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"

    micro_to_macro_ner_mapping = {
        'O': "O",
        "B-AerospaceManufacturer": "B-Group",
        'I-AerospaceManufacturer': 'I-Group',
        'B-AnatomicalStructure': "B-Medical",
        'I-AnatomicalStructure': "I-Medical",
        'B-ArtWork': "B-CreativeWork",
        'I-ArtWork': "I-CreativeWork",
        'B-Artist': "B-Person",
        'I-Artist': "I-Person",
        'B-Athlete': "B-Person",
        'I-Athlete': "I-Person",
        'B-CarManufacturer': "B-Group",
        'I-CarManufacturer': "I-Group",
        'B-Cleric': "B-Person",
        'I-Cleric': "I-Person",
        'B-Clothing': "B-Product",
        'I-Clothing': "I-Product",
        'B-Disease': "B-Medical",
        'I-Disease': "I-Medical",
        'B-Drink': "B-Product",
        'I-Drink': "I-Product",
        'B-Facility': "B-Location",
        'I-Facility': "I-Location",
        'B-Food': "B-Product",
        'I-Food': "I-Product",
        'B-HumanSettlement': "B-Location",
        'I-HumanSettlement': "I-Location",
        'B-MedicalProcedure': "B-Medical",
        'I-MedicalProcedure': "I-Medical",
        'B-Medication/Vaccine': "B-Medical",
        'I-Medication/Vaccine': "I-Medical",
        'B-MusicalGRP': "B-Group",
        'I-MusicalGRP': "I-Group",
        'B-MusicalWork': "B-CreativeWork",
        'I-MusicalWork': "I-CreativeWork",
        'B-ORG': "B-Group",
        'I-ORG': "I-Group",
        'B-OtherLOC': "B-Location",
        'I-OtherLOC': "I-Location",
        'B-OtherPER': "B-Person",
        'I-OtherPER': "I-Person",
        'B-OtherPROD': "B-Product",
        'I-OtherPROD': "I-Product",
        'B-Politician': "B-Person",
        'I-Politician': "I-Person",
        'B-PrivateCorp': "B-Group",
        'I-PrivateCorp': "I-Group",
        'B-PublicCorp': "B-Group",
        'I-PublicCorp': "I-Group",
        'B-Scientist': "B-Person",
        'I-Scientist': "I-Person",
        'B-Software': "B-CreativeWork",
        'I-Software': "I-CreativeWork",
        'B-SportsGRP': "B-Group",
        'I-SportsGRP': "I-Group",
        'B-SportsManager': "B-Person",
        'I-SportsManager': "I-Person",
        'B-Station': 'B-Location',
        'I-Station': 'I-Location',
        'B-Symptom': "B-Medical",
        'I-Symptom': "I-Medical",
        'B-Vehicle': "B-Product",
        'I-Vehicle': "I-Product",
        'B-VisualWork': "B-CreativeWork",
        'I-VisualWork': "I-CreativeWork",
        'B-WrittenWork': "B-CreativeWork",
        'I-WrittenWork': "I-CreativeWork",
    }

    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']
                        )
                    ),
                    "ner_macro_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=['O',
                                   "B-Location", "I-Location",
                                   "B-CreativeWork", "I-CreativeWork",
                                   "B-Group", "I-Group",
                                   "B-Person", "I-Person",
                                   "B-Product", "I-Product",
                                   "B-Medical", "I-Medical",
                                   ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage="https://www.aclweb.org/anthology/W03-0419/",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager: datasets.DownloadManager):
        """Returns SplitGenerators."""

        downloaded_files = dl_manager.download_and_extract({
            "train": f"{self.config.name}-train.conll",
            "dev": f"{self.config.name}-dev.conll",
        })

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
        ]

    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 = []
            ner_macro_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,
                            "ner_macro_tags": ner_macro_tags,
                        }
                        guid += 1
                        tokens = []
                        ner_tags = []
                        ner_macro_tags = []
                else:
                    # conll2003 tokens are space separated
                    splits = line.split(" _ _ ")
                    tokens.append(splits[0])
                    ner_tags.append(splits[1].rstrip())
                    ner_macro_tags.append(self.micro_to_macro_ner_mapping[splits[1].rstrip()])
            # last example
            if tokens:
                yield guid, {
                    "id": str(guid),
                    "tokens": tokens,
                    "ner_tags": ner_tags,
                    "ner_macro_tags": ner_macro_tags,
                }