wikiann.py

""" NER dataset compiled by T-NER library https://github.com/asahi417/tner/tree/master/tner """
import json
from itertools import chain
import datasets

logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """[WikiAnn](https://aclanthology.org/P17-1178/)"""
_NAME = "wikiann"
_VERSION = "1.1.0"
_CITATION = """
@inproceedings{pan-etal-2017-cross,
    title = "Cross-lingual Name Tagging and Linking for 282 Languages",
    author = "Pan, Xiaoman  and
      Zhang, Boliang  and
      May, Jonathan  and
      Nothman, Joel  and
      Knight, Kevin  and
      Ji, Heng",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P17-1178",
    doi = "10.18653/v1/P17-1178",
    pages = "1946--1958",
    abstract = "The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. We achieve this goal by performing a series of new KB mining methods: generating {``}silver-standard{''} annotations by transferring annotations from English to other languages through cross-lingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from cross-lingual links. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data.",
}
"""

_HOME_PAGE = "https://github.com/asahi417/tner"
_URL = f'https://huggingface.co/datasets/tner/{_NAME}/resolve/main/dataset'
_LANGUAGE = ["ace", "bg", "da", "fur", "ilo", "lij", "mzn", "qu", "su", "vi", "af", "bh", "de", "fy", "io", "lmo", "nap",
        "rm", "sv", "vls", "als", "bn", "diq", "ga", "is", "ln", "nds", "ro", "sw", "vo", "am", "bo", "dv", "gan", "it",
        "lt", "ne", "ru", "szl", "wa", "an", "br", "el", "gd", "ja", "lv", "nl", "rw", "ta", "war", "ang", "bs", "eml",
        "gl", "jbo", "map-bms", "nn", "sa", "te", "wuu", "ar", "ca", "en", "gn", "jv", "mg", "no", "sah", "tg", "xmf",
        "arc", "cbk-zam", "eo", "gu", "ka", "mhr", "nov", "scn", "th", "yi", "arz", "cdo", "es", "hak", "kk", "mi",
        "oc", "sco", "tk", "yo", "as", "ce", "et", "he", "km", "min", "or", "sd", "tl", "zea", "ast", "ceb", "eu", "hi",
        "kn", "mk", "os", "sh", "tr", "zh-classical", "ay", "ckb", "ext", "hr", "ko", "ml", "pa", "si", "tt",
        "zh-min-nan", "az", "co", "fa", "hsb", "ksh", "mn", "pdc", "simple", "ug", "zh-yue", "ba", "crh", "fi", "hu",
        "ku", "mr", "pl", "sk", "uk", "zh", "bar", "cs", "fiu-vro", "hy", "ky", "ms", "pms", "sl", "ur", "bat-smg",
        "csb", "fo", "ia", "la", "mt", "pnb", "so", "uz", "be-x-old", "cv", "fr", "id", "lb", "mwl", "ps", "sq", "vec",
        "be", "cy", "frr", "ig", "li", "my", "pt", "sr", "vep"]
_URLS = {
    l: {
      str(datasets.Split.TEST): [f'{_URL}/{l}/test.jsonl'],
      str(datasets.Split.TRAIN): [f'{_URL}/{l}/train.jsonl'],
      str(datasets.Split.VALIDATION): [f'{_URL}/{l}/dev.jsonl']
     } for l in _LANGUAGE
}


class WikiAnnConfig(datasets.BuilderConfig):
    """BuilderConfig"""

    def __init__(self, **kwargs):
        """BuilderConfig.

        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(WikiAnnConfig, self).__init__(**kwargs)


class WikiAnn(datasets.GeneratorBasedBuilder):
    """Dataset."""

    BUILDER_CONFIGS = [
        WikiAnnConfig(name=l, version=datasets.Version(_VERSION), description=f"{_DESCRIPTION} (language: {l})") for l in _LANGUAGE
    ]

    def _split_generators(self, dl_manager):
        downloaded_file = dl_manager.download_and_extract(_URLS[self.config.name])
        return [datasets.SplitGenerator(name=i, gen_kwargs={"filepaths": downloaded_file[str(i)]})
                for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]]

    def _generate_examples(self, filepaths):
        _key = 0
        for filepath in filepaths:
            logger.info(f"generating examples from = {filepath}")
            with open(filepath, encoding="utf-8") as f:
                _list = [i for i in f.read().split('\n') if len(i) > 0]
                for i in _list:
                    data = json.loads(i)
                    yield _key, data
                    _key += 1

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "tags": datasets.Sequence(datasets.Value("int32")),
                }
            ),
            supervised_keys=None,
            homepage=_HOME_PAGE,
            citation=_CITATION,
        )