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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+{"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

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e5fa89a4832fc9bb19f71085e8ff9c623c707995797782a5623c96172a60b8f1
+size 2136

xsum.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
+"""XSum dataset."""
+
+from __future__ import absolute_import, division, print_function
+
+import os
+
+import datasets
+
+
+_CITATION = """
+@article{Narayan2018DontGM,
+  title={Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization},
+  author={Shashi Narayan and Shay B. Cohen and Mirella Lapata},
+  journal={ArXiv},
+  year={2018},
+  volume={abs/1808.08745}
+}
+"""
+
+_DESCRIPTION = """
+Extreme Summarization (XSum) Dataset.
+
+There are two features:
+  - document: Input news article.
+  - summary: One sentence summary of the article.
+
+"""
+
+
+_URL = "https://s3.amazonaws.com/datasets.huggingface.co/summarization/xsum.tar.gz"
+
+_DOCUMENT = "document"
+_SUMMARY = "summary"
+
+
+class Xsum(datasets.GeneratorBasedBuilder):
+    """Extreme Summarization (XSum) Dataset."""
+
+    # Version 1.1.0 removes web contents.
+    VERSION = datasets.Version("1.1.0")
+    SUPPORTED_VERSIONS = [datasets.Version("1.0.0", "Dataset without cleaning.")]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    _DOCUMENT: datasets.Value("string"),
+                    _SUMMARY: datasets.Value("string"),
+                }
+            ),
+            supervised_keys=(_DOCUMENT, _SUMMARY),
+            homepage="https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        dl_path = dl_manager.download_and_extract(_URL)
+
+        dl_path = os.path.join(dl_path, "xsum")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "source": os.path.join(dl_path, "train.source"),
+                    "target": os.path.join(dl_path, "train.target"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "source": os.path.join(dl_path, "val.source"),
+                    "target": os.path.join(dl_path, "val.target"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "source": os.path.join(dl_path, "test.source"),
+                    "target": os.path.join(dl_path, "test.target"),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, source, target):
+        """Yields examples."""
+        with open(source, encoding="utf-8") as f1:
+            source = f1.readlines()
+        with open(target, encoding="utf-8") as f2:
+            target = f2.readlines()
+        assert len(source) == len(target)
+        for i in range(len(target)):
+            yield i, {_DOCUMENT: source[i], _SUMMARY: target[i]}