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import json |
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import datasets |
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import pandas as pd |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """ |
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ADD CITATION |
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""" |
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_DESCRIPTION = """ |
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professional translation into Spanish of Winograd NLI dataset as published in GLUE Benchmark. |
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The Winograd NLI dataset presents 855 sentence pairs, |
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in which the first sentence contains an ambiguity and the second one a possible interpretation of it. |
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The label indicates if the interpretation is correct (1) or not (0). |
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""" |
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_HOMEPAGE = """https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html""" |
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_URL = "./" |
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_TRAINING_FILE = "wnli-train-es.tsv" |
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_DEV_FILE = "wnli-dev-es.tsv" |
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_TEST_FILE = "wnli-test-shuffled-es.tsv" |
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class WinogradConfig(datasets.BuilderConfig): |
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""" Builder config for the Winograd-CA dataset """ |
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def __init__(self, **kwargs): |
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"""BuilderConfig for Winograd-CA. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(WinogradConfig, self).__init__(**kwargs) |
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class Winograd(datasets.GeneratorBasedBuilder): |
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""" Winograd Dataset """ |
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BUILDER_CONFIGS = [ |
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WinogradConfig( |
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name="winograd", |
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version=datasets.Version("1.0.0"), |
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description="Winograd dataset", |
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), |
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] |
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def __init__(self, **kwargs): |
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super(Winograd, self).__init__(**dict(kwargs, column_names = ['index','sentence1','sentence2','label'])) |
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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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"sentence1": datasets.Value("string"), |
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"sentence2": datasets.Value("string"), |
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"label": datasets.features.ClassLabel |
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(names= |
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[ |
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"not_entailment", |
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"entailment" |
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] |
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), |
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} |
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), |
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homepage=_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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"""This function returns the examples in the raw (text) form.""" |
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logger.info("generating examples from = %s", filepath) |
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df = pd.read_csv(filepath, sep='\t') |
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header = df.keys() |
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process_label = {0: "not_entailment", 1: "entailment"} |
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if "label" in header: |
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for id_, (ref, sentence1, sentence2, score) in df.iterrows(): |
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yield id_, { |
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"sentence1": sentence1, |
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"sentence2": sentence2, |
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"label": process_label[score], |
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} |
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else: |
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for id_, (ref, sentence1, sentence2) in df.iterrows(): |
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yield id_, { |
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"sentence1": sentence1, |
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"sentence2": sentence2, |
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"label": -1, |
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} |
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