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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+*.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
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+*.gz filter=lfs diff=lfs merge=lfs -text
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+*.joblib filter=lfs diff=lfs merge=lfs -text
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+*.model filter=lfs diff=lfs merge=lfs -text
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+*.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
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+*.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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+{"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

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0c6ad4c902043b00ae5e94b7601c6179cff358c8c1cb94677bceae466ba19db4
+size 2942

opinosis.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
+"""Opinosis Opinion Dataset."""
+
+from __future__ import absolute_import, division, print_function
+
+import os
+
+import datasets
+
+
+_CITATION = """
+@inproceedings{ganesan2010opinosis,
+  title={Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions},
+  author={Ganesan, Kavita and Zhai, ChengXiang and Han, Jiawei},
+  booktitle={Proceedings of the 23rd International Conference on Computational Linguistics},
+  pages={340--348},
+  year={2010},
+  organization={Association for Computational Linguistics}
+}
+"""
+
+_DESCRIPTION = """
+The Opinosis Opinion Dataset consists of sentences extracted from reviews for 51 topics.
+Topics and opinions are obtained from Tripadvisor, Edmunds.com and Amazon.com.
+"""
+
+_URL = "https://github.com/kavgan/opinosis-summarization/raw/master/OpinosisDataset1.0_0.zip"
+
+_REVIEW_SENTS = "review_sents"
+_SUMMARIES = "summaries"
+
+
+class Opinosis(datasets.GeneratorBasedBuilder):
+    """Opinosis Opinion Dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    _REVIEW_SENTS: datasets.Value("string"),
+                    _SUMMARIES: datasets.features.Sequence(datasets.Value("string")),
+                }
+            ),
+            supervised_keys=(_REVIEW_SENTS, _SUMMARIES),
+            homepage="http://kavita-ganesan.com/opinosis/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        extract_path = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"path": extract_path},
+            ),
+        ]
+
+    def _generate_examples(self, path=None):
+        """Yields examples."""
+        topics_path = os.path.join(path, "topics")
+        filenames = sorted(os.listdir(topics_path))
+        for filename in filenames:
+            file_path = os.path.join(topics_path, filename)
+            topic_name = filename.split(".txt")[0]
+            with open(file_path, "rb") as src_f:
+                input_data = src_f.read().decode("latin-1")
+            summaries_path = os.path.join(path, "summaries-gold", topic_name)
+            summary_lst = []
+            for summ_filename in sorted(os.listdir(summaries_path)):
+                file_path = os.path.join(summaries_path, summ_filename)
+                with open(file_path, "rb") as tgt_f:
+                    data = tgt_f.read().strip().decode("latin-1")
+                    summary_lst.append(data)
+            summary_data = summary_lst
+            yield filename, {_REVIEW_SENTS: input_data, _SUMMARIES: summary_data}