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import logging |
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import datasets |
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_CITATION = """\ |
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@inproceedings{, |
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title = "", |
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author = "Garagiola, Nazareno", |
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year = "2022", |
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url = "" |
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} |
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""" |
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_DESCRIPTION = """Dataset used to train a NER model""" |
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_URL = "https://raw.githubusercontent.com/NazaGara/betoNER/main/data/wikiner/" |
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_TRAINING_FILE = "train.conllu" |
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class WikinerConfig(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(WikinerConfig, self).__init__(**kwargs) |
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class Wikiner(datasets.GeneratorBasedBuilder): |
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"""Wikiner dataset.""" |
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BUILDER_CONFIGS = [ |
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WikinerConfig( |
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name="wikiner", |
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version=datasets.Version("1.1.0"), |
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description="wikiner dataset", |
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), |
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] |
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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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"id": datasets.Value("string"), |
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"tokens": datasets.Sequence(datasets.Value("string")), |
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"pos_tags": datasets.Sequence( |
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datasets.features.ClassLabel( |
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names=[ |
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"ACRNM", |
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"ADJ", |
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"ADV", |
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"ALFS", |
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"ART", |
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"BACKSLASH", |
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"CARD", |
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"CC", |
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"CCAD", |
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"CCNEG", |
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"CM", |
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"CODE", |
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"COLON", |
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"CQUE", |
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"CSUBF", |
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"CSUBI", |
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"CSUBX", |
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"DM", |
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"DOTS", |
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"FS", |
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"INT", |
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"LP", |
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"NC", |
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"NEG", |
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"NMEA", |
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"NMON", |
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"NP", |
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"ORD", |
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"PAL", |
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"PDEL", |
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"PE", |
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"PERCT", |
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"PPC", |
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"PPO", |
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"PPX", |
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"PREP", |
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"QT", |
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"QU", |
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"REL", |
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"RP", |
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"SE", |
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"SEMICOLON", |
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"SLASH", |
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"SYM", |
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"UMMX", |
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"VCLIfin", |
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"VCLIger", |
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"VCLIinf", |
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"VEadj", |
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"VEfin", |
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"VEger", |
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"VEinf", |
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"VHadj", |
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"VHfin", |
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"VHger", |
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"VHinf", |
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"VLadj", |
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"VLfin", |
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"VLger", |
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"VLinf", |
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"VMadj", |
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"VMfin", |
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"VMger", |
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"VMinf", |
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"VSadj", |
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"VSfin", |
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"VSger", |
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"VSinf", |
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] |
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) |
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), |
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"ner_tags": datasets.Sequence( |
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datasets.features.ClassLabel( |
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names=[ |
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"O", |
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"B-PER", |
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"I-PER", |
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"B-ORG", |
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"I-ORG", |
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"B-LOC", |
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"I-LOC", |
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"B-MISC", |
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"I-MISC", |
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] |
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) |
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), |
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} |
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), |
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supervised_keys=None, |
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homepage=_URL, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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urls_to_download = { |
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"train": f"{_URL}{_TRAINING_FILE}", |
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} |
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downloaded_files = dl_manager.download_and_extract(urls_to_download) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"filepath": downloaded_files["train"]}, |
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), |
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] |
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def _generate_examples(self, filepath): |
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logging.info("⏳ Generating examples from = %s", filepath) |
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with open(filepath, encoding="utf-8") as f: |
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guid = 0 |
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tokens = [] |
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pos_tags = [] |
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ner_tags = [] |
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for line in f: |
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if line.startswith("-DOCSTART-") or line == "" or line == "\n": |
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if tokens: |
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yield guid, { |
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"id": str(guid), |
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"tokens": tokens, |
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"pos_tags": pos_tags, |
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"ner_tags": ner_tags, |
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} |
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guid += 1 |
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tokens = [] |
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pos_tags = [] |
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ner_tags = [] |
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else: |
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splits = line.split(" ") |
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tokens.append(splits[0]) |
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pos_tags.append(splits[1]) |
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ner_tags.append(splits[2].rstrip()) |
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|
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if tokens: |
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yield guid, { |
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"id": str(guid), |
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"tokens": tokens, |
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"pos_tags": pos_tags, |
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"ner_tags": ner_tags, |
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} |
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