xnli.py

# 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
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# distributed under the License is distributed on an "AS IS" BASIS,
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# 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"],
            }