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": "\nThis corpus contains preprocessed posts from the Reddit dataset.\nThe dataset consists of 3,848,330 posts with an average length of 270 words for content,\nand 28 words for the summary.\n\nFeatures includes strings: author, body, normalizedBody, content, summary, subreddit, subreddit_id.\nContent is used as document and summary is used as summary.\n", "citation": "\n@inproceedings{volske-etal-2017-tl,\n    title = \"{TL};{DR}: Mining {R}eddit to Learn Automatic Summarization\",\n    author = {V{\"o}lske, Michael  and\n      Potthast, Martin  and\n      Syed, Shahbaz  and\n      Stein, Benno},\n    booktitle = \"Proceedings of the Workshop on New Frontiers in Summarization\",\n    month = sep,\n    year = \"2017\",\n    address = \"Copenhagen, Denmark\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W17-4508\",\n    doi = \"10.18653/v1/W17-4508\",\n    pages = \"59--63\",\n    abstract = \"Recent advances in automatic text summarization have used deep neural networks to generate high-quality abstractive summaries, but the performance of these models strongly depends on large amounts of suitable training data. We propose a new method for mining social media for author-provided summaries, taking advantage of the common practice of appending a {``}TL;DR{''} to long posts. A case study using a large Reddit crawl yields the Webis-TLDR-17 dataset, complementing existing corpora primarily from the news genre. Our technique is likely applicable to other social media sites and general web crawls.\",\n}\n", "homepage": "https://github.com/webis-de/webis-tldr-17-corpus", "license": "", "features": {"author": {"dtype": "string", "id": null, "_type": "Value"}, "body": {"dtype": "string", "id": null, "_type": "Value"}, "normalizedBody": {"dtype": "string", "id": null, "_type": "Value"}, "subreddit": {"dtype": "string", "id": null, "_type": "Value"}, "subreddit_id": {"dtype": "string", "id": null, "_type": "Value"}, "id": {"dtype": "string", "id": null, "_type": "Value"}, "content": {"dtype": "string", "id": null, "_type": "Value"}, "summary": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "reddit", "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": 18940542951, "num_examples": 3848330, "dataset_name": "reddit"}}, "download_checksums": {"https://zenodo.org/record/1043504/files/corpus-webis-tldr-17.zip?download=1": {"num_bytes": 3141854161, "checksum": "c1a0f8c4374c7314d3c9ec50dd505303c536062d87037d4dca7035b89b36938a"}}, "download_size": 3141854161, "dataset_size": 18940542951, "size_in_bytes": 22082397112}}

dummy/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:faf476f84f45e64caf7bf338d437c6248cdecfffca8fc5bfc62f65dfeb847f68
+size 520

reddit.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
+"""Reddit dataset using tldr as summaries."""
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """
+@inproceedings{volske-etal-2017-tl,
+    title = {TL;DR: Mining {R}eddit to Learn Automatic Summarization},
+    author = {V{\"o}lske, Michael  and Potthast, Martin  and Syed, Shahbaz  and Stein, Benno},
+    booktitle = {Proceedings of the Workshop on New Frontiers in Summarization},
+    month = {sep},
+    year = {2017},
+    address = {Copenhagen, Denmark},
+    publisher = {Association for Computational Linguistics},
+    url = {https://www.aclweb.org/anthology/W17-4508},
+    doi = {10.18653/v1/W17-4508},
+    pages = {59--63},
+    abstract = {Recent advances in automatic text summarization have used deep neural networks to generate high-quality abstractive summaries, but the performance of these models strongly depends on large amounts of suitable training data. We propose a new method for mining social media for author-provided summaries, taking advantage of the common practice of appending a {``}TL;DR{''} to long posts. A case study using a large Reddit crawl yields the Webis-TLDR-17 dataset, complementing existing corpora primarily from the news genre. Our technique is likely applicable to other social media sites and general web crawls.},
+}
+"""
+
+_DESCRIPTION = """
+This corpus contains preprocessed posts from the Reddit dataset.
+The dataset consists of 3,848,330 posts with an average length of 270 words for content,
+and 28 words for the summary.
+
+Features includes strings: author, body, normalizedBody, content, summary, subreddit, subreddit_id.
+Content is used as document and summary is used as summary.
+"""
+
+_URL = "https://zenodo.org/record/1043504/files/corpus-webis-tldr-17.zip?download=1"
+
+_DOCUMENT = "content"
+_SUMMARY = "summary"
+_ADDITIONAL_FEATURES = ["author", "body", "normalizedBody", "subreddit", "subreddit_id", "id"]
+
+
+class Reddit(datasets.GeneratorBasedBuilder):
+    """Reddit Dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {k: datasets.Value("string") for k in _ADDITIONAL_FEATURES + [_DOCUMENT, _SUMMARY]}
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/webis-de/webis-tldr-17-corpus",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        dl_path = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"path": os.path.join(dl_path, "corpus-webis-tldr-17.json")},
+            )
+        ]
+
+    def _generate_examples(self, path=None):
+        """Yields examples."""
+        with open(path, "rb") as f:
+            for i, line in enumerate(f):
+                # possible keys are:
+                #   author: string (nullable = true)
+                #   body: string (nullable = true)
+                #   normalizedBody: string (nullable = true)
+                #   content: string (nullable = true)
+                #   content_len: long (nullable = true)
+                #   summary: string (nullable = true)
+                #   summary_len: long (nullable = true)
+                #   id: string (nullable = true)
+                #   subreddit: string (nullable = true)
+                #   subreddit_id: string (nullable = true)
+                #   title: string (nullable = true)
+                d = json.loads(line)
+                if _SUMMARY in d and _DOCUMENT in d:
+                    yield i, {k: d.get(k, "") for k in _ADDITIONAL_FEATURES + [_DOCUMENT, _SUMMARY]}