annotated_reference_strings.py

"""\
Annotated Reference Strings dataset synthesized using CSL processor on citations obtained from CrossRef, JSTOR and
PubMed
"""

import gzip
import json

import datasets


_CITATION = """\
@techreport{kee2021,
    author = {Yuan Chuan Kee},
    title = {Synthesis of a large dataset of annotated reference strings for developing citation parsers},
    institution = {National University of Singapore},
    year = {2021}
}
"""

_DESCRIPTION = """\
A repository of reference strings annotated using CSL processor using citations obtained from various sources.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://www.github.com/kylase"

_LICENSE = "cc-by-4.0"

_BASE_URL = "https://huggingface.co/datasets/yuanchuan/annotated_reference_strings/resolve/main"
_URL_FORMAT = "{base_url}/{lang}/{source}-part-{part:05}.jsonl.gz"

_SOURCES_PARTS = {
    "crossref": 16,
    "pubmed": 32,
    "jstor": 1
}

_URLs = {
    "default": [
        _URL_FORMAT.format(base_url=_BASE_URL, lang="en", source=source, part=i)
        for source, total_parts in _SOURCES_PARTS.items()
        for i in range(1, total_parts + 1)
    ]
}


class AnnotatedReferenceStringsDataset(datasets.GeneratorBasedBuilder):
    """Annotated Reference Strings dataset"""

    VERSION = datasets.Version("0.2.1")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="default", version=VERSION,
                               description="This dataset is the raw representation without tokenization."),
    ]

    DEFAULT_CONFIG_NAME = "default"

    def _info(self):
        features = datasets.Features(
            {
                "source": datasets.Value("string"),
                "lang": datasets.Value("string"),
                "entry_type": datasets.Value("string"),
                "doi_prefix": datasets.Value("string"),
                "csl_style": datasets.Value("string"),
                "content": datasets.Value("string")
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        data_urls = _URLs[self.config.name]
        files = dl_manager.download(data_urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepaths": files,
                    "split": "train",
                },
            )
        ]

    def _generate_examples(self, filepaths, split):
        id_ = 0

        for filepath in filepaths:
            with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
                for line in f:
                    if line:
                        example = json.loads(line)
                        yield id_, example
                        id_ += 1