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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

Files changed (4) hide show
  1. .gitattributes +27 -0
  2. dataset_infos.json +1 -0
  3. dummy/1.1.0/dummy_data.zip +3 -0
  4. xsum.py +111 -0
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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dataset_infos.json ADDED
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+ {"default": {"description": "\nExtreme Summarization (XSum) Dataset.\n\nThere are two features:\n - document: Input news article.\n - summary: One sentence summary of the article.\n\nThis data need to manaully downloaded and extracted as described in\nhttps://github.com/EdinburghNLP/XSum/blob/master/XSum-Dataset/README.md.\nThe folder 'xsum-extracts-from-downloads' need to be compressed as\n'xsum-extracts-from-downloads.tar.gz' and put in manually downloaded folder.\n", "citation": "\n@article{Narayan2018DontGM,\n title={Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization},\n author={Shashi Narayan and Shay B. Cohen and Mirella Lapata},\n journal={ArXiv},\n year={2018},\n volume={abs/1808.08745}\n}\n", "homepage": "https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset", "license": "", "features": {"document": {"dtype": "string", "id": null, "_type": "Value"}, "summary": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": {"input": "document", "output": "summary"}, "builder_name": "xsum", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "datasets_version_to_prepare": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 474092909, "num_examples": 204017, "dataset_name": "xsum"}, "validation": {"name": "validation", "num_bytes": 26011730, "num_examples": 11327, "dataset_name": "xsum"}, "test": {"name": "test", "num_bytes": 26470484, "num_examples": 11333, "dataset_name": "xsum"}}, "download_checksums": {"https://s3.amazonaws.com/datasets.huggingface.co/summarization/xsum.tar.gz": {"num_bytes": 204844092, "checksum": "3daaea63a068ad9d9c250ca39fcfe1e985e08696984dfbc3274f6a4082a29f88"}}, "download_size": 204844092, "dataset_size": 526575123, "size_in_bytes": 731419215}}
dummy/1.1.0/dummy_data.zip ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:e5fa89a4832fc9bb19f71085e8ff9c623c707995797782a5623c96172a60b8f1
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+ size 2136
xsum.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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+ """XSum dataset."""
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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 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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+ @article{Narayan2018DontGM,
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+ title={Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization},
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+ author={Shashi Narayan and Shay B. Cohen and Mirella Lapata},
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+ journal={ArXiv},
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+ year={2018},
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+ volume={abs/1808.08745}
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+ }
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+ """
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+
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+ _DESCRIPTION = """
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+ Extreme Summarization (XSum) Dataset.
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+
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+ There are two features:
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+ - document: Input news article.
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+ - summary: One sentence summary of the article.
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+
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+ """
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+
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+
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+ _URL = "https://s3.amazonaws.com/datasets.huggingface.co/summarization/xsum.tar.gz"
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+
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+ _DOCUMENT = "document"
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+ _SUMMARY = "summary"
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+
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+
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+ class Xsum(datasets.GeneratorBasedBuilder):
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+ """Extreme Summarization (XSum) Dataset."""
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+
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+ # Version 1.1.0 removes web contents.
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+ VERSION = datasets.Version("1.1.0")
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+ SUPPORTED_VERSIONS = [datasets.Version("1.0.0", "Dataset without cleaning.")]
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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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+ _DOCUMENT: datasets.Value("string"),
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+ _SUMMARY: datasets.Value("string"),
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+ }
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+ ),
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+ supervised_keys=(_DOCUMENT, _SUMMARY),
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+ homepage="https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset",
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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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+ """Returns SplitGenerators."""
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+
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+ dl_path = dl_manager.download_and_extract(_URL)
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+
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+ dl_path = os.path.join(dl_path, "xsum")
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+ return [
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TRAIN,
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+ gen_kwargs={
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+ "source": os.path.join(dl_path, "train.source"),
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+ "target": os.path.join(dl_path, "train.target"),
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+ },
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.VALIDATION,
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+ gen_kwargs={
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+ "source": os.path.join(dl_path, "val.source"),
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+ "target": os.path.join(dl_path, "val.target"),
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+ },
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TEST,
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+ gen_kwargs={
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+ "source": os.path.join(dl_path, "test.source"),
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+ "target": os.path.join(dl_path, "test.target"),
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+ },
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+ ),
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+ ]
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+
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+ def _generate_examples(self, source, target):
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+ """Yields examples."""
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+ with open(source, encoding="utf-8") as f1:
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+ source = f1.readlines()
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+ with open(target, encoding="utf-8") as f2:
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+ target = f2.readlines()
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+ assert len(source) == len(target)
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+ for i in range(len(target)):
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+ yield i, {_DOCUMENT: source[i], _SUMMARY: target[i]}