datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
OpenThoughts-1k-sample
[!NOTE]
We have released a paper for OpenThoughts! See our paper here.
Open-Thoughts-1k-sample
This is a 1k sample of the OpenThoughts-114k dataset.
Open synthetic reasoning dataset with high-quality examples covering math, science, code, and puzzles!
Inspect the content with rich formatting with Curator Viewer.
Available Subsets
default subset containing ready-to-train data used to finetune the OpenThinker-7B and OpenThinker-32B models:
ds =… See the full description on the dataset page: https://huggingface.co/datasets/ryanmarten/OpenThoughts-1k-sample.mbpp
Dataset Card for Mostly Basic Python Problems (mbpp)
Dataset Summary
The benchmark consists of around 1,000 crowd-sourced Python programming problems, designed to be solvable by entry level programmers, covering programming fundamentals, standard library functionality, and so on. Each problem consists of a task description, code solution and 3 automated test cases. As described in the paper, a subset of the data has been hand-verified by us.
Released here as part of… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/mbpp.hellaswag
Dataset Card for "hellaswag"
Dataset Summary
HellaSwag: Can a Machine Really Finish Your Sentence? is a new dataset for commonsense NLI. A paper was published at ACL2019.
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed
Dataset Structure
Data Instances
default
Size of downloaded dataset files: 71.49 MB
Size of the generated dataset: 65.32 MB
Total… See the full description on the dataset page: https://huggingface.co/datasets/Rowan/hellaswag.Rosetta-Activations
Rosetta Activations
Updated: 2026-06-15 02:30 UTC
Contrastive activation extractions for 17 semantic concepts across 46 language models,
supporting cross-architecture mechanistic interpretability research.
Companion concept pair corpus: jamesrahenry/Rosetta_Concept_Pairs
Papers: forthcoming
Dataset Structure
Rosetta-Activations/
├── rcp_v1/ # Current extraction line — richest data (N≈2000)
│ └── {Model_Name}/
│ ├── calibration_{concept}.npy… See the full description on the dataset page: https://huggingface.co/datasets/james-ra-henry/Rosetta-Activations.resultssquad
Dataset Card for SQuAD
Dataset Summary
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
SQuAD 1.1 contains 100,000+ question-answer pairs on 500+ articles.
Supported Tasks and Leaderboards
Question… See the full description on the dataset page: https://huggingface.co/datasets/rajpurkar/squad.paws
Dataset Card for PAWS: Paraphrase Adversaries from Word Scrambling
Dataset Summary
PAWS: Paraphrase Adversaries from Word Scrambling
This dataset contains 108,463 human-labeled and 656k noisily labeled pairs that feature the importance of modeling structure, context, and word order information for the problem of paraphrase identification. The dataset has two subsets, one based on Wikipedia and the other one based on the Quora Question Pairs (QQP) dataset.
For further… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/paws.RekaDaily-10k-raw
RekaDaily-10k (raw)
Raw, unscripted, first-person daily-life video, collected through
Claru, Reka's data collection marketplace — recorded by
paid collectors in their own homes and workplaces on head-mounted and handheld
phones, across multiple regions.
Videos are delivered as recorded — no cuts, no trimming, no editing, no
filtering beyond basic integrity checks. A processed tier (short clips with
machine captions) is released separately under the same RekaDaily-10k prefix.… See the full description on the dataset page: https://huggingface.co/datasets/RekaAI/RekaDaily-10k-raw.race
Dataset Card for "race"
Dataset Summary
RACE is a large-scale reading comprehension dataset with more than 28,000 passages and nearly 100,000 questions. The
dataset is collected from English examinations in China, which are designed for middle school and high school students.
The dataset can be served as the training and test sets for machine comprehension.
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed… See the full description on the dataset page: https://huggingface.co/datasets/ehovy/race.OpenR1-Math-220k
OpenR1-Math-220k
Dataset description
OpenR1-Math-220k is a large-scale dataset for mathematical reasoning. It consists of 220k math problems with two to four reasoning traces generated by DeepSeek R1 for problems from NuminaMath 1.5.
The traces were verified using Math Verify for most samples and Llama-3.3-70B-Instruct as a judge for 12% of the samples, and each problem contains at least one reasoning trace with a correct answer.
The dataset consists of two… See the full description on the dataset page: https://huggingface.co/datasets/open-r1/OpenR1-Math-220k.RealCam-Vid
RealCam-Vid Dataset
News
25/04/08: We provide torch dataset demo code for example usage of our RealCam-Vid.
25/03/26: Release our dataset RealCam-Vid v1 for metric-scale camera-controlled video generation, containing ~100K video clips with dedicated short/long captions and metric-scale camera annotations.
25/02/18: Initial commit of the project, we plan to release the full dataset and data processing code in several… See the full description on the dataset page: https://huggingface.co/datasets/MuteApo/RealCam-Vid.riddle_senserecipes
🦛 Chonkie Recipes 🍳
Chonkie loves to cook up a storm in the kitchen
This repository contains all the recipes that you can use with Chonkie to manage various documents, languages, and more.
Usage
To use the recipes, you need to install chonkie with the hub feature, with the following command:
pip install "chonkie[hub]"
This would enable Hubie which is used internally to get the recipes from this repository. So, you can do things like use the from_recipe… See the full description on the dataset page: https://huggingface.co/datasets/feyninc/recipes.SWE-rebench-V2
SWE-rebench-V2
Dataset Summary
SWE-rebench-V2 is a curated dataset of software-engineering tasks derived from real GitHub issues and pull requests. The dataset contains 32,079 samples covering Python, Go, TypeScript, JavaScript, Rust, Java, PHP, Kotlin, Julia, Elixir, Scala, Swift, Dart, C, C++, C#, R, Clojure, OCaml, and Lua.
For log parser functions, base Dockerfiles, and the prompts used, please see https://github.com/SWE-rebench/SWE-rebench-V2The detailed technical… See the full description on the dataset page: https://huggingface.co/datasets/nebius/SWE-rebench-V2.squad_v2
Dataset Card for SQuAD 2.0
Dataset Summary
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
SQuAD 2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers… See the full description on the dataset page: https://huggingface.co/datasets/rajpurkar/squad_v2.TinyStoriesDataset containing synthetically generated (by GPT-3.5 and GPT-4) short stories that only use a small vocabulary.
Described in the following paper: https://arxiv.org/abs/2305.07759.
The models referred to in the paper were trained on TinyStories-train.txt (the file tinystories-valid.txt can be used for validation loss). These models can be found on Huggingface, at roneneldan/TinyStories-1M/3M/8M/28M/33M/1Layer-21M.
Additional resources:
tinystories_all_data.tar.gz - contains a superset of… See the full description on the dataset page: https://huggingface.co/datasets/roneneldan/TinyStories.SWE-rebench
Dataset Summary
SWE-rebench is a large-scale dataset designed to support training and evaluation of LLM-based software engineering (SWE) agents, building upon and expanding our earlier release, SWE-bench-extra. It is constructed using a fully automated pipeline that continuously extracts real-world interactive SWE tasks from GitHub repositories at scale, as detailed in our paper SWE-rebench: An Automated Pipeline for Task Collection and Decontaminated Evaluation of Software… See the full description on the dataset page: https://huggingface.co/datasets/nebius/SWE-rebench.falcon-refinedweb
📀 Falcon RefinedWeb
Falcon RefinedWeb is a massive English web dataset built by TII and released under an ODC-By 1.0 license.
See the 📓 paper on arXiv for more details.
RefinedWeb is built through stringent filtering and large-scale deduplication of CommonCrawl; we found models trained on RefinedWeb to achieve performance in-line or better than models trained on curated datasets, while only relying on web data.
RefinedWeb is also "multimodal-friendly": it contains links and… See the full description on the dataset page: https://huggingface.co/datasets/tiiuae/falcon-refinedweb.debugtest3
requests
Open LLM Leaderboard Requests
This repository contains the request files of models that have been submitted to the Open LLM Leaderboard.
You can take a look at the current status of your model by finding its request file in this dataset. If your model failed, feel free to open an issue on the Open LLM Leaderboard! (We don't follow issues in this repository as often)
Evaluation Methodology
The evaluation process involves running your models against several benchmarks from… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard-old/requests.natural_questions
Dataset Card for Natural Questions
Dataset Summary
The NQ corpus contains questions from real users, and it requires QA systems to
read and comprehend an entire Wikipedia article that may or may not contain the
answer to the question. The inclusion of real user questions, and the
requirement that solutions should read an entire page to find the answer, cause
NQ to be a more realistic and challenging task than prior QA datasets.
Supported Tasks and Leaderboards… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/natural_questions.loc_chronicling_america_1770-1810
Dataset Card for Chronicling America: Historic American Newspapers 1770–1810
Dataset Summary
A dataset drawn from the Library of Congress Chronicling America digital collection, part of the National Digital Newspaper Program (NDNP). This dataset includes page-level records with images, original Chronicling America OCR, AI-generated OCR, and publication metadata for newspapers published between 1770 and 1810. It provides a foundation for research, machine learning… See the full description on the dataset page: https://huggingface.co/datasets/RevolutionCrossroads/loc_chronicling_america_1770-1810.R2E-Gym-LiteVDR_MEGA_MultiDomain_DocRetrieval
Visual Document Retrieval Dataset
Overview
This dataset is designed for training visual document retrieval models. It combines multiple datasets from the VDR series, Colpali, and LlamaIndex to create the most comprehensive training resource for visual document retrieval tasks.
Dataset Structure
The dataset contains structured fields including unique identifiers with string lengths ranging from 45 to 50 characters, search query text with variable lengths between… See the full description on the dataset page: https://huggingface.co/datasets/racineai/VDR_MEGA_MultiDomain_DocRetrieval.RobotDesign1M
RobotDesign1M: A Large-scale Dataset for Robot Design Understanding
RobotDesign1M is a large-scale, multimodal dataset for robot design understanding, built from image–text data curated from scientific literature across a wide range of robotics domains. It is designed to support research on design-aware foundation models, including design image generation, visual question answering about designs, and design image retrieval.
📄 Paper: RobotDesign1M: A Large-scale Dataset for… See the full description on the dataset page: https://huggingface.co/datasets/Fsoft-AIC/RobotDesign1M.no_robots
Dataset Card for No Robots 🙅♂️🤖
Look Ma, an instruction dataset that wasn't generated by GPTs!
Dataset Summary
No Robots is a high-quality dataset of 10,000 instructions and demonstrations created by skilled human annotators. This data can be used for supervised fine-tuning (SFT) to make language models follow instructions better. No Robots was modelled after the instruction dataset described in OpenAI's InstructGPT paper, and is comprised mostly of single-turn… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/no_robots.rotten_tomatoes
Dataset Card for "rotten_tomatoes"
Dataset Summary
Movie Review Dataset.
This is a dataset of containing 5,331 positive and 5,331 negative processed
sentences from Rotten Tomatoes movie reviews. This data was first used in Bo
Pang and Lillian Lee, ``Seeing stars: Exploiting class relationships for
sentiment categorization with respect to rating scales.'', Proceedings of the
ACL, 2005.
Supported Tasks and Leaderboards
More Information Needed
Languages… See the full description on the dataset page: https://huggingface.co/datasets/cornell-movie-review-data/rotten_tomatoes.SWE-Gym-RawSWE-Gym Raw contains 64,689 instances sourced from 358 Python repos.
Most of the instances there doesn't have associated python environment configured and is not validated with SWE-Bench verification process.
If you are working to scale training environments, these instances might be helpful.
Otherwise, please take a look at SWE-Gym and SWE-Gym Lite , why are ready to be used for agent training.
Get started at project page github.com/SWE-Gym/SWE-Gym
Repository
Frequency… See the full description on the dataset page: https://huggingface.co/datasets/SWE-Gym/SWE-Gym-Raw.reclorhttps://whyu.me/reclor/
@inproceedings{yu2020reclor,
author = {Yu, Weihao and Jiang, Zihang and Dong, Yanfei and Feng, Jiashi},
title = {ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning},
booktitle = {International Conference on Learning Representations (ICLR)},
month = {April},
year = {2020}
}
Code-Reasoning
Code-Reasoning
Dataset Description
Code problem-solving data with reasoning, direct-answer, and task-synthesis subsets. This repository is part of the K2 Horizon collection.
The repository is organized into multiple subsets. Every subset has a train split backed by Parquet shards, which supports Dataset Viewer inspection and streaming access.
K2 Horizon Dataset Series
Dataset repository
Focus
Subsets
IFM/TxT360-v2
Web and… See the full description on the dataset page: https://huggingface.co/datasets/IFM/Code-Reasoning.