Update files from the datasets library (from 1.0.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

dataset_infos.json

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+{"plain_text": {"description": "XNLI is a subset of a few thousand examples from MNLI which has been translated\ninto a 14 different languages (some low-ish resource). As with MNLI, the goal is\nto predict textual entailment (does sentence A imply/contradict/neither sentence\nB) and is a classification task (given two sentences, predict one of three\nlabels).\n", "citation": "@InProceedings{conneau2018xnli,\n  author = \"Conneau, Alexis\n                 and Rinott, Ruty\n                 and Lample, Guillaume\n                 and Williams, Adina\n                 and Bowman, Samuel R.\n                 and Schwenk, Holger\n                 and Stoyanov, Veselin\",\n  title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",\n  booktitle = \"Proceedings of the 2018 Conference on Empirical Methods\n               in Natural Language Processing\",\n  year = \"2018\",\n  publisher = \"Association for Computational Linguistics\",\n  location = \"Brussels, Belgium\",\n}", "homepage": "https://www.nyu.edu/projects/bowman/xnli/", "license": "", "features": {"premise": {"languages": ["ar", "bg", "de", "el", "en", "es", "fr", "hi", "ru", "sw", "th", "tr", "ur", "vi", "zh"], "id": null, "_type": "Translation"}, "hypothesis": {"languages": ["ar", "bg", "de", "el", "en", "es", "fr", "hi", "ru", "sw", "th", "tr", "ur", "vi", "zh"], "num_languages": 15, "id": null, "_type": "TranslationVariableLanguages"}, "label": {"num_classes": 3, "names": ["entailment", "neutral", "contradiction"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "supervised_keys": null, "builder_name": "xnli", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 19419463, "num_examples": 5010, "dataset_name": "xnli"}, "validation": {"name": "validation", "num_bytes": 9582145, "num_examples": 2490, "dataset_name": "xnli"}}, "download_checksums": {"https://www.nyu.edu/projects/bowman/xnli/XNLI-1.0.zip": {"num_bytes": 17865352, "checksum": "4ba1d5e1afdb7161f0f23c66dc787802ccfa8a25a3ddd3b165a35e50df346ab1"}}, "download_size": 17865352, "dataset_size": 29001608, "size_in_bytes": 46866960}}

dummy/plain_text/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:087e5fb69a4751ad440cfac64e14928dd0ada6afe3d1ec2caaaaf2128b00da6b
+size 6032

xnli.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."""
+
+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"],
+            }