datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
FineFineWeb
FineFineWeb: A Comprehensive Study on Fine-Grained Domain Web Corpus
arXiv: Coming Soon
Project Page: Coming Soon
Blog: Coming Soon
Data Statistics
Domain (#tokens/#samples)
Iteration 1 Tokens
Iteration 2 Tokens
Iteration 3 Tokens
Total Tokens
Iteration 1 Count
Iteration 2 Count
Iteration 3 Count
Total Count
aerospace
5.77B
261.63M
309.33M
6.34B
9100000
688505
611034
10399539
agronomy
13.08B
947.41M
229.04M
14.26B
15752828
2711790
649404
19114022… See the full description on the dataset page: https://huggingface.co/datasets/m-a-p/FineFineWeb.wikitext
Dataset Card for "wikitext"
Dataset Summary
The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified
Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike License.
Compared to the preprocessed version of Penn Treebank (PTB), WikiText-2 is over 2 times larger and WikiText-103 is over
110 times larger. The WikiText dataset also features a far… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/wikitext.gsm8k
Dataset Card for GSM8K
Dataset Summary
GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.
These problems take between 2 and 8 steps to solve.
Solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to… See the full description on the dataset page: https://huggingface.co/datasets/openai/gsm8k.c4
C4
Dataset Summary
A colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: "https://commoncrawl.org".
This is the processed version of Google's C4 dataset
We prepared five variants of the data: en, en.noclean, en.noblocklist, realnewslike, and multilingual (mC4).
For reference, these are the sizes of the variants:
en: 305GB
en.noclean: 2.3TB
en.noblocklist: 380GB
realnewslike: 15GB
multilingual (mC4): 9.7TB (108 subsets, one… See the full description on the dataset page: https://huggingface.co/datasets/allenai/c4.fine-news
Fine-News
Fine-News is a multilingual corpus of 635,068,343 news documents from the INFINI-NEWS Corpus.
INFINI-NEWS extracts article text from Common Crawl News web archives.
Fine-News applies language checks, quality filters, and duplicate removal to that text, and stores each retained document with a sampling weight.
The corpus covers August 2016 through April 2026, using the month of the web crawl.
At a glance
Measure
Value
News documents
635,068,343… See the full description on the dataset page: https://huggingface.co/datasets/ksolovev/fine-news.MINT-1T-HTML
🍃 MINT-1T:Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens
🍃 MINT-1T is an open-source Multimodal INTerleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. 🍃 MINT-1T is created by a team from the University of Washington in… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations/MINT-1T-HTML.FineWeb-HQ
FineWeb-HQ
Dataset Summary
FineWeb-HQ is a high-quality, model-filtered pretraining dataset derived as a subset of FineWeb. FineWeb-HQ was created by selecting the top 10% of FineWeb documents based on a deep learning classifier trained to identify structured and knowledge-rich samples. This classifier uses XLM-RoBERTa embeddings to score documents.
To validate our approach, we pretrained 1B-parameter LLM models with a Llama-like architecture across multiple… See the full description on the dataset page: https://huggingface.co/datasets/epfml/FineWeb-HQ.fineweb-edu
📚 FineWeb-Edu
1.3 trillion tokens of the finest educational data the 🌐 web has to offer
Paper: https://arxiv.org/abs/2406.17557
What is it?
📚 FineWeb-Edu dataset consists of 1.3T tokens and 5.4T tokens (FineWeb-Edu-score-2) of educational web pages filtered from 🍷 FineWeb dataset. This is the 1.3 trillion version.
To enhance FineWeb's quality, we developed an educational quality classifier using annotations generated by LLama3-70B-Instruct. We… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu.fineweb
🍷 FineWeb
15 trillion tokens of the finest data the 🌐 web has to offer
What is it?
The 🍷 FineWeb dataset consists of more than 18.5T tokens (originally 15T tokens) of cleaned and deduplicated english web data from CommonCrawl. The data processing pipeline is optimized for LLM performance and ran on the 🏭 datatrove library, our large scale data processing library.
🍷 FineWeb was originally meant to be a fully open replication of 🦅 RefinedWeb, with a… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb.NuminaMath-CoT
Dataset Card for NuminaMath CoT
Dataset Summary
Approximately 860k math problems, where each solution is formatted in a Chain of Thought (CoT) manner. The sources of the dataset range from Chinese high school math exercises to US and international mathematics olympiad competition problems. The data were primarily collected from online exam paper PDFs and mathematics discussion forums. The processing steps include (a) OCR from the original PDFs, (b) segmentation… See the full description on the dataset page: https://huggingface.co/datasets/AI-MO/NuminaMath-CoT.wikipedia
Dataset Card for Wikimedia Wikipedia
Dataset Summary
Wikipedia dataset containing cleaned articles of all languages.
The dataset is built from the Wikipedia dumps (https://dumps.wikimedia.org/)
with one subset per language, each containing a single train split.
Each example contains the content of one full Wikipedia article with cleaning to strip
markdown and unwanted sections (references, etc.).
All language subsets have already been processed for recent dump… See the full description on the dataset page: https://huggingface.co/datasets/wikimedia/wikipedia.fineweb-edu-translated
Helsinki-NLP/fineweb-edu-translated
fineweb-edu-tanslated is a collection of automatically translated documents from fineweb-edu.
Translations are based on OPUS-MT and HPLT-MT models.
The data in v1.0 covers 36,704,000 documents with over 28 billion space-searated tokens of English data translated into 36 languages.
The total v1.0 data set includes over 960 billion tokens and the translated documents are aligned across all languages.
In the v1.1 release, additional translations… See the full description on the dataset page: https://huggingface.co/datasets/Helsinki-NLP/fineweb-edu-translated.jat-dataset
JAT Dataset
Dataset Description
The Jack of All Trades (JAT) dataset combines a wide range of individual datasets. It includes expert demonstrations by expert RL agents, image and caption pairs, textual data and more. The JAT dataset is part of the JAT project, which aims to build a multimodal generalist agent.
Paper: https://huggingface.co/papers/2402.09844
Usage
>>> from datasets import load_dataset
>>> dataset =… See the full description on the dataset page: https://huggingface.co/datasets/jat-project/jat-dataset.IFEval
Dataset Card for IFEval
Dataset Summary
This dataset contains the prompts used in the Instruction-Following Eval (IFEval) benchmark for large language models. It contains around 500 "verifiable instructions" such as "write in more than 400 words" and "mention the keyword of AI at least 3 times" which can be verified by heuristics. To load the dataset, run:
from datasets import load_dataset
ifeval = load_dataset("google/IFEval")
Supported Tasks and… See the full description on the dataset page: https://huggingface.co/datasets/google/IFEval.HPLT2.0_cleanedNB: HPLT2.0 is now superseded by a newer release:
HPLT3.0
We recommed switching to v3.0, unless you have a compelling reason to stay on 2.0.
This is a large-scale collection of web-crawled documents in 191 world languages, produced by the HPLT project.
The source of the data is mostly Internet Archive with some additions from Common Crawl.
For a detailed description of the dataset, please refer to our website and our pre-print.
The Cleaned variant of HPLT Datasets v2.0
This is… See the full description on the dataset page: https://huggingface.co/datasets/HPLT/HPLT2.0_cleaned.MATH-500
Dataset Card for MATH-500
This dataset contains a subset of 500 problems from the MATH benchmark that OpenAI created in their Let's Verify Step by Step paper. See their GitHub repo for the source file: https://github.com/openai/prm800k/tree/main?tab=readme-ov-file#math-splits
arxiv-complete
arXiv Complete Corpus
A snapshot of arXiv's metadata, version history, submission files and rendered
documents. It covers 3,148,796 papers and includes file contents, paths, sizes
and SHA-256 digests. Metadata comes from arXiv's OAI-PMH arXivRaw interface;
files come from the GCS mirror, S3 source archives and direct PDF fetches.
This release holds a PDF for 99.47% of papers and 99.54% of versions reported
with a non-zero submission size. It is a one-off snapshot; coverage gaps… See the full description on the dataset page: https://huggingface.co/datasets/secemp9/arxiv-complete.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.finephrase
Dataset Card for HuggingFaceFW/finephrase
Dataset Summary
Synthetic data generated by DataTrove:
Model: HuggingFaceTB/SmolLM2-1.7B-Instruct (main)
Source dataset: HuggingFaceFW/fineweb-edu, config sample-350BT, split train
Generation config: temperature=1.0, top_p=1.0, top_k=50, max_tokens=2048, model_max_context=8192
Speculative decoding: {"method":"suffix","num_speculative_tokens":32}
System prompt: None
Input column: text
Prompt families:
faq prompt
Rewrite… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/finephrase.MathNet
Quick Start · Overview · Tasks · Comparison · Dataset Stats · Data Sources · Pipeline · Schema · License · Citation
This is the official MathNet v0. A larger version v1 will be uploaded soon (more countires, problems and richer metadata). Schema is stable but field values may be revised in v1.
Quick start
from datasets import load_dataset
# Default: all problems
ds = load_dataset("ShadenA/MathNet", split="train")
# Or a specific country / competition-body config… See the full description on the dataset page: https://huggingface.co/datasets/ShadenA/MathNet.truthful_qa
Dataset Card for truthful_qa
Dataset Summary
TruthfulQA is a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. Questions are crafted so that some humans would answer falsely due to a false belief or misconception. To perform well, models must avoid generating false answers learned from imitating human texts.… See the full description on the dataset page: https://huggingface.co/datasets/truthfulqa/truthful_qa.fineweb-2
🥂 FineWeb2
A sparkling update with 1000s of languages
What is it?
This is the second iteration of the popular 🍷 FineWeb dataset, bringing high quality pretraining data to over 1000 🗣️ languages.
The 🥂 FineWeb2 dataset is fully reproducible, available under the permissive ODC-By 1.0 license and extensively validated through hundreds of ablation experiments.
In particular, on the set of 9 diverse languages we used to guide our processing decisions, 🥂… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb-2.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.gpqa
Dataset Card for GPQA
GPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy, despite spending >30m with full access to Google.
We request that you do not reveal examples from this dataset in plain text or images online, to reduce the risk of leakage into foundation… See the full description on the dataset page: https://huggingface.co/datasets/Idavidrein/gpqa.demo_data
1,000 examples from https://huggingface.co/datasets/llamafactory/alpaca_gpt4_en
1,000 examples from https://huggingface.co/datasets/llamafactory/alpaca_gpt4_zh
300 examples from https://huggingface.co/datasets/llamafactory/glaive_toolcall_en
300 examples from https://huggingface.co/datasets/llamafactory/glaive_toolcall_zh
91 examples for identity learning
300 examples from https://huggingface.co/datasets/cognitivecomputations/SystemChat-2.0
6 examples for multimodal supervised… See the full description on the dataset page: https://huggingface.co/datasets/llamafactory/demo_data.NuminaMath-1.5
Dataset Card for NuminaMath 1.5
Dataset Summary
This is the second iteration of the popular NuminaMath dataset, bringing high quality post-training data for approximately 900k competition-level math problems. Each solution is formatted in a Chain of Thought (CoT) manner. The sources of the dataset range from Chinese high school math exercises to US and international mathematics olympiad competition problems. The data were primarily collected from online exam paper PDFs… See the full description on the dataset page: https://huggingface.co/datasets/AI-MO/NuminaMath-1.5.OpenMathInstruct-2
OpenMathInstruct-2
OpenMathInstruct-2 is a math instruction tuning dataset with 14M problem-solution pairs
generated using the Llama3.1-405B-Instruct model.
The training set problems of GSM8K
and MATH are used for constructing the dataset in the following ways:
Solution augmentation: Generating chain-of-thought solutions for training set problems in GSM8K and MATH.
Problem-Solution augmentation: Generating new problems, followed by solutions for these new problems.… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenMathInstruct-2.UbuntuIRCCompletely uncurated collection of IRC logs from the Ubuntu IRC channels
SWE-smith
SWE-smith Dataset
Code
•
Paper
•
Site
[12/14/2025] NOTE: We will no longer actively update this dataset.
While this dataset is still functional and usable, we recommend you use the `SWE-bench/SWE-smith-[lang]` datasets.
For better maintainability and ease-of-use, we are maintaining language-specific datasets in lieu of this mono-repo.
The SWE-smith Dataset is a training dataset of 50137 task instances from 128 GitHub repositories, collected using the SWE-smith toolkit.… See the full description on the dataset page: https://huggingface.co/datasets/SWE-bench/SWE-smith.alpaca
Dataset Card for Alpaca
Dataset Summary
Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's text-davinci-003 engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.
The authors built on the data generation pipeline from Self-Instruct framework and made the following modifications:
The text-davinci-003 engine to generate the instruction data… See the full description on the dataset page: https://huggingface.co/datasets/tatsu-lab/alpaca.