dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: abstract
dtype: string
- name: authors
dtype: string
- name: published_date
dtype: string
- name: link
dtype: string
- name: markdown
dtype: string
splits:
- name: train
num_bytes: 6952989384
num_examples: 138380
download_size: 3232936300
dataset_size: 6952989384
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-nc-sa-4.0
Arxiver Dataset
Arxiver consists of 138,830 arXiv papers converted to multi-markdown (.mmd) format. Our dataset includes original arXiv article IDs, titles, abstracts, authors, publication dates, URLs and corresponding markdown files published between January 2023 and October 2023.
We hope our dataset will be useful for various applications such as semantic search, domain specific language modeling, question answering and summarization.
Curation
The Arxiver dataset is created using a neural OCR - Nougat. After OCR processing, we apply custom text processing steps to refine the data. This includes extracting author information, removing reference sections, and performing additional cleaning and formatting.
Using Arxiver
You can easily download and use the arxiver dataset with Hugging Face's datasets library.
from datasets import load_dataset
# whole dataset takes 3.3GB
dataset = load_dataset("neuralwork/arxiver")
print(dataset)
Alternatively, you can stream the dataset to save disk space or to partially download the dataset:
from datasets import load_dataset
dataset = load_dataset("neuralwork/arxiver", streaming=True)
print(dataset)
print(next(iter(dataset['train'])))
References
The original articles are maintained by arXiv and copyrighted to the original authors, please refer to the arXiv license information page for details. We release our dataset with a Creative Commons Attribution-Noncommercial-ShareAlike (CC BY-NC-SA 4.0) license, if you use this dataset in your research or project, please cite it as follows:
@misc{acar_arxiver2024,
author = {Alican Acar, Alara Dirik, Muhammet Hatipoglu},
title = {ArXiver},
year = {2024},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/datasets/neuralwork/arxiver}}
}