datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
documentation-images
This dataset contains images used in the documentation of HuggingFace's libraries.
HF Team: Please make sure you optimize the assets before uploading them.
My favorite tool for this is https://tinypng.com/.
doc-build-devThis is a dataset which contains the docs from all the PRs that are updating one of the docs from https://huggingface.co/docs.
It is automatically updated by this github action from the doc-buider repo.
documentation-imagesdoc-buildThis repo contains all the docs published on https://huggingface.co/docs.
The docs are generated with https://github.com/huggingface/doc-builder.
document-haystack
Document Haystack Dataset
This repository contains the dataset for the paper “Document Haystack: A Long Context Multimodal Image/Document Understanding Vision LLM Benchmark”.
📑 Abstract Paper
The proliferation of multimodal Large Language Models has significantly advanced the ability to analyze and understand complex data inputs from different modalities. However, the processing of long documents remains under-explored, largely due to a lack of suitable benchmarks. To… See the full description on the dataset page: https://huggingface.co/datasets/AmazonScience/document-haystack.wikitext_document_level
Wikitext Document Level
This is a modified version of https://huggingface.co/datasets/wikitext that returns Wiki pages instead of Wiki text line-by-line. The original readme is contained below.
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… See the full description on the dataset page: https://huggingface.co/datasets/EleutherAI/wikitext_document_level.VDR_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.transformers.js-docsDocmatix
Dataset Card for Docmatix
Dataset description
Docmatix is part of the Idefics3 release (stay tuned).
It is a massive dataset for Document Visual Question Answering that was used for the fine-tuning of the vision-language model Idefics3.
Load the dataset
To load the dataset, install the library datasets with pip install datasets. Then,
from datasets import load_dataset
ds = load_dataset("HuggingFaceM4/Docmatix")
If you want the dataset to link to the pdf files… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceM4/Docmatix.documentation-imagesDocVQA
Large-scale Multi-modality Models Evaluation Suite
Accelerating the development of large-scale multi-modality models (LMMs) with lmms-eval
🏠 Homepage | 📚 Documentation | 🤗 Huggingface Datasets
This Dataset
This is a formatted version of DocVQA. It is used in our lmms-eval pipeline to allow for one-click evaluations of large multi-modality models.
@article{mathew2020docvqa,
title={DocVQA: A Dataset for VQA on Document Images. CoRR abs/2007.00398 (2020)}… See the full description on the dataset page: https://huggingface.co/datasets/lmms-lab-encoder/DocVQA.documentation-mediacc-doc-linksdocTR-resource-collectiondocumentation-imagesDocHPLT
DocHPLT: A Massively Multilingual Document-Level Translation Dataset
Existing document-level machine translation resources are only available for a handful of languages, mostly high-resourced ones. To facilitate the training and evaluation of document-level translation and, more broadly, long-context modeling for global communities, we create DocHPLT, the largest publicly available document-level translation dataset to date. It contains 124 million aligned document pairs across 50… See the full description on the dataset page: https://huggingface.co/datasets/HPLT/DocHPLT.policy-docs
Public Policy at Hugging Face
AI Policy at Hugging Face is a multidisciplinary and cross-organizational workstream. Instead of being part of a vertical communications or global affairs organization, our policy work is rooted in the expertise of our many researchers and developers, from Ethics and Society Regulars and legal team to machine learning engineers working on healthcare, art, and evaluations.
What we work on is informed by our Hugging Face community needs and experiences… See the full description on the dataset page: https://huggingface.co/datasets/huggingface/policy-docs.docredMultiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs. In order to accelerate the research on document-level RE, we introduce DocRED, a new dataset constructed from Wikipedia and Wikidata with three features:
- DocRED annotates both named entities and relations, and is the largest human-annotated dataset for document-level RE from plain text.
- DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document.
- Along with the human-annotated data, we also offer large-scale distantly supervised data, which enables DocRED to be adopted for both supervised and weakly supervised scenarios.docs-imagesMMLongBench-DocSaaS-Bench-docker
SaaS-Bench Docker Images
Docker image archives for the SaaS-Bench benchmark — a suite of 23
self-hosted SaaS applications used to evaluate computer-use LLM agents on
real, multi-step business workflows.
This repository hosts the prebuilt .tar images (≈ 63 GB total) so you
can reproduce the benchmark environment without rebuilding each app from
source. The eval harness, task definitions, and verifiers live in the main
SaaS-Bench repository.
Paper: SaaS-Bench: Can Computer-Use… See the full description on the dataset page: https://huggingface.co/datasets/Marti844/SaaS-Bench-docker.diffusers-images-docsdocumentation-imagesregression-dataset-for-docling-parse
Regression Dataset for docling-parse
This repository contains the reference dataset used as a regression test corpus for
docling-parse.
Its purpose is to make parser and renderer changes safe: when behavior changes in
docling-parse, the test
suite can compare the current output against the expected artifacts stored in this
dataset.
Correct workflow to add new files
cp /path/to/new.pdf regression/new.pdf
git add regression/new.pdf
git lfs status
git commit -s -m… See the full description on the dataset page: https://huggingface.co/datasets/docling-project/regression-dataset-for-docling-parse.documentation-imagesThis dataset contains images used in the documentation of HuggingFace's Optimum library.
foia-reading-room-documents
Foia Reading Room Documents
Documents from federal FOIA reading rooms and Inspector General report libraries: audits, inspections, investigative summaries and records released under the Freedom of Information Act.
Every document here was published by a US federal agency and is a work of the
United States government. Nothing has been altered: files are byte-identical to
what the agency posted, and the checksum in metadata.parquet is of the
original bytes.
Why this… See the full description on the dataset page: https://huggingface.co/datasets/abigailhaddad/foia-reading-room-documents.DoclingMatix
DoclingMatix
DoclingMatix is a large-scale, multimodal dataset designed for training vision-language models in the domain of document intelligence. It was created specifically for training the SmolDocling model, an ultra-compact model for end-to-end document conversion.
The dataset is constructed by augmenting Hugging Face's Docmatix. Each sample in Docmatix, which consists of a document image and a few questions and answers about it, has been transformed. The text field is now… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceM4/DoclingMatix.dolma3-6t-sample-100000-docs
dolma3-6t-sample-100000-docs
Materialized stratified sample of 100,000 docs per bin (~58k source-shard files, ~186 GB) from the deduplicated 6T Dolma3 corpus. Seed 42, materialized by 128 Modal workers (worker_0000/ through worker_0127/).
Layout
HCAI-Lab/dolma3-6t-sample-100000-docs/
├── bin_summary.csv
├── sample_contract.json
└── worker_NNNN/
└── soc127__phase1_pool_shared__...__shard_NNNNNNNN.jsonl.zst (~450 files per worker)
Dual-access… See the full description on the dataset page: https://huggingface.co/datasets/HCAI-Lab-GT/dolma3-6t-sample-100000-docs.docinsights-2026-shared-task-data
DocInsights 2026 Shared Task: DocSem
Document-grounded quantitative reasoning with evidence attribution
DocSem is the shared task of DocInsights 2026, the Workshop on Document Intelligence and Understanding co-located with EMNLP 2026 in Budapest, Hungary. The workshop theme is Beyond Plain Text: Bridging NLP and Document AI.
Workshop shared task | Source repository | Submission portal | Participant guide
Participants receive a PDF document and a paraphrased user_query. Systems… See the full description on the dataset page: https://huggingface.co/datasets/amitbcp/docinsights-2026-shared-task-data.DocLayNet-v1.2
Dataset Card for DocLayNet v1.2
Dataset Summary
This dataset is an extention of the original DocLayNet dataset which embeds the PDF files of the document images inside a binary column.
DocLayNet provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories. It provides several unique features compared to related work such as PubLayNet or DocBank:
Human Annotation: DocLayNet is… See the full description on the dataset page: https://huggingface.co/datasets/docling-project/DocLayNet-v1.2.