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Update files from the datasets library (from 1.0.0)

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

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dataset_infos.json ADDED
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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 ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:415936f595d8ec7c004a08095d90c41b29ffcb769d032de336e67475c0a4d744
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+ size 2282
snli.py ADDED
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+ # coding=utf-8
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+ # Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+
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+ # Lint as: python3
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+ """The Stanford Natural Language Inference (SNLI) Corpus."""
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+
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+ from __future__ import absolute_import, division, print_function
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+
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+ import csv
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+ import os
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+
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+ import datasets
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+
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+
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+ _CITATION = """\
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+ @inproceedings{snli:emnlp2015,
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+ Author = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D.},
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+ Booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
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+ Publisher = {Association for Computational Linguistics},
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+ Title = {A large annotated corpus for learning natural language inference},
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+ Year = {2015}
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+ }
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+ """
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+
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+ _DESCRIPTION = """\
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+ The SNLI corpus (version 1.0) is a collection of 570k human-written English
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+ sentence pairs manually labeled for balanced classification with the labels
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+ entailment, contradiction, and neutral, supporting the task of natural language
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+ inference (NLI), also known as recognizing textual entailment (RTE).
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+ """
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+
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+ _DATA_URL = "https://nlp.stanford.edu/projects/snli/snli_1.0.zip"
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+
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+
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+ class Snli(datasets.GeneratorBasedBuilder):
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+ """The Stanford Natural Language Inference (SNLI) Corpus."""
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+
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+ BUILDER_CONFIGS = [
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+ datasets.BuilderConfig(
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+ name="plain_text",
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+ version=datasets.Version("1.0.0", ""),
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+ description="Plain text import of SNLI",
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+ )
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+ ]
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+
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+ def _info(self):
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+ return datasets.DatasetInfo(
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+ description=_DESCRIPTION,
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+ features=datasets.Features(
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+ {
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+ "premise": datasets.Value("string"),
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+ "hypothesis": datasets.Value("string"),
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+ "label": datasets.features.ClassLabel(names=["entailment", "neutral", "contradiction"]),
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+ }
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+ ),
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+ # No default supervised_keys (as we have to pass both premise
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+ # and hypothesis as input).
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+ supervised_keys=None,
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+ homepage="https://nlp.stanford.edu/projects/snli/",
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ dl_dir = dl_manager.download_and_extract(_DATA_URL)
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+ data_dir = os.path.join(dl_dir, "snli_1.0")
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+ return [
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_test.txt")}
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_dev.txt")}
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_train.txt")}
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+ ),
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+ ]
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+
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+ def _generate_examples(self, filepath):
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+ """This function returns the examples in the raw (text) form."""
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+ with open(filepath, encoding="utf-8") as f:
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+ reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
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+ for idx, row in enumerate(reader):
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+ label = -1 if row["gold_label"] == "-" else row["gold_label"]
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+ yield idx, {
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+ "premise": row["sentence1"],
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+ "hypothesis": row["sentence2"],
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+ "label": label,
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+ }