datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
standard-chess-games
[!CAUTION]
This dataset is still a work in progress and some breaking changes might occur.
Lichess Rated Standard Chess Games Dataset
Dataset Description
6,771,826,271 standard rated games, played on lichess.org, updated monthly from the database dumps.
This version of the data is meant for data analysis. If you need PGN files you can find those here. That said, once you have a subset of interest, it is trivial to convert it back to PGN as shown in the Dataset Usage… See the full description on the dataset page: https://huggingface.co/datasets/Lichess/standard-chess-games.nonmyopia_resultspkgsrchttps://github.com/stal-ix/stal-ix.github.io/blob/main/MIRROR.md
gpic
GPIC: A Giant Permissive Image Corpus for Visual Generation
Keshigeyan Chandrasegaran*1,
Kyle Sargent*1,
Suchir Agarwal1,
Michael Jang1,
Michael Poli1,2,
Juan Carlos Niebles1,4,
Justin Johnson3,
Jiajun Wu1,
Li Fei-Fei1
1 Stanford University
2 Radical Numerics
3 University of Michigan
4 Salesforce… See the full description on the dataset page: https://huggingface.co/datasets/stanford-vision-lab/gpic.stereo-550Stereo-550
Paper ·
Code ·
Build it yourself ·
3D viewer ·
Blog
Collected with FPV Labs Open-Source Stereo Hardware
Dataset overview
A first-person calibrated stereo RGB video dataset capturing everyday human manipulation across objects, materials, tools, and multi-step activities. Every session is recorded as a synchronized left/right camera pair with per-session stereo calibration, giving the visual geometry of hands, object interaction, state… See the full description on the dataset page: https://huggingface.co/datasets/fpvlabs/stereo-550.stack-v3-train
🥞 The Stack v3
What is it?
What is being released
How to download and use it
Dataset statistics
Dataset structure
Dataset creation
Considerations for using the data
Additional information
What is it?
The Stack v3 is the largest, most up-to-date open dataset of source code, crawled directly from GitHub and built to pre-train code LLMs with full-repository context. It is the successor to The Stack v2 and, like its predecessor, is released to make the training… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceCode/stack-v3-train.imdb
Dataset Card for "imdb"
Dataset Summary
Large Movie Review Dataset.
This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed
Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/stanfordnlp/imdb.fusion-dwdatacomp200m
Datacomp200m
This is a smaller version of the datacomp_1b dataset.
Filtering was done by taking all rows that had self similarity (inner product) above 0.32. This resulted in 213009083 (213 million) rows.
The results of the datacomp paper suggest that filtering by CLIP score is better than random sampling.
Included in this repo are search indices created using autofaiss, over the text and image embeddings. There are two ways to access metadata, either in .parquet files in the… See the full description on the dataset page: https://huggingface.co/datasets/adams-story/datacomp200m.MegaPairs-Standard
MegaPairs-Standard (Standardized Version)
Dataset Summary
This is a standardized, high-efficiency version of the JUNJIE99/MegaPairs dataset.
Why use this version?
The original dataset is distributed as a massive Tar archive containing millions of images, accompanied by a separate JSONL annotation file.
The Problem: Using the original format requires extracting terabytes of small files (which can exhaust disk inodes) or writing complex logic to read from archives. It… See the full description on the dataset page: https://huggingface.co/datasets/86Cao/MegaPairs-Standard.snli
Dataset Card for SNLI
Dataset Summary
The SNLI corpus (version 1.0) is a collection of 570k human-written English sentence pairs manually labeled for balanced classification with the labels entailment, contradiction, and neutral, supporting the task of natural language inference (NLI), also known as recognizing textual entailment (RTE).
Supported Tasks and Leaderboards
Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), is the… See the full description on the dataset page: https://huggingface.co/datasets/stanfordnlp/snli.sts12-sts
STS12
An MTEB dataset
Massive Text Embedding Benchmark
SemEval-2012 Task 6.
Task category
t2t
Domains
Encyclopaedic, News, Written
Reference
https://www.aclweb.org/anthology/S12-1051.pdf
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["STS12"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
To learn more about how to… See the full description on the dataset page: https://huggingface.co/datasets/mteb/sts12-sts.State-Parse-FilteredThe single cell RNA-seq dataset with human PBMC samples was sourced from Parse Biosciences [1]. [1] Performance of Evercode™ WT v3 in Human Immune Cells (PBMCs), https://www.parsebiosciences.com/datasets/performance-of-evercode-wt-v3-in-human-immune-cells-pbmcs/; Parse Biosciences, Seattle, USA; accessed 05/27/2025.
Certain uses of this data may require a license from Parse Biosciences, Inc.
faiss-smollm
FAISS-Based Novelty Detection for SmolLM and SmolLM2
This tutorial demonstrates how to the measure novelty of text queries with respect to the provided SmolLM and SmolLM2 pretraining corpora, with optional ColBERTv2 re-ranking for improved precision.
Overview
The pipeline consists of four main steps:
Generate Embeddings - Encode your queries using a sentence transformer
FAISS Search - Retrieve top-K most similar documents from the pretraining corpus
Combine… See the full description on the dataset page: https://huggingface.co/datasets/stai-tuebingen/faiss-smollm.ralph-v2-statusstarcoderdata
StarCoder Training Dataset
Dataset description
This is the dataset used for training StarCoder and StarCoderBase. It contains 783GB of code in 86 programming languages, and includes 54GB GitHub Issues + 13GB Jupyter notebooks in scripts and text-code pairs,
and 32GB of GitHub commits, which is approximately 250 Billion tokens.
Dataset creation
The creation and filtering of The Stack is explained in the original dataset, we additionally decontaminate and… See the full description on the dataset page: https://huggingface.co/datasets/bigcode/starcoderdata.sst2
Dataset Card for [Dataset Name]
Dataset Summary
The Stanford Sentiment Treebank is a corpus with fully labeled parse trees that allows for a complete analysis of the
compositional effects of sentiment in language. The corpus is based on the dataset introduced by Pang and Lee (2005)
and consists of 11,855 single sentences extracted from movie reviews. It was parsed with the Stanford parser and
includes a total of 215,154 unique phrases from those parse trees, each… See the full description on the dataset page: https://huggingface.co/datasets/stanfordnlp/sst2.my-storagests22-crosslingual-sts
STS22.v2
An MTEB dataset
Massive Text Embedding Benchmark
SemEval 2022 Task 8: Multilingual News Article Similarity. Version 2 filters updated on STS22 by removing pairs where one of entries contain empty sentences.
Task category
t2t
Domains
News, Written
Reference
https://competitions.codalab.org/competitions/33835
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/sts22-crosslingual-sts.llm-network-study-data
LLM-Network-Study-Data
Per-request network captures (.pcapng) collected by the
LLM-Network-Study benchmark harness (benchmark.py and the
per-workload test scripts). Each directory holds one capture file per request,
named request_<id>_run<n>_<timestamp>.pcapng.
A directory name encodes four dimensions:
<capture-env>_<provider/model>_<workload>[_<dataset/variant>]_results
Dimension legend
Dimension
Values
Meaning
Capture env
ethernet
Wired connection to… See the full description on the dataset page: https://huggingface.co/datasets/wayslab/llm-network-study-data.the-stack-smol
Dataset Description
A small subset (~0.1%) of the-stack dataset, each programming language has 10,000 random samples from the original dataset. The dataset has 2.6GB of text (code).
Languages
The dataset contains 30 programming languages:
"assembly", "batchfile", "c++", "c", "c-sharp", "cmake", "css", "dockerfile", "fortran", "go", "haskell", "html", "java",
"javascript", "julia", "lua", "makefile", "markdown", "perl", "php", "powershell", "python", "ruby", "rust"… See the full description on the dataset page: https://huggingface.co/datasets/bigcode/the-stack-smol.store-aux-07
store-aux-07
binary runtime assets
sickr-sts
SICK-R
An MTEB dataset
Massive Text Embedding Benchmark
Semantic Textual Similarity SICK-R dataset
Task category
t2t
Domains
Web, Written
Reference
https://aclanthology.org/L14-1314/
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["SICK-R"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
To learn more about how to run models… See the full description on the dataset page: https://huggingface.co/datasets/mteb/sickr-sts.strategic_game_chess
Chess
Recent advancements in artificial intelligence (AI) underscore the progress of reasoning and planning shown by recent generalist machine learning (ML) models. The progress can be boosted by datasets that can further boost these generic capabilities when used for training foundation models of various kind. This research initiative has generated extensive synthetic datasets from complex games — chess, Rubik's Cube, and mazes — to study facilitation and the advancement of these… See the full description on the dataset page: https://huggingface.co/datasets/laion/strategic_game_chess.sts13-sts
STS13
An MTEB dataset
Massive Text Embedding Benchmark
SemEval STS 2013 dataset.
Task category
t2t
Domains
Web, News, Non-fiction, Written
Reference
https://www.aclweb.org/anthology/S13-1004/
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["STS13"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
To learn more about how… See the full description on the dataset page: https://huggingface.co/datasets/mteb/sts13-sts.sts14-sts
STS14
An MTEB dataset
Massive Text Embedding Benchmark
SemEval STS 2014 dataset. Currently only the English dataset
Task category
t2t
Domains
Blog, Web, Spoken
Reference
https://www.aclweb.org/anthology/S14-1002
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["STS14"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
To… See the full description on the dataset page: https://huggingface.co/datasets/mteb/sts14-sts.imagenet1k-256-wds-latentsThe imagenet1k dataset in the webdataset format
Each image was resized so that the max side resolution is 256, making sure to preserve aspect ratio.
Each image was encoded to latents using the sixteen channel https://huggingface.co/ostris/vae-kl-f8-d16
No cropping was used to encode to latents!
The resulting dataset has images in their original aspect ratio, but much smaller, and encodeded with a vae.
Bespoke-Stratos-17k
Bespoke-Stratos-17k
We replicated and improved the Berkeley Sky-T1 data pipeline using SFT distillation data
from DeepSeek-R1 to create Bespoke-Stratos-17k -- a reasoning dataset of questions, reasoning traces, and answers.
This data was used to train:
Bespoke-Stratos-32B, a 32B reasoning model which is a fine-tune of Qwen-2.5-32B-Instruct
Bespoke-Stratos-7B, a 7B reasoning model which is a fine-tune of Qwen-2.5-7B-Instruct.
Metrics for Bespoke-Stratos-32B… See the full description on the dataset page: https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k.biosses-sts
BIOSSES
An MTEB dataset
Massive Text Embedding Benchmark
Biomedical Semantic Similarity Estimation.
Task category
t2t
Domains
Medical
Reference
https://tabilab.cmpe.boun.edu.tr/BIOSSES/DataSet.html
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["BIOSSES"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
To learn more… See the full description on the dataset page: https://huggingface.co/datasets/mteb/biosses-sts.SteamScreenshots-Bugs
Samples