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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."""
from __future__ import absolute_import, division, print_function
import collections
import csv
import os
import six
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).
"""
_DATA_URL = "https://www.nyu.edu/projects/bowman/xnli/XNLI-1.0.zip"
_LANGUAGES = ("ar", "bg", "de", "el", "en", "es", "fr", "hi", "ru", "sw", "th", "tr", "ur", "vi", "zh")
class Xnli(datasets.GeneratorBasedBuilder):
"""XNLI: The Cross-Lingual NLI Corpus. Version 1.0."""
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="plain_text",
version=datasets.Version("1.0.0", ""),
description="Plain text import of XNLI",
)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"premise": datasets.features.Translation(
languages=_LANGUAGES,
),
"hypothesis": datasets.features.TranslationVariableLanguages(
languages=_LANGUAGES,
),
"label": datasets.features.ClassLabel(names=["entailment", "neutral", "contradiction"]),
}
),
# 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_dir = dl_manager.download_and_extract(_DATA_URL)
data_dir = os.path.join(dl_dir, "XNLI-1.0")
return [
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, "xnli.test.tsv")}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(data_dir, "xnli.dev.tsv")}
),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
rows_per_pair_id = collections.defaultdict(list)
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 six.itervalues(rows_per_pair_id):
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"],
}
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