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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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  1. .gitattributes +27 -0
  2. dataset_infos.json +1 -0
  3. dummy/1.0.0/dummy_data.zip +3 -0
  4. reddit.py +101 -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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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.gz filter=lfs diff=lfs merge=lfs -text
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+ *.h5 filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ *.msgpack filter=lfs diff=lfs merge=lfs -text
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+ *.parquet filter=lfs diff=lfs merge=lfs -text
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+ *.pt filter=lfs diff=lfs merge=lfs -text
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+ *.pth filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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+ *.zip filter=lfs diff=lfs merge=lfs -text
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+ *.zstandard filter=lfs diff=lfs merge=lfs -text
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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
dataset_infos.json ADDED
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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 ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:faf476f84f45e64caf7bf338d437c6248cdecfffca8fc5bfc62f65dfeb847f68
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+ size 520
reddit.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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+ """Reddit dataset using tldr as summaries."""
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+
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+ import json
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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{volske-etal-2017-tl,
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+ title = {TL;DR: Mining {R}eddit to Learn Automatic Summarization},
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+ author = {V{\"o}lske, Michael and Potthast, Martin and Syed, Shahbaz and Stein, Benno},
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+ booktitle = {Proceedings of the Workshop on New Frontiers in Summarization},
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+ month = {sep},
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+ year = {2017},
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+ address = {Copenhagen, Denmark},
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+ publisher = {Association for Computational Linguistics},
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+ url = {https://www.aclweb.org/anthology/W17-4508},
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+ doi = {10.18653/v1/W17-4508},
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+ pages = {59--63},
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+ 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.},
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+ }
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+ """
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+
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+ _DESCRIPTION = """
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+ This corpus contains preprocessed posts from the Reddit dataset.
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+ The dataset consists of 3,848,330 posts with an average length of 270 words for content,
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+ and 28 words for the summary.
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+
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+ Features includes strings: author, body, normalizedBody, content, summary, subreddit, subreddit_id.
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+ Content is used as document and summary is used as summary.
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+ """
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+
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+ _URL = "https://zenodo.org/record/1043504/files/corpus-webis-tldr-17.zip?download=1"
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+
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+ _DOCUMENT = "content"
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+ _SUMMARY = "summary"
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+ _ADDITIONAL_FEATURES = ["author", "body", "normalizedBody", "subreddit", "subreddit_id", "id"]
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+
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+
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+ class Reddit(datasets.GeneratorBasedBuilder):
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+ """Reddit 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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+ {k: datasets.Value("string") for k in _ADDITIONAL_FEATURES + [_DOCUMENT, _SUMMARY]}
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+ ),
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+ supervised_keys=None,
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+ homepage="https://github.com/webis-de/webis-tldr-17-corpus",
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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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+ dl_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": os.path.join(dl_path, "corpus-webis-tldr-17.json")},
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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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+ with open(path, "rb") as f:
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+ for i, line in enumerate(f):
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+ # possible keys are:
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+ # author: string (nullable = true)
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+ # body: string (nullable = true)
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+ # normalizedBody: string (nullable = true)
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+ # content: string (nullable = true)
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+ # content_len: long (nullable = true)
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+ # summary: string (nullable = true)
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+ # summary_len: long (nullable = true)
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+ # id: string (nullable = true)
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+ # subreddit: string (nullable = true)
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+ # subreddit_id: string (nullable = true)
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+ # title: string (nullable = true)
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+ d = json.loads(line)
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+ if _SUMMARY in d and _DOCUMENT in d:
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+ yield i, {k: d.get(k, "") for k in _ADDITIONAL_FEATURES + [_DOCUMENT, _SUMMARY]}