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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """\ |
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""" |
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_DESCRIPTION = """\ |
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""" |
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_URL = "https://huggingface.co/datasets/mrojas/medication/resolve/main/data/" |
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_TRAINING_FILE = "Medication_train.conll" |
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_DEV_FILE = "Medication_dev.conll" |
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_TEST_FILE = "Medication_test.conll" |
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class MedicationConfig(datasets.BuilderConfig): |
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"""BuilderConfig for Medication""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for Medication. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(MedicationConfig, self).__init__(**kwargs) |
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class Medication(datasets.GeneratorBasedBuilder): |
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"""Medication dataset.""" |
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BUILDER_CONFIGS = [ |
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MedicationConfig(name="medication", version=datasets.Version("1.0.0"), description="Medication dataset"), |
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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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"tokens": datasets.Sequence(datasets.Value("string")), |
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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-Medication", |
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"I-Medication", |
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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="", |
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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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"dev": f"{_URL}{_DEV_FILE}", |
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"test": f"{_URL}{_TEST_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(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), |
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] |
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def _generate_examples(self, filepath): |
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logger.info("⏳ Generating examples from = %s", filepath) |
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with open(filepath, encoding="utf-8") as f: |
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id_ = 0 |
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tokens = [] |
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ner_tags = [] |
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for line in f: |
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if line == "" or line == "\n": |
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if tokens: |
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yield id_, { |
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"tokens": tokens, |
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"ner_tags": ner_tags, |
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} |
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id_ += 1 |
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tokens = [] |
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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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ner_tags.append(splits[1].rstrip()) |
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yield id_, { |
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"tokens": tokens, |
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"ner_tags": ner_tags, |
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} |
