeast",
"transcription-factors",
"gene-expression",
"bin... | 100M<n<1B | 15,394 | null | 740,680,294 | 6,396,587,874 | [] | # Mahendrawada 2025
This data is taken from the Supplement of
[Mahendrawada, L., Warfield, L., Donczew, R. et al. Low overlap of transcription factor DNA binding and regulatory targets. Nature 642, 796–804 (2025). https://doi.org/10.1038/s41586-025-08916-0](https://doi.org/10.1038/s41586-025-08916-0)
and [GSE236948]... |
dataset | bigscience/xP3mt | bigscience | 2022-09-28 | 2023-05-30 | [
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",
"pt",
"rn",
"rw",
"sn",
"st",
"sw",
"ta",
"te",
"tn",
"ts",
"tum",
"tw",
... | apache-2.0 | [
"other"
] | [
"task_categories:other",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"multilinguality:multilingual",
"language:ak",
"language:ar",
"language:as",
"language:bm",
"language:bn",
"language:ca",
"language:code",
"language:en",
"language:es",
"language:eu",
"... | 10M<n<100M | 15,861 | [
"multilingual"
] | 40,023,564 | 203,554,157,796 | [
"2211.01786"
] |
# Dataset Card for xP3
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
... |
dataset | E4DRR/icechunk-stores | E4DRR | 2026-03-06 | 2026-03-07 | [] | null | [] | [
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | n<1K | 15,619 | null | 3 | 7,209,539,759 | [] | |
dataset | liang12121/dreamzero-egoverse-480-pretrain | liang12121 | 2026-04-30 | 2026-04-30 | [] | null | [] | [
"size_categories:10M<n<100M",
"format:parquet",
"modality:tabular",
"modality:text",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us"
] | 10M<n<100M | 15,886 | null | 39,249,602 | 167,971,423,835 | [] | |
dataset | w3en2g/QwQ_InfInstruct_Gen_v0 | w3en2g | 2024-12-21 | 2025-12-12 | [] | apache-2.0 | [
"text-generation"
] | [
"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",
"region:us"
] | 1M<n<10M | 15,084 | null | 1,456,927 | 7,589,582,703 | [] |
use QwQ 32b preview to generate response to answer the question from Infinity-Instruct gen |
dataset | SII-WANGZJ/Polymarket_data | SII-WANGZJ | 2026-01-01 | 2026-05-04 | [] | null | [] | [
"size_categories:1B<n<10B",
"modality:tabular",
"modality:text",
"region:us"
] | 1B<n<10B | 15,090 | null | 4,008,788,898 | 170,846,141,010 | [] | <div align="center">
<h1>Polymarket Data</h1>
<h3>Complete Data Infrastructure for Polymarket — Fetch, Process, Analyze</h3>
<p style="max-width: 750px; margin: 0 auto;">
A comprehensive dataset of 1.9 billion trading records from Polymarket, processed into multiple analysis-ready formats. Features cleaned data, uni... |
dataset | joey234/globaltrace | joey234 | 2026-05-04 | 2026-05-04 | [
"en"
] | cc-by-4.0 | [
"other"
] | [
"task_categories:other",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:geospatial",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"gpx",
"trajectory",
"gps",... | 10K<n<100K | 15,719 | null | 10,839 | 3,265,416,943 | [] |
# GlobalTrace
GlobalTrace is a benchmark of GPS trajectories drawn from 73 cities/regions across the world, with masked-segment annotations for evaluating trajectory reconstruction methods. Three masking strategies are provided per trajectory: `destination`, `large_gap`, and `simple_interpolation` (200m gap target).
... |
dataset | MathArena/aime_2026 | MathArena | 2026-02-13 | 2026-05-15 | [
"en"
] | cc-by-nc-sa-4.0 | [] | [
"benchmark:official",
"benchmark:eval-yaml",
"language:en",
"license:cc-by-nc-sa-4.0",
"size_categories:n<1K",
"format:parquet",
"format:optimized-parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2605.00674... | n<1K | 15,008 | null | 30 | 14,736 | [
"2605.00674"
] |
### Homepage and repository
- **Homepage:** [https://matharena.ai/](https://matharena.ai/)
- **Repository:** [https://github.com/eth-sri/matharena](https://github.com/eth-sri/matharena)
### Dataset Summary
This dataset contains the questions from AIME 2026 used for the MathArena Leaderboard
### Data Fields
The d... |
dataset | cmriat/gaia | cmriat | 2025-06-26 | 2025-06-26 | [] | null | [] | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | n<1K | 14,917 | null | 165 | 121,404 | [] | |
dataset | BangumiBase/ninjatokoroshiyanofutarigurashi | BangumiBase | 2025-08-07 | 2025-08-07 | [] | mit | [] | [
"license:mit",
"size_categories:1K<n<10K",
"modality:image",
"modality:text",
"region:us",
"art"
] | 1K<n<10K | 15,071 | null | null | 10,261,963,743 | [] |
# Bangumi Image Base of Ninja To Koroshiya No Futarigurashi
This is the image base of bangumi Ninja to Koroshiya no Futarigurashi, we detected 60 characters, 4262 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual... |
dataset | fancyzhx/yelp_polarity | fancyzhx | 2022-03-02 | 2024-08-08 | [
"en"
] | null | [
"text-classification"
] | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"language:en",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:1509.01626",
"region:us"
] | 100K<n<1M | 14,979 | null | 598,000 | 273,813,368 | [
"1509.01626"
] |
# Dataset Card for "yelp_polarity"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-insta... |
dataset | Intelligent-Internet/ii-agent_gaia-benchmark_validation | Intelligent-Internet | 2025-05-17 | 2025-05-17 | [] | null | [] | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | n<1K | 14,970 | null | 165 | 472,565,323 | [] | |
dataset | matchbench/selfkg-dwy100k-dbpwd | matchbench | 2023-03-04 | 2023-03-04 | [] | null | [] | [
"size_categories:1M<n<10M",
"modality:tabular",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 1M<n<10M | 14,788 | null | 1,453,496 | 714,070,306 | [] | |
dataset | wecover/OPUS_Europarl | wecover | 2024-01-31 | 2024-01-31 | [] | null | [] | [
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 10M<n<100M | 15,012 | null | 14,173,311 | 1,032,590,061 | [] | |
dataset | md-nishat-008/mHumanEval-Benchmark | md-nishat-008 | 2025-04-24 | 2025-04-24 | [
"aa",
"ab",
"ae",
"af",
"ak",
"am",
"an",
"ar",
"as",
"av",
"ay",
"az",
"ba",
"be",
"bg",
"bh",
"bi",
"bm",
"bn",
"bo",
"br",
"bs",
"ca",
"ce",
"ch",
"co",
"cr",
"cs",
"cu",
"cv",
"cy",
"da",
"de",
"dv",
"dz",
"ee",
"el",
"en",
"eo",
"es"... | apache-2.0 | [
"text2text-generation"
] | [
"language:aa",
"language:ab",
"language:ae",
"language:af",
"language:ak",
"language:am",
"language:an",
"language:ar",
"language:as",
"language:av",
"language:ay",
"language:az",
"language:ba",
"language:be",
"language:bg",
"language:bh",
"language:bi",
"language:bm",
"language:... | 100K<n<1M | 15,075 | null | 446,911 | 296,577,168 | [
"2410.15037",
"2107.03374",
"2004.09095"
] |
<div align="center">
## 🔷 **Accepted in NAACL Proceedings (2025)** 🔷
</div>
<div align="center">
<table>
<tr>
<td>
<a href="https://arxiv.org/abs/2410.15037">
<img src="https://img.shields.io/badge/arXiv-Read_Paper-blue?style=for-the-badge&logo=arxiv" alt="Read Paper"/>
<... |
dataset | BangumiBase/isshundechiryoushiteitanoniyakutatazutotsuihousaretatensaichiyushiyamihealertoshitetanoshikuiki | BangumiBase | 2025-08-07 | 2025-08-07 | [] | mit | [] | [
"license:mit",
"size_categories:1K<n<10K",
"modality:image",
"modality:text",
"region:us",
"art"
] | 1K<n<10K | 14,993 | null | null | 13,210,613,574 | [] |
# Bangumi Image Base of Isshun De Chiryou Shiteita Noni Yakutatazu To Tsuihou Sareta Tensai Chiyushi, Yami Healer Toshite Tanoshiku Ikiru
This is the image base of bangumi Isshun de Chiryou shiteita noni Yakutatazu to Tsuihou sareta Tensai Chiyushi, Yami Healer toshite Tanoshiku Ikiru, we detected 57 characters, 4431... |
dataset | h2asdf/BLADE-test | h2asdf | 2026-04-24 | 2026-04-29 | [
"en"
] | cc-by-sa-4.0 | [
"text-to-3d"
] | [
"task_categories:text-to-3d",
"language:en",
"license:cc-by-sa-4.0",
"size_categories:1M<n<10M",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"vision-language-action",
"vision-language"
] | 1M<n<10M | 15,882 | null | 4,477,743 | 4,028,657,093,727 | [] |
## Cite
```
``` |
dataset | meta-agents-research-environments/gaia2_filesystem | meta-agents-research-environments | 2025-08-20 | 2025-09-10 | [
"en"
] | cc-by-4.0 | [] | [
"language:en",
"license:cc-by-4.0",
"size_categories:n<1K",
"format:imagefolder",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | n<1K | 14,885 | null | 9 | 259,227,017 | [] | # GAIA2 Filesystem
This is a dataset containing files for the GAIA2 benchmark. You should not use this dataset on its own, but instead use the [Meta Agents Research Environments](https://github.com/facebookresearch/meta-agents-research-environments) framework to execute scenarios from that [GAIA2 dataset](https://hugg... |
dataset | davidilag/FPSC | davidilag | 2026-05-11 | 2026-05-13 | [
"fo"
] | cc-by-4.0 | [
"automatic-speech-recognition"
] | [
"task_categories:automatic-speech-recognition",
"language:fo",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:audiofolder",
"modality:audio",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 10K<n<100K | 15,993 | null | 48,028 | 99,279,108,040 | [] |
# FPSC — Faroese Parliament Speech Corpus
FPSC is a large-scale Faroese parliamentary speech corpus constructed from publicly available recordings from *Løgtingið*, the Parliament of the Faroe Islands.
The dataset contains approximately:
- 1,600 hours of speech
- 89,000+ parliamentary speeches
- 368 parlia... |
dataset | OpenLLM-France/Lucie-Training-Dataset | OpenLLM-France | 2024-10-16 | 2025-05-27 | [
"en",
"fr",
"de",
"es",
"it",
"code"
] | cc-by-nc-sa-4.0 | [
"text-generation",
"text2text-generation"
] | [
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:multilingual",
"language:en",
"language:fr",
"language:de",
"language:es",
"language:it",
"language:code",
"license:cc-by-nc-sa-4.0",
"size_categories:10B<n<100B",
"format:parquet",
"modality:text",
"library:... | 10B<n<100B | 15,475 | [
"multilingual"
] | 528,473,672 | 6,658,220,324,923 | [
"2308.12477",
"2311.16840",
"2402.00786",
"1905.10892",
"1906.02192",
"2108.01139",
"2010.12871",
"2406.17557",
"2312.17120",
"2201.07311",
"1904.01557",
"2101.00027",
"2211.15533",
"2503.12294"
] |
# Lucie Training Dataset Card
The Lucie Training Dataset is a curated collection of text data
in English, French, German, Spanish and Italian culled from a variety of sources including: web data, video subtitles, academic papers,
digital books, newspapers, and magazines, some of which were processed by Optical Charac... |
dataset | Helsinki-NLP/opus_books | Helsinki-NLP | 2022-03-02 | 2024-03-29 | [
"ca",
"de",
"el",
"en",
"eo",
"es",
"fi",
"fr",
"hu",
"it",
"nl",
"no",
"pl",
"pt",
"ru",
"sv"
] | other | [
"translation"
] | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"source_datasets:original",
"language:ca",
"language:de",
"language:el",
"language:en",
"language:eo",
"language:es",
"language:fi",
"language:fr",
"language:hu",
"lang... | 1M<n<10M | 14,844 | [
"multilingual"
] | 1,250,632 | 210,308,823 | [] |
# Dataset Card for OPUS Books
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)... |
dataset | asahi417/seamless-align-enA-jaA.speaker-embedding.w2vbert-600m | asahi417 | 2024-06-11 | 2024-06-14 | [] | null | [] | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | 100K<n<1M | 14,983 | null | 173,344 | 1,142,903,078,405 | [] | |
dataset | philschmid/mt-bench | philschmid | 2023-10-27 | 2023-10-27 | [] | null | [] | [
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | n<1K | 14,771 | null | 80 | 51,236 | [] | |
dataset | AllTheBacteria/BacCorpus | AllTheBacteria | 2026-05-08 | 2026-05-10 | [] | null | [] | [
"size_categories:10M<n<100M",
"modality:text",
"region:us"
] | 10M<n<100M | 15,314 | null | 19,443,879 | 8,772,546,808,118 | [] |
# BacCorpus
|
dataset | Logistic12/xhs-image-extractor-20260314115142 | Logistic12 | 2026-03-14 | 2026-03-17 | [] | null | [] | [
"size_categories:1K<n<10K",
"format:imagefolder",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 1K<n<10K | 14,944 | null | 3,582 | 419,339,068 | [] | |
dataset | cua-lite/ScaleCUA | cua-lite | 2026-04-29 | 2026-05-27 | [] | other | [
"image-text-to-text"
] | [
"task_categories:image-text-to-text",
"license:other",
"size_categories:1M<n<10M",
"modality:image",
"modality:text",
"region:us",
"cua-lite",
"gui",
"sft"
] | 1M<n<10M | 15,356 | null | 4,953,995 | 1,984,900,604,879 | [] |
# cua-lite/ScaleCUA
cua-lite preprocessed version of ScaleCUA (OpenGVLab/ScaleCUA-Data + zyliu/ScaleCUA-Data-Understanding). Large-scale multi-platform / multi-task-type GUI dataset spanning understanding, grounding:action, grounding:bbox, grounding:point, and navigation.
## Origin
- [https://huggingface.co/dataset... |
dataset | mteb/fiqa | mteb | 2024-03-02 | 2025-05-04 | [
"eng"
] | unknown | [
"text-retrieval"
] | [
"task_categories:text-retrieval",
"task_ids:multiple-choice-qa",
"annotations_creators:human-annotated",
"multilinguality:monolingual",
"language:eng",
"license:unknown",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
... | 10K<n<100K | 14,761 | [
"monolingual"
] | 81,396 | 48,881,656 | [
"2502.13595",
"2210.07316"
] | <!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
<h1 styl... |
dataset | 3rdn4/terminal-bench-2-leaderboard | 3rdn4 | 2026-03-09 | 2026-03-09 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"modality:text",
"region:us"
] | null | 15,477 | null | null | 4,018,672,364 | [] |
# Terminal-Bench 2.0 Leaderboard Submissions
This repository accepts leaderboard submissions for [Terminal-Bench 2.0](https://terminal-bench.org).
## How to Submit
1. [Fork this repository](https://huggingface.co/docs/hub/en/repositories-next-steps#duplicating-with-the-git-history-fork)
2. Create a new bran... |
dataset | tinyBenchmarks/tinyHellaswag | tinyBenchmarks | 2024-02-22 | 2024-05-25 | [
"en"
] | null | [] | [
"multilinguality:monolingual",
"source_datasets:Rowan/hellaswag",
"language:en",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2402.14992",
"region:us"
] | 10K<n<100K | 14,679 | [
"monolingual"
] | 50,008 | 50,117,627 | [
"2402.14992"
] | # tinyHellaswag
Welcome to tinyHellaswag! This dataset serves as a concise version of the [hellaswag](https://huggingface.co/datasets/hellaswag) dataset, offering a subset of 100 data points selected from the original compilation.
tinyHellaswag is designed to enable users to efficiently estimate the performance of a ... |
dataset | nvidia/Nemotron-Cascade-2-SFT-Data | nvidia | 2026-03-19 | 2026-03-19 | [] | other | [] | [
"license:other",
"size_categories:10M<n<100M",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us"
] | 10M<n<100M | 14,694 | null | 1,940,375 | 592,920,827,900 | [] |
# Nemotron-Cascade-2-SFT-Data
We release the SFT data used for training [Nemotron-Cascade-2](https://huggingface.co/nvidia/Nemotron-Cascade-2-30B-A3B).
## Data sources
#### Math
Our non-proof math prompts are sourced from [Nemotron-Cascade-1-SFT](https://huggingface.co/datasets/nvidia/Nemotron-Cascade-SFT-Stage-2)... |
dataset | BLINK-Benchmark/BLINK | BLINK-Benchmark | 2024-04-04 | 2025-09-03 | [] | apache-2.0 | [] | [
"license:apache-2.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2404.12390",
"region:us"
] | 1K<n<10K | 14,738 | null | 3,807 | 805,181,939 | [
"2404.12390"
] |
# BLINK: Multimodal Large Language Models Can See but Not Perceive
[**🌐 Homepage**](https://zeyofu.github.io/blink/) | [**💻 Code**](https://github.com/zeyofu/BLINK_Benchmark) | [**📖 Paper**](https://arxiv.org/abs/2404.12390.pdf) | [**📖 arXiv**](https://arxiv.org/abs/2404.12390) | [**🔗 Eval AI**](https://eval.ai/... |
dataset | qwedsacf/competition_math | qwedsacf | 2023-01-28 | 2023-01-28 | [
"en"
] | mit | [
"text2text-generation"
] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"li... | 10K<n<100K | 14,699 | [
"monolingual"
] | 12,500 | 4,855,429 | [
"2103.03874"
] |
# Dataset Card for Mathematics Aptitude Test of Heuristics (MATH) dataset
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#lang... |
dataset | Tevatron/browsecomp-plus-corpus | Tevatron | 2025-07-23 | 2025-08-23 | [] | mit | [
"question-answering"
] | [
"task_categories:question-answering",
"license:mit",
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2508.06600",
"region:us",
"retrieval-augmented-generation",
"benchmark",
"llm",
"deep-r... | 100K<n<1M | 14,656 | null | 100,195 | 1,761,586,179 | [
"2508.06600"
] |
# BrowseComp-Plus
[Project Page](https://texttron.github.io/BrowseComp-Plus/) | [Paper](https://arxiv.org/abs/2508.06600) | [Code](https://github.com/texttron/BrowseComp-Plus)
BrowseComp-Plus is a new benchmark for Deep-Research system, isolating the effect of the retriever and the LLM agent to enable **fair, transp... |
dataset | mteb/amazon_massive_intent | mteb | 2022-05-15 | 2026-02-24 | [
"afr",
"amh",
"ara",
"aze",
"ben",
"cmo",
"cym",
"dan",
"deu",
"ell",
"eng",
"fas",
"fin",
"fra",
"heb",
"hin",
"hun",
"hye",
"ind",
"isl",
"ita",
"jav",
"jpn",
"kan",
"kat",
"khm",
"kor",
"lav",
"mal",
"mon",
"msa",
"mya",
"nld",
"nob",
"pol",
"... | apache-2.0 | [
"text-classification"
] | [
"task_categories:text-classification",
"annotations_creators:human-annotated",
"multilinguality:translated",
"language:afr",
"language:amh",
"language:ara",
"language:aze",
"language:ben",
"language:cmo",
"language:cym",
"language:dan",
"language:deu",
"language:ell",
"language:eng",
"la... | 100K<n<1M | 14,768 | [
"translated"
] | 842,571 | 16,957,343 | [
"2204.08582",
"2502.13595",
"2210.07316"
] | <!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
<h1 styl... |
dataset | CSU-JPG/VisPrompt5M | CSU-JPG | 2026-04-07 | 2026-04-09 | [
"en"
] | apache-2.0 | [
"image-to-image",
"text-to-image"
] | [
"task_categories:image-to-image",
"task_categories:text-to-image",
"language:en",
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2604.06757",
"region:... | 1M<n<10M | 14,717 | null | 5,360,970 | 2,972,374,569,546 | [
"2604.06757"
] | <div align="center">
<h2 align="center" style="margin-top: 0; margin-bottom: 15px;">
<span style="color:#0052CC">F</span><span style="color:#135FD0">l</span><span style="color:#266CD4">o</span><span style="color:#3979D7">w</span><span style="color:#4C86DB">I</span><span style="color:#6093DF">n</span><span style="... |
dataset | PKU-Alignment/PKU-SafeRLHF | PKU-Alignment | 2023-06-14 | 2024-10-18 | [
"en"
] | cc-by-nc-4.0 | [
"text-generation"
] | [
"task_categories:text-generation",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:100K<n<1M",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2406.15513",
"region:us",
"safe",
"safety",
"... | 100K<n<1M | 14,552 | null | 164,236 | 233,015,453 | [
"2406.15513"
] |
# Dataset Card for PKU-SafeRLHF
<span style="color: red;">Warning: this dataset contains data that may be offensive or harmful. The data are intended for research purposes, especially research that can make models less harmful. The views expressed in the data do not reflect the views of PKU-Alignment Team or any of i... |
dataset | IPEC-COMMUNITY/EO-Data1.5M | IPEC-COMMUNITY | 2025-08-28 | 2025-12-20 | [
"en"
] | apache-2.0 | [
"robotics",
"visual-question-answering",
"video-text-to-text",
"image-text-to-text"
] | [
"task_categories:robotics",
"task_categories:visual-question-answering",
"task_categories:video-text-to-text",
"task_categories:image-text-to-text",
"language:en",
"license:apache-2.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"librar... | 1M<n<10M | 14,518 | null | 1,422,808 | 201,086,345,485 | [
"2508.21112"
] |
<div align="center">
# 🤖 EO-Data-1.5M
### A Large-Scale Interleaved Vision-Text-Action Dataset for Embodied AI
<p align="center">
<a href="http://eo-robotics.ai/eo-1">
<img src="https://img.shields.io/badge/EO--Robotics-Website-5865F2?logo=googleplay&logoColor=white" alt="EO-Robotics Website"/>
</a>
<a h... |
dataset | SALT-Research/DeepDialogue-xtts | SALT-Research | 2025-05-12 | 2025-05-28 | [
"en"
] | cc-by-nc-4.0 | [
"audio-classification",
"automatic-speech-recognition"
] | [
"task_categories:audio-classification",
"task_categories:automatic-speech-recognition",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:audio",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"l... | 100K<n<1M | 15,092 | null | 243,295 | 7,973,936,314 | [
"2505.19978"
] | # DeepDialogue-xtts
**DeepDialogue-xtts** is a large-scale multimodal dataset containing 40,150 high-quality multi-turn dialogues spanning 41 domains and incorporating 20 distinct emotions with coherent emotional progressions.
This repository contains the XTTS-v2 variant of the dataset, where speech is generated usin... |
dataset | habit-anonymous/HABIT | habit-anonymous | 2026-05-05 | 2026-05-07 | [
"en"
] | cc-by-4.0 | [
"robotics"
] | [
"task_categories:robotics",
"language:en",
"license:cc-by-4.0",
"size_categories:1M<n<10M",
"format:parquet",
"modality:tabular",
"modality:text",
"modality:timeseries",
"modality:video",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us",
"robot-man... | 1M<n<10M | 15,832 | null | 4,051,250 | 172,157,202,723 | [] |
# HABIT: Human-Aware Behavior and Interaction Training Dataset for Robot Manipulation
[](https://creativecommons.org/licenses/by/4.0/)
[![LeRobot](https://img.shields.io/badge/LeRobot-v2.0-blue... |
dataset | anonymous21016/gaia_train_web_worker | anonymous21016 | 2025-05-16 | 2025-05-16 | [] | null | [] | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1K<n<10K | 14,599 | null | 1,897 | 78,177,149 | [] | |
dataset | anon-ops/ops-lite | anon-ops | 2026-05-07 | 2026-05-13 | [
"en"
] | apache-2.0 | [
"graph-ml",
"tabular-classification",
"other"
] | [
"task_categories:graph-ml",
"task_categories:tabular-classification",
"task_categories:other",
"task_ids:multi-class-classification",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"size_categories:n... | n<1K | 15,051 | null | 500 | 4,134,860,208 | [] |
# ops-lite
A curated 500-case root-cause-analysis (RCA) evaluation set for
microservice systems, with manifest-driven causal-graph ground truth.
Each case bundles:
- a chaos-injection ground truth (`injection.json`)
- a causal service graph derived from the injection's fault contract
(`causal_graph.json`)
- the r... |
dataset | Arshii/RAW_Agromet_Reports_2025_ENGLISH_JAN_FEB_MARCH | Arshii | 2026-04-29 | 2026-04-29 | [] | null | [] | [
"size_categories:10K<n<100K",
"modality:document",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 10K<n<100K | 14,769 | null | 11,277 | 5,704,874,760 | [] | |
dataset | opencompass/AIME2025 | opencompass | 2025-02-08 | 2025-02-25 | [
"en"
] | mit | [
"question-answering"
] | [
"task_categories:question-answering",
"language:en",
"license:mit",
"size_categories:n<1K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | n<1K | 14,277 | null | 30 | 14,958 | [] |
# AIME 2025 Dataset
## Dataset Description
This dataset contains problems from the American Invitational Mathematics Examination (AIME) 2025-I & II. |
dataset | HuggingFaceFW/finewiki | HuggingFaceFW | 2025-10-13 | 2025-10-22 | [] | cc-by-sa-4.0 | [
"text-generation"
] | [
"task_categories:text-generation",
"license:cc-by-sa-4.0",
"license:gfdl",
"size_categories:10M<n<100M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us"
] | 10M<n<100M | 14,597 | null | 61,550,610 | 198,431,212,547 | [] |
This is an **updated and better extracted** version of the `wikimedia/Wikipedia` dataset originally released in 2023. We carefully parsed [Wikipedia HTML dumps](https://dumps.wikimedia.org/other/enter... |
dataset | Logistic12/xhs-image-extractor-v2 | Logistic12 | 2026-03-17 | 2026-03-17 | [] | null | [] | [
"size_categories:1K<n<10K",
"format:imagefolder",
"modality:image",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | 1K<n<10K | 14,615 | null | 3,582 | 419,419,046 | [] | |
dataset | gaianet/web3_diary | gaianet | 2024-10-25 | 2024-10-25 | [] | mit | [] | [
"license:mit",
"size_categories:n<1K",
"format:text",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | n<1K | 14,384 | null | 103 | 7,769,832 | [] | |
dataset | AdaMLLab/TurMix | AdaMLLab | 2026-01-15 | 2026-01-30 | [
"tr"
] | other | [
"text-generation"
] | [
"task_categories:text-generation",
"language:tr",
"license:other",
"size_categories:100M<n<1B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"arxiv:2512.18834",
"arxiv:2506.20920",
"region:us"
] | 100M<n<1B | 14,508 | null | 680,726,602 | 1,180,629,720,722 | [
"2512.18834",
"2506.20920"
] |
<img src="https://huggingface.co/datasets/AdaMLLab/TurMix/resolve/main/finetasks_turkish_main_results.png" width="900" alt="Finetasks benchmark scores, showing TurMix-Matched as SOTA.">
<p align="center">
<a href="https://huggingface.co/collections/AdaMLLab/mixminmatch">
<img src="https://img.shields.io/badge/�... |
dataset | sriom1/electrical-panels-dataset | sriom1 | 2026-03-10 | 2026-03-20 | [] | null | [] | [
"modality:image",
"modality:text",
"region:us"
] | null | 16,053 | null | null | 9,441,704,148 | [] | # Electrical Panels Detection Dataset
Auto-scraped, CLIP-filtered, YOLOE-26 annotated.
Classes: 107
Target images per class: 500
Annotation: Two-pass YOLOE-26m + SAM
Teacher model: YOLOE-26m-seg
Student model: YOLO26n (knowledge distilled)
|
dataset | shamikbose89/gaia_traces | shamikbose89 | 2025-04-21 | 2025-04-21 | [] | null | [] | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1K<n<10K | 14,323 | null | 1,204 | 4,490,240 | [] | |
dataset | openbmb/Ultra-FineWeb-L3 | openbmb | 2026-02-07 | 2026-05-28 | [
"en",
"zh"
] | apache-2.0 | [
"text-generation"
] | [
"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:polars",
"library:mlcroissant",
"arxiv:2505.05427",
"arxiv:2602.09003",
"region:us",
"llm",
... | 1B<n<10B | 14,442 | null | 1,058,535,126 | 1,899,216,536,437 | [
"2505.05427",
"2602.09003"
] |
# Ultra-FineWeb-L3
<div align="center">
<img src="assets/ultra-fineweb-l3-logo.png" width="600"/>
</div>
<p align="center">
<a href="https://arxiv.org/abs/2505.05427">📜 Ultra-FineWeb Technical Report</a> |
<a href="https://huggingface.co/collections/openbmb/ultradata">📦 UltraData Collection</a> |
<a href="https:... |
dataset | dkatz238/gaianode-datasets | dkatz238 | 2024-09-12 | 2024-09-18 | [] | null | [] | [
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | 1K<n<10K | 14,206 | null | 3,915 | 137,255,956 | [] | |
dataset | Angelou0516/HCC-TACE-Seg | Angelou0516 | 2026-05-05 | 2026-05-05 | [] | cc-by-4.0 | [
"image-segmentation"
] | [
"task_categories:image-segmentation",
"license:cc-by-4.0",
"size_categories:n<1K",
"format:parquet",
"format:optimized-parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"medical",
"ct",
"liver",
"hep... | n<1K | 15,853 | null | 104 | 28,608,170,320 | [] |
# HCC-TACE-Seg
Multimodality annotated hepatocellular carcinoma data set including pre- and
post-TACE multiphasic contrast-enhanced CT with voxel-level segmentations of
liver, tumor mass, portal vein, and abdominal aorta.
## Dataset Details
| Field | Value |
|---|---|
| Modality | CT (multiphasic contrast-enhanced)... |
dataset | CohereLabs/beir-embed-english-v3 | CohereLabs | 2023-12-19 | 2026-03-25 | [] | null | [] | [
"size_categories:10M<n<100M",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"region:us"
] | 10M<n<100M | 14,210 | null | 50,490,419 | 102,978,702,551 | [] |
# BEIR embeddings with Cohere embed-english-v3.0 model
This datasets contains all query & document embeddings for [BEIR](https://github.com/beir-cellar/beir), embedded with the [Cohere embed-english-v3.0](https://huggingface.co/CohereLabs/Cohere-embed-english-v3.0) embedding model.
## Overview of datasets
This rep... |
dataset | ceselder/loracle-fineweb-data | ceselder | 2026-04-09 | 2026-04-21 | [] | null | [] | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us"
] | 10K<n<100K | 14,306 | null | 10,957 | 408,054,945,216 | [] |
# ceselder/loracle-fineweb-data
Per-LoRA train / dpo_heldout / test split. Splits are disjoint on `doc_id` —
all QA rows for a given LoRA stay in the same split.
| Split | Rows | Unique `doc_id` |
|---|---:|---:|
| train | 10657 | 10657 |
| dpo_heldout | 200 | 200 |
| test | 100 | 100 |
Split ID manifest (exhaustiv... |
dataset | BangumiBase/zenshuu | BangumiBase | 2025-08-07 | 2025-08-07 | [] | mit | [] | [
"license:mit",
"size_categories:1K<n<10K",
"modality:image",
"modality:text",
"region:us",
"art"
] | 1K<n<10K | 14,165 | null | null | 11,305,489,974 | [] |
# Bangumi Image Base of Zenshuu.
This is the image base of bangumi Zenshuu., we detected 80 characters, 4266 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this ... |
dataset | UnipatAI/RoadmapBench | UnipatAI | 2026-05-08 | 2026-05-12 | [
"en"
] | mit | [
"text-generation"
] | [
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:n<1K",
"format:json",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"code-generation",
"software-engineering",
"benchmark",
... | n<1K | 15,346 | null | 115 | 1,350,834,096 | [] |
# RoadmapBench
A benchmark for evaluating AI coding agents on multi-target, long-horizon software development tasks derived from open-source project version upgrades.
## Overview
RoadmapBench contains **115 tasks** spanning **17 open-source repositories** across **5 programming languages** (Python, TypeScript, Go, ... |
dataset | Sugita-daichi/LoRA-Merge-Images | Sugita-daichi | 2026-04-20 | 2026-04-21 | [
"en"
] | creativeml-openrail-m | [
"image-to-image"
] | [
"task_categories:image-to-image",
"language:en",
"license:creativeml-openrail-m",
"size_categories:100K<n<1M",
"format:json",
"modality:image",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"lora",
"stable... | 100K<n<1M | 16,189 | null | 494,348 | 33,721,877,444 | [] | |
dataset | gsarti/flores_101 | gsarti | 2022-03-02 | 2022-10-27 | [
"af",
"am",
"ar",
"hy",
"as",
"ast",
"az",
"be",
"bn",
"bs",
"bg",
"my",
"ca",
"ceb",
"zho",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"tl",
"fi",
"fr",
"ff",
"gl",
"lg",
"ka",
"de",
"el",
"gu",
"ha",
"he",
"hi",
"hu",
"is",
"ig",
"id",
"ga",
"... | cc-by-sa-4.0 | [
"text-generation",
"translation"
] | [
"task_categories:text-generation",
"task_categories:translation",
"annotations_creators:found",
"language_creators:expert-generated",
"multilinguality:multilingual",
"multilinguality:translation",
"source_datasets:extended|flores",
"language:af",
"language:am",
"language:ar",
"language:hy",
"l... | 100K<n<1M | 14,135 | [
"multilingual",
"translation"
] | 206,927 | 293,274 | [
"2106.03193"
] |
# Dataset Card for Flores 101
## Table of Contents
- [Dataset Card for Flores 101](#dataset-card-for-flores-101)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderbo... |
dataset | OptimalScale/ClimbLab | OptimalScale | 2025-04-18 | 2025-05-04 | [
"en"
] | apache-2.0 | [
"text-generation"
] | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"size_categories:1B<n<10B",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2504.13161",
"region:us"
] | 1B<n<10B | 14,174 | null | 1,668,456 | 3,077,652,006,117 | [
"2504.13161"
] | [ClimbLab](https://huggingface.co/datasets/nvidia/ClimbLab) is a high-quality pre-training corpus released by NVIDIA. Here is the description:
>ClimbLab is a filtered 1.2-trillion-token corpus with 20 clusters.
Based on Nemotron-CC and SmolLM-Corpus, we employed our proposed CLIMB-clustering to semantically reorganiz... |
dataset | bigcode/bigcodebench-hard | bigcode | 2024-09-14 | 2025-02-23 | [] | null | [] | [
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | n<1K | 14,070 | null | 740 | 2,761,946 | [] | |
dataset | mlabonne/harmful_behaviors | mlabonne | 2024-05-28 | 2024-06-04 | [
"en"
] | null | [] | [
"language:en",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | n<1K | 14,067 | null | 520 | 23,169 | [] | |
dataset | ForceBody/ForceBody_ano | ForceBody | 2026-05-07 | 2026-05-07 | [
"en"
] | cc-by-4.0 | [
"other"
] | [
"task_categories:other",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"region:us",
"3d-human-body-models",
"force-paired-dataset",
"uncer... | 10K<n<100K | 14,554 | null | 10,386 | 19,811,854,035 | [] |
# ForceBody
ForceBody pairs the SKEL parametric body model with measured ground reaction forces and inverse-dynamics joint torques across 10,386 motion trials (26.9 hours of motion at 100 Hz) from 140 subjects. A subset of 8,652 trials additionally ships per-frame, per-joint Monte Carlo uncertainty `sigma_tau` for ev... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.