dialect_nli.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
#
# 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://gmuedu-my.sharepoint.com/:u:/g/personal/ffaisal_gmu_edu/EVJ2LyvweSVJpUFvTMkKiKsB9P7DDr0T4ZL7EPFahruyow?download=1"

_TEST_DATA_URL = "https://gmuedu-my.sharepoint.com/:u:/g/personal/ffaisal_gmu_edu/ERNIHGKDoYZNi5mj5HIQbaMB7mWr4s1z3iVq35pbUeBjEg?download=1"

_VAL_DATA_URL = "https://gmuedu-my.sharepoint.com/:u:/g/personal/ffaisal_gmu_edu/EWqXGwiQwwpEup1xMmoRRvUBpj675UlDc9qj1EPNEUNM9w?download=1"


_LANGUAGES = ("eng_Latn",
              "lmo_Latn",
              "ita_Latn",
              "fur_Latn",
              "scn_Latn",
              "srd_Latn",
              "vec_Latn",
              "azb_Arab",
              "azj_Latn",
              "tur_Latn",
              "kmr_Latn",
              "ckb_Arab",
              "nno_Latn",
              "nob_Latn",
              "lim_Latn",
              "ltz_Latn",
              "nld_Latn",
              "lvs_Latn",
              "ltg_Latn",
              "acm_Arab",
              "acq_Arab",
              "aeb_Arab",
              "ajp_Arab",
              "apc_Arab",
              "arb_Arab",
              "ars_Arab",
              "ary_Arab",
              "arz_Arab",
              "kab_Latn",
              "asm_Beng",
              "ben_Beng",
              "lij_Latn",
              "oci_Latn",
              "yue_Hant",
              "zho_Hans",
              "zho_Hant",
              "glg_Latn",
              "spa_Latn",
              "por_Latn",
              "nso_Latn",
              "sot_Latn")


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,
                "test_data": _TEST_DATA_URL,
                "val_data": _VAL_DATA_URL,
            }
        )
        train_dir = os.path.join(dl_dirs["train_data"])
        test_dir = os.path.join(dl_dirs["test_data"])
        val_dir = os.path.join(dl_dirs["val_data"])
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepaths": [
                        os.path.join(train_dir, f"train-{lang}.tsv") for lang in self.config.languages if lang=='eng_Latn'
                    ],
                    "data_format": "XNLI-MT",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"filepaths": [os.path.join(test_dir, "test.tsv")], "data_format": "XNLI"},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"filepaths": [os.path.join(val_dir, "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"],
                                }