# coding=utf-8 # Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Lint as: python3 """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 ] + [ XnliConfig( name="all_languages", language="all_languages", version=datasets.Version("1.1.0", ""), description="Plain text import of XNLI for all languages", ) ] def _info(self): if self.config.language == "all_languages": features = datasets.Features( { "premise": datasets.Translation( languages=_LANGUAGES, ), "hypothesis": datasets.TranslationVariableLanguages( languages=_LANGUAGES, ), "label": datasets.ClassLabel(names=["entailment", "neutral", "contradiction"]), } ) else: 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, # No default supervised_keys (as we have to pass both premise # and hypothesis as input). 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.TRAIN, gen_kwargs={ "filepaths": [ os.path.join(train_dir, f"multinli.train.{lang}.tsv") for lang in self.config.languages ], "data_format": "XNLI-MT", }, ), 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"], }