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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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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow 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": "\nThe Opinosis Opinion Dataset consists of sentences extracted from reviews for 51 topics.\nTopics and opinions are obtained from Tripadvisor, Edmunds.com and Amazon.com.\n", "citation": "\n@inproceedings{ganesan2010opinosis,\n title={Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions},\n author={Ganesan, Kavita and Zhai, ChengXiang and Han, Jiawei},\n booktitle={Proceedings of the 23rd International Conference on Computational Linguistics},\n pages={340--348},\n year={2010},\n organization={Association for Computational Linguistics}\n}\n", "homepage": "http://kavita-ganesan.com/opinosis/", "license": "", "features": {"review_sents": {"dtype": "string", "id": null, "_type": "Value"}, "summaries": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "supervised_keys": {"input": "review_sents", "output": "summaries"}, "builder_name": "opinosis", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 741270, "num_examples": 51, "dataset_name": "opinosis"}}, "download_checksums": {"https://github.com/kavgan/opinosis-summarization/raw/master/OpinosisDataset1.0_0.zip": {"num_bytes": 757398, "checksum": "ed0be6c80fe32e9071ddd7fbb7a272fea7efa39b6b3fa77aa1460c7b81026547"}}, "download_size": 757398, "dataset_size": 741270, "size_in_bytes": 1498668}}
dummy/1.0.0/dummy_data.zip ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:0c6ad4c902043b00ae5e94b7601c6179cff358c8c1cb94677bceae466ba19db4
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+ size 2942
opinosis.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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+ """Opinosis Opinion 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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+ @inproceedings{ganesan2010opinosis,
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+ title={Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions},
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+ author={Ganesan, Kavita and Zhai, ChengXiang and Han, Jiawei},
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+ booktitle={Proceedings of the 23rd International Conference on Computational Linguistics},
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+ pages={340--348},
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+ year={2010},
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+ organization={Association for Computational Linguistics}
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+ }
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+ """
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+
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+ _DESCRIPTION = """
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+ The Opinosis Opinion Dataset consists of sentences extracted from reviews for 51 topics.
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+ Topics and opinions are obtained from Tripadvisor, Edmunds.com and Amazon.com.
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+ """
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+
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+ _URL = "https://github.com/kavgan/opinosis-summarization/raw/master/OpinosisDataset1.0_0.zip"
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+
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+ _REVIEW_SENTS = "review_sents"
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+ _SUMMARIES = "summaries"
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+
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+
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+ class Opinosis(datasets.GeneratorBasedBuilder):
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+ """Opinosis Opinion Dataset."""
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+
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+ VERSION = datasets.Version("1.0.0")
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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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+ _REVIEW_SENTS: datasets.Value("string"),
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+ _SUMMARIES: datasets.features.Sequence(datasets.Value("string")),
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+ }
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+ ),
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+ supervised_keys=(_REVIEW_SENTS, _SUMMARIES),
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+ homepage="http://kavita-ganesan.com/opinosis/",
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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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+ extract_path = dl_manager.download_and_extract(_URL)
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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={"path": extract_path},
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+ ),
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+ ]
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+
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+ def _generate_examples(self, path=None):
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+ """Yields examples."""
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+ topics_path = os.path.join(path, "topics")
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+ filenames = sorted(os.listdir(topics_path))
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+ for filename in filenames:
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+ file_path = os.path.join(topics_path, filename)
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+ topic_name = filename.split(".txt")[0]
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+ with open(file_path, "rb") as src_f:
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+ input_data = src_f.read().decode("latin-1")
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+ summaries_path = os.path.join(path, "summaries-gold", topic_name)
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+ summary_lst = []
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+ for summ_filename in sorted(os.listdir(summaries_path)):
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+ file_path = os.path.join(summaries_path, summ_filename)
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+ with open(file_path, "rb") as tgt_f:
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+ data = tgt_f.read().strip().decode("latin-1")
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+ summary_lst.append(data)
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+ summary_data = summary_lst
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+ yield filename, {_REVIEW_SENTS: input_data, _SUMMARIES: summary_data}