Delete loading script

xnli.py

@@ -1,211 +0,0 @@
-# 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"],
-                                }