neuralwork/arxiver

Arxiver Dataset

Arxiver consists of 63,357 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. Please refer to our GitHub repo for details.

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 1.44GB
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}}
}