Attend the Women in ML Symposium on December 7 Register now

scientific_papers

References:

arxiv

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:scientific_papers/arxiv')
  • Description:
Scientific papers datasets contains two sets of long and structured documents.
The datasets are obtained from ArXiv and PubMed OpenAccess repositories.

Both "arxiv" and "pubmed" have two features:
  - article: the body of the document, pagragraphs seperated by "/n".
  - abstract: the abstract of the document, pagragraphs seperated by "/n".
  - section_names: titles of sections, seperated by "/n".
  • License: No known license
  • Version: 1.1.1
  • Splits:
Split Examples
'test' 6440
'train' 203037
'validation' 6436
  • Features:
{
    "article": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "abstract": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "section_names": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

pubmed

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:scientific_papers/pubmed')
  • Description:
Scientific papers datasets contains two sets of long and structured documents.
The datasets are obtained from ArXiv and PubMed OpenAccess repositories.

Both "arxiv" and "pubmed" have two features:
  - article: the body of the document, pagragraphs seperated by "/n".
  - abstract: the abstract of the document, pagragraphs seperated by "/n".
  - section_names: titles of sections, seperated by "/n".
  • License: No known license
  • Version: 1.1.1
  • Splits:
Split Examples
'test' 6658
'train' 119924
'validation' 6633
  • Features:
{
    "article": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "abstract": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "section_names": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}