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"""XNLI: The Cross-Lingual NLI Corpus.""" |
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import collections |
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import csv |
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import os |
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from contextlib import ExitStack |
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import datasets |
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_CITATION = """\ |
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@InProceedings{conneau2018xnli, |
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author = {Conneau, Alexis |
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and Rinott, Ruty |
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and Lample, Guillaume |
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and Williams, Adina |
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and Bowman, Samuel R. |
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and Schwenk, Holger |
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and Stoyanov, Veselin}, |
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title = {XNLI: Evaluating Cross-lingual Sentence Representations}, |
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booktitle = {Proceedings of the 2018 Conference on Empirical Methods |
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in Natural Language Processing}, |
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year = {2018}, |
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publisher = {Association for Computational Linguistics}, |
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location = {Brussels, Belgium}, |
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}""" |
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_DESCRIPTION = """\ |
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XNLI is a subset of a few thousand examples from MNLI which has been translated |
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into a 14 different languages (some low-ish resource). As with MNLI, the goal is |
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to predict textual entailment (does sentence A imply/contradict/neither sentence |
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B) and is a classification task (given two sentences, predict one of three |
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labels). |
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""" |
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_TRAIN_DATA_URL = "https://dl.fbaipublicfiles.com/XNLI/XNLI-MT-1.0.zip" |
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_TESTVAL_DATA_URL = "https://dl.fbaipublicfiles.com/XNLI/XNLI-1.0.zip" |
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_LANGUAGES = ("ar", "bg", "de", "el", "en", "es", "fr", "hi", "ru", "sw", "th", "tr", "ur", "vi", "zh") |
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class XnliConfig(datasets.BuilderConfig): |
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"""BuilderConfig for XNLI.""" |
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def __init__(self, language: str, languages=None, **kwargs): |
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"""BuilderConfig for XNLI. |
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Args: |
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language: One of ar,bg,de,el,en,es,fr,hi,ru,sw,th,tr,ur,vi,zh, or all_languages |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(XnliConfig, self).__init__(**kwargs) |
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self.language = language |
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if language != "all_languages": |
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self.languages = [language] |
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else: |
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self.languages = languages if languages is not None else _LANGUAGES |
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class Xnli(datasets.GeneratorBasedBuilder): |
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"""XNLI: The Cross-Lingual NLI Corpus. Version 1.0.""" |
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VERSION = datasets.Version("1.1.0", "") |
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BUILDER_CONFIG_CLASS = XnliConfig |
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BUILDER_CONFIGS = [ |
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XnliConfig( |
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name=lang, |
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language=lang, |
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version=datasets.Version("1.1.0", ""), |
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description=f"Plain text import of XNLI for the {lang} language", |
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) |
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for lang in _LANGUAGES |
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] + [ |
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XnliConfig( |
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name="all_languages", |
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language="all_languages", |
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version=datasets.Version("1.1.0", ""), |
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description="Plain text import of XNLI for all languages", |
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) |
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] |
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def _info(self): |
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if self.config.language == "all_languages": |
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features = datasets.Features( |
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{ |
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"premise": datasets.Translation( |
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languages=_LANGUAGES, |
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), |
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"hypothesis": datasets.TranslationVariableLanguages( |
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languages=_LANGUAGES, |
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), |
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"label": datasets.ClassLabel(names=["entailment", "neutral", "contradiction"]), |
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} |
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) |
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else: |
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features = datasets.Features( |
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{ |
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"premise": datasets.Value("string"), |
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"hypothesis": datasets.Value("string"), |
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"label": datasets.ClassLabel(names=["entailment", "neutral", "contradiction"]), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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supervised_keys=None, |
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homepage="https://www.nyu.edu/projects/bowman/xnli/", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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dl_dirs = dl_manager.download_and_extract( |
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{ |
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"train_data": _TRAIN_DATA_URL, |
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"testval_data": _TESTVAL_DATA_URL, |
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} |
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) |
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train_dir = os.path.join(dl_dirs["train_data"], "XNLI-MT-1.0", "multinli") |
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testval_dir = os.path.join(dl_dirs["testval_data"], "XNLI-1.0") |
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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={ |
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"filepaths": [ |
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os.path.join(train_dir, f"multinli.train.{lang}.tsv") for lang in self.config.languages |
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], |
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"data_format": "XNLI-MT", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"filepaths": [os.path.join(testval_dir, "xnli.test.tsv")], "data_format": "XNLI"}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={"filepaths": [os.path.join(testval_dir, "xnli.dev.tsv")], "data_format": "XNLI"}, |
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), |
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] |
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def _generate_examples(self, data_format, filepaths): |
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"""This function returns the examples in the raw (text) form.""" |
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if self.config.language == "all_languages": |
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if data_format == "XNLI-MT": |
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with ExitStack() as stack: |
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files = [stack.enter_context(open(filepath, encoding="utf-8")) for filepath in filepaths] |
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readers = [csv.DictReader(file, delimiter="\t", quoting=csv.QUOTE_NONE) for file in files] |
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for row_idx, rows in enumerate(zip(*readers)): |
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yield row_idx, { |
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"premise": {lang: row["premise"] for lang, row in zip(self.config.languages, rows)}, |
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"hypothesis": {lang: row["hypo"] for lang, row in zip(self.config.languages, rows)}, |
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"label": rows[0]["label"].replace("contradictory", "contradiction"), |
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} |
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else: |
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rows_per_pair_id = collections.defaultdict(list) |
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for filepath in filepaths: |
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with open(filepath, encoding="utf-8") as f: |
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reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE) |
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for row in reader: |
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rows_per_pair_id[row["pairID"]].append(row) |
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for rows in rows_per_pair_id.values(): |
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premise = {row["language"]: row["sentence1"] for row in rows} |
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hypothesis = {row["language"]: row["sentence2"] for row in rows} |
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yield rows[0]["pairID"], { |
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"premise": premise, |
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"hypothesis": hypothesis, |
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"label": rows[0]["gold_label"], |
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} |
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else: |
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if data_format == "XNLI-MT": |
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for file_idx, filepath in enumerate(filepaths): |
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file = open(filepath, encoding="utf-8") |
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reader = csv.DictReader(file, delimiter="\t", quoting=csv.QUOTE_NONE) |
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for row_idx, row in enumerate(reader): |
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key = str(file_idx) + "_" + str(row_idx) |
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yield key, { |
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"premise": row["premise"], |
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"hypothesis": row["hypo"], |
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"label": row["label"].replace("contradictory", "contradiction"), |
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} |
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else: |
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for filepath in filepaths: |
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with open(filepath, encoding="utf-8") as f: |
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reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE) |
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for row in reader: |
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if row["language"] == self.config.language: |
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yield row["pairID"], { |
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"premise": row["sentence1"], |
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"hypothesis": row["sentence2"], |
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"label": row["gold_label"], |
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} |
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