multiconer2.py

import os
import datasets

logger = datasets.logging.get_logger(__name__)


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
SemEval 2023 Task 2: MultiCoNER II
Multilingual Complex Named Entity Recognition
"""

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

# 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 Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = ""


class Multiconer2Config(datasets.BuilderConfig):
    """BuilderConfig for Multiconer2"""

    def __init__(self, **kwargs):
        """BuilderConfig for Multiconer2.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(Multiconer2Config, self).__init__(**kwargs)


class Multiconer2(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")

    # 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 = [
        Multiconer2Config(name="bn", version=VERSION),
        Multiconer2Config(name="de", version=VERSION),
        Multiconer2Config(name="en", version=VERSION),
        Multiconer2Config(name="es", version=VERSION),
        Multiconer2Config(name="fa", version=VERSION),
        Multiconer2Config(name="fr", version=VERSION),
        Multiconer2Config(name="hi", version=VERSION),
        Multiconer2Config(name="it", version=VERSION),
        Multiconer2Config(name="pt", version=VERSION),
        Multiconer2Config(name="sv", version=VERSION),
        Multiconer2Config(name="uk", version=VERSION),
        Multiconer2Config(name="zh", version=VERSION),
    ]

    DEFAULT_CONFIG_NAME = "en"

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=['O',
                                   "B-AerospaceManufacturer", 'I-AerospaceManufacturer',
                                   'B-AnatomicalStructure', 'I-AnatomicalStructure',
                                   'B-ArtWork', 'I-ArtWork',
                                   'B-Artist', 'I-Artist',
                                   'B-Athlete', 'I-Athlete',
                                   'B-CarManufacturer', 'I-CarManufacturer',
                                   'B-Cleric', 'I-Cleric',
                                   'B-Clothing', 'I-Clothing',
                                   'B-Disease', 'I-Disease',
                                   'B-Drink', 'I-Drink',
                                   'B-Facility', 'I-Facility',
                                   'B-Food', 'I-Food',
                                   'B-HumanSettlement', 'I-HumanSettlement',
                                   'B-MedicalProcedure', 'I-MedicalProcedure',
                                   'B-Medication/Vaccine', 'I-Medication/Vaccine',
                                   'B-MusicalGRP', 'I-MusicalGRP',
                                   'B-MusicalWork', 'I-MusicalWork',
                                   'B-ORG', 'I-ORG',
                                   'B-OtherLOC', 'I-OtherLOC',
                                   'B-OtherPER', 'I-OtherPER',
                                   'B-OtherPROD', 'I-OtherPROD',
                                   'B-Politician', 'I-Politician',
                                   'B-PrivateCorp', 'I-PrivateCorp',
                                   'B-PublicCorp', 'I-PublicCorp',
                                   'B-Scientist', 'I-Scientist',
                                   'B-Software', 'I-Software',
                                   'B-SportsGRP', 'I-SportsGRP',
                                   'B-SportsManager', 'I-SportsManager',
                                   'B-Station', 'I-Station',
                                   'B-Symptom', 'I-Symptom',
                                   'B-Vehicle', 'I-Vehicle',
                                   'B-VisualWork', 'I-VisualWork',
                                   'B-WrittenWork', 'I-WrittenWork']
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage="https://www.aclweb.org/anthology/W03-0419/",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager: datasets.DownloadManager):
        """Returns SplitGenerators."""
        base_path = dl_manager.download_and_extract([
            f"{self.config.name}-train.conll",
            f"{self.config.name}-dev.conll",
        ])

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(base_path,
                                                                                                    f"{self.config.name}-train.conll")}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(base_path,
                                                                                                         f"{self.config.name}-dev.conll")}),
        ]

    def _generate_examples(self, filepath):
        logger.info("⏳ Generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            guid = 0
            tokens = []
            ner_tags = []
            for line in f:
                if line.startswith("#") or line == "" or line == "\n":
                    if tokens:
                        yield guid, {
                            "id": str(guid),
                            "tokens": tokens,
                            "ner_tags": ner_tags,
                        }
                        guid += 1
                        tokens = []
                        ner_tags = []
                else:
                    # conll2003 tokens are space separated
                    splits = line.split(" _ _ ")
                    tokens.append(splits[0])
                    ner_tags.append(splits[1].rstrip())
            # last example
            if tokens:
                yield guid, {
                    "id": str(guid),
                    "tokens": tokens,
                    "ner_tags": ner_tags,
                }