Create dialect_nli.py

dialect_nli.py

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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://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"],
+                                }