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""" NER dataset compiled by T-NER library https://github.com/asahi417/tner/tree/master/tner """ |
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import json |
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from itertools import chain |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_DESCRIPTION = """[wikineural](https://aclanthology.org/2021.findings-emnlp.215/)""" |
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_NAME = "wikineural" |
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_VERSION = "1.0.0" |
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_CITATION = """ |
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@inproceedings{tedeschi-etal-2021-wikineural-combined, |
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title = "{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}", |
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author = "Tedeschi, Simone and |
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Maiorca, Valentino and |
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Campolungo, Niccol{\`o} and |
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Cecconi, Francesco and |
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Navigli, Roberto", |
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booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", |
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month = nov, |
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year = "2021", |
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address = "Punta Cana, Dominican Republic", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2021.findings-emnlp.215", |
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doi = "10.18653/v1/2021.findings-emnlp.215", |
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pages = "2521--2533", |
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abstract = "Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.", |
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} |
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""" |
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_HOME_PAGE = "https://github.com/asahi417/tner" |
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_URL = f'https://huggingface.co/datasets/tner/{_NAME}/resolve/main/dataset' |
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_LANGUAGE = ['de', 'en', 'es', 'fr', 'it', 'nl', 'pl', 'pt', 'ru'] |
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_URLS = { |
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l: { |
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str(datasets.Split.TEST): [f'{_URL}/{l}/test.jsonl'], |
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str(datasets.Split.TRAIN): [f'{_URL}/{l}/train.jsonl'], |
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str(datasets.Split.VALIDATION): [f'{_URL}/{l}/dev.jsonl'] |
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} for l in _LANGUAGE |
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} |
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class WikiNeuralConfig(datasets.BuilderConfig): |
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"""BuilderConfig""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(WikiNeuralConfig, self).__init__(**kwargs) |
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class WikiNeural(datasets.GeneratorBasedBuilder): |
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"""Dataset.""" |
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BUILDER_CONFIGS = [ |
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WikiNeuralConfig(name=l, version=datasets.Version(_VERSION), description=f"{_DESCRIPTION} (language: {l})") for l in _LANGUAGE |
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] |
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def _split_generators(self, dl_manager): |
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downloaded_file = dl_manager.download_and_extract(_URLS[self.config.name]) |
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return [datasets.SplitGenerator(name=i, gen_kwargs={"filepaths": downloaded_file[str(i)]}) |
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for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]] |
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def _generate_examples(self, filepaths): |
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_key = 0 |
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for filepath in filepaths: |
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logger.info(f"generating examples from = {filepath}") |
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with open(filepath, encoding="utf-8") as f: |
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_list = [i for i in f.read().split('\n') if len(i) > 0] |
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for i in _list: |
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data = json.loads(i) |
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yield _key, data |
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_key += 1 |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"tokens": datasets.Sequence(datasets.Value("string")), |
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"tags": datasets.Sequence(datasets.Value("int32")), |
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} |
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), |
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supervised_keys=None, |
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homepage=_HOME_PAGE, |
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citation=_CITATION, |
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) |
