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 os

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}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

# TODO: Add link to the official dataset URLs here
# The HuggingFace dataset library don't host the datasets but only point to the original files
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_BASE_URL = "https://huggingface.co/datasets/yuanchuan/annotated_reference_strings"
_URLs = {
    "default": [f"{_BASE_URL}/resolve/main/data/jstor.jsonl.gz"]
}


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

    VERSION = datasets.Version("0.1.0")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    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,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features,
            # specify them here. They'll be used if as_supervised=True in
            # builder.as_dataset.
            supervised_keys=None,
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        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