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": "The SNLI corpus (version 1.0) is a collection of 570k human-written English\nsentence pairs manually labeled for balanced classification with the labels\nentailment, contradiction, and neutral, supporting the task of natural language\ninference (NLI), also known as recognizing textual entailment (RTE).\n", "citation": "@inproceedings{snli:emnlp2015,\n\tAuthor = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D.},\n\tBooktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)},\n\tPublisher = {Association for Computational Linguistics},\n\tTitle = {A large annotated corpus for learning natural language inference},\n\tYear = {2015}\n}\n", "homepage": "https://nlp.stanford.edu/projects/snli/", "license": "", "features": {"premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["entailment", "neutral", "contradiction"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "supervised_keys": null, "builder_name": "snli", "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": 1263912, "num_examples": 10000, "dataset_name": "snli"}, "train": {"name": "train", "num_bytes": 66159510, "num_examples": 550152, "dataset_name": "snli"}, "validation": {"name": "validation", "num_bytes": 1268044, "num_examples": 10000, "dataset_name": "snli"}}, "download_checksums": {"https://nlp.stanford.edu/projects/snli/snli_1.0.zip": {"num_bytes": 94550081, "checksum": "afb3d70a5af5d8de0d9d81e2637e0fb8c22d1235c2749d83125ca43dab0dbd3e"}}, "download_size": 94550081, "dataset_size": 68691466, "size_in_bytes": 163241547}}

dummy/plain_text/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:415936f595d8ec7c004a08095d90c41b29ffcb769d032de336e67475c0a4d744
+size 2282

snli.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
+"""The Stanford Natural Language Inference (SNLI) Corpus."""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import os
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{snli:emnlp2015,
+    Author = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D.},
+    Booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
+    Publisher = {Association for Computational Linguistics},
+    Title = {A large annotated corpus for learning natural language inference},
+    Year = {2015}
+}
+"""
+
+_DESCRIPTION = """\
+The SNLI corpus (version 1.0) is a collection of 570k human-written English
+sentence pairs manually labeled for balanced classification with the labels
+entailment, contradiction, and neutral, supporting the task of natural language
+inference (NLI), also known as recognizing textual entailment (RTE).
+"""
+
+_DATA_URL = "https://nlp.stanford.edu/projects/snli/snli_1.0.zip"
+
+
+class Snli(datasets.GeneratorBasedBuilder):
+    """The Stanford Natural Language Inference (SNLI) Corpus."""
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="plain_text",
+            version=datasets.Version("1.0.0", ""),
+            description="Plain text import of SNLI",
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "premise": datasets.Value("string"),
+                    "hypothesis": datasets.Value("string"),
+                    "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://nlp.stanford.edu/projects/snli/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        dl_dir = dl_manager.download_and_extract(_DATA_URL)
+        data_dir = os.path.join(dl_dir, "snli_1.0")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_test.txt")}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_dev.txt")}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_train.txt")}
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        with open(filepath, encoding="utf-8") as f:
+            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
+            for idx, row in enumerate(reader):
+                label = -1 if row["gold_label"] == "-" else row["gold_label"]
+                yield idx, {
+                    "premise": row["sentence1"],
+                    "hypothesis": row["sentence2"],
+                    "label": label,
+                }