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/.
documentation-imagesdocument-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.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.Docmatix
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-mediadocumentation-imagesdocs-imagesdiffusers-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.
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.fixtures_docvqaThis dataset includes 2 document images of the DocVQA dataset.
They are used for testing the LayoutLMv2FeatureExtractor + LayoutLMv2Processor inside the HuggingFace Transformers library.
More specifically, they are used in tests/test_feature_extraction_layoutlmv2.py and tests/test_processor_layoutlmv2.py.
PubTabNet_OTSL
Dataset Card for PubTabNet_OTSL
Dataset Summary
This dataset is a conversion of the original PubTabNet into the OTSL format presented in our paper "Optimized Table Tokenization for Table Structure Recognition". The dataset includes the original annotations amongst new additions.
Dataset Structure
cells: origunal dataset cell groundtruth (content).
otsl: new reduced table structure token format
html: original dataset groundtruth HTML (structure).
html_restored:… See the full description on the dataset page: https://huggingface.co/datasets/docling-project/PubTabNet_OTSL.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.SynthCodeNet
SynthCodeNet
SynthCodeNet is a multimodal dataset created for training the SmolDocling model. It consists of over 9.3 million synthetically generated image-text pairs, covering code snippets from 56 different programming languages. Text data was sourced from permissively licensed sources, while images were synthetically generated at 120 DPI using LaTeX and Pygments to ensure visual diversity.
Dataset Statistics
Total samples: 9,334,257
Training set: 8,400… See the full description on the dataset page: https://huggingface.co/datasets/docling-project/SynthCodeNet.pixmo-docs
PixMo-Docs
We now recommend using CoSyn-400k and CoSyn-point over these
datasets. They are improved versions with more images categories and an improved generation pipeline.
PixMo-Docs is a collection of synthetic question-answer pairs about various kinds of computer-generated images, including charts, tables, diagrams, and documents.
The data was created by using the Claude large language model to generate code that can be executed to render an image,
and using GPT-4o mini to… See the full description on the dataset page: https://huggingface.co/datasets/allenai/pixmo-docs.DocLayNet-v1.1
Dataset Card for DocLayNet v1.1
Dataset Summary
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 hand-annotated by well-trained experts, providing a gold-standard in layout segmentation through human recognition and interpretation of… See the full description on the dataset page: https://huggingface.co/datasets/docling-project/DocLayNet-v1.1.documentation-imagesdocumentation-imagestransformers_image_docPubTables-1M_OTSL
Dataset Card for PubTables-1M_OTSL
Dataset Summary
This dataset enables the evaluation of both object detection models and image-to-text methods.
PubTables-1M is introduced in the publication "PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents" by Smock et al. The conversion into HF (Hugging Face) and the addition of the OTSL (Optimized Table Structure Language) format is presented in our paper "Optimized Table Tokenization for Table… See the full description on the dataset page: https://huggingface.co/datasets/docling-project/PubTables-1M_OTSL.DocumentVQAdocvqa-single-page-questions
Dataset Card for DocVQA Dataset
Dataset Summary
DocVQA dataset is a document dataset introduced in Mathew et al. (2021) consisting of 50,000 questions defined on 12,000+ document images.
Please visit the challenge page (https://rrc.cvc.uab.es/?ch=17) and paper (https://arxiv.org/abs/2007.00398) for further information.
Usage
This dataset can be used with current releases of Hugging Face datasets library.
Here is an example using a custom collator to bundle… See the full description on the dataset page: https://huggingface.co/datasets/pixparse/docvqa-single-page-questions.MMLongBench-docdocumentation-imagesMP-DocVQA