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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"],
}
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