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xnli / xnli.py

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	# 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"],
	}