|
--- |
|
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](https://arxiv.org/) 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](https://facebookresearch.github.io/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](https://huggingface.co/datasets) library. |
|
```py |
|
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: |
|
```py |
|
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](https://arxiv.org/) and copyrighted to the original authors, please refer to the arXiv license information [page](https://info.arxiv.org/help/license/index.html) 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}} |
|
} |
|
``` |
