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