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Datasets:
tner
/
tweetner7

Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
100K<n<1M
ArXiv:
Tags:
Libraries:
Datasets
Croissant
License:
Dataset card Viewer Files Community
1
tweetner7
File size: 3,111 Bytes 
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""" 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 = """[TweetNER7](TBA)"""
_NAME = "tweetner7"
_VERSION = "1.0.0"
_CITATION = """
TBA
"""

_HOME_PAGE = "https://github.com/asahi417/tner"
_URL = f'https://huggingface.co/datasets/tner/{_NAME}/raw/main/dataset'
_URLS = {
    str(datasets.Split.TEST): [f'{_URL}/2021.test.json'],
    str(datasets.Split.VALIDATION): [f'{_URL}/2020.dev.json'],
    str(datasets.Split.TRAIN): [f'{_URL}/2020.train.json'],
    f'{str(datasets.Split.TEST)}_2020': [f'{_URL}/2020.test.json'],
    f'{str(datasets.Split.TEST)}_2021': [f'{_URL}/2021.test.json'],
    f'{str(datasets.Split.VALIDATION)}_2020': [f'{_URL}/2020.dev.json'],
    f'{str(datasets.Split.VALIDATION)}_2021': [f'{_URL}/2021.dev.json'],
    f'{str(datasets.Split.TRAIN)}_2020': [f'{_URL}/2020.train.json'],
    f'{str(datasets.Split.TRAIN)}_2021': [f'{_URL}/2021.train.json'],
    f'{str(datasets.Split.VALIDATION)}_random': [f'{_URL}/random.dev.json'],
    f'{str(datasets.Split.TRAIN)}_random': [f'{_URL}/random.train.json'],
    f'extra_2020': [f'{_URL}/extra/2020.extra{i:02d}.json' for i in range(9)],
    f'extra_2021': [f'{_URL}/extra/2021.extra{i:02d}.json' for i in range(10)]
}


class TweetNER7Config(datasets.BuilderConfig):
    """BuilderConfig"""

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

        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(TweetNER7Config, self).__init__(**kwargs)


class TweetNER7(datasets.GeneratorBasedBuilder):
    """Dataset."""

    BUILDER_CONFIGS = [
        TweebankNERConfig(name=_NAME, version=datasets.Version(_VERSION), description=_DESCRIPTION),
    ]

    def _split_generators(self, dl_manager):
        downloaded_file = dl_manager.download_and_extract(_URLS)
        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")),
                    "id": datasets.Value("string"),
                    "date": datasets.Value("string")
                }
            ),
            supervised_keys=None,
            homepage=_HOME_PAGE,
            citation=_CITATION,
        )