Datasets:
Commit
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Parent(s):
Update files from the datasets library (from 1.0.0)
Browse filesRelease notes: https://github.com/huggingface/datasets/releases/tag/1.0.0
- .gitattributes +27 -0
- dataset_infos.json +1 -0
- dummy/arxiv/1.1.1/dummy_data.zip +3 -0
- dummy/pubmed/1.1.1/dummy_data.zip +3 -0
- scientific_papers.py +139 -0
.gitattributes
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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dataset_infos.json
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{"arxiv": {"description": "\nScientific papers datasets contains two sets of long and structured documents.\nThe datasets are obtained from ArXiv and PubMed OpenAccess repositories.\n\nBoth \"arxiv\" and \"pubmed\" have two features:\n - article: the body of the document, pagragraphs seperated by \"/n\".\n - abstract: the abstract of the document, pagragraphs seperated by \"/n\".\n - section_names: titles of sections, seperated by \"/n\".\n\n", "citation": "\n@article{Cohan_2018,\n title={A Discourse-Aware Attention Model for Abstractive Summarization of\n Long Documents},\n url={http://dx.doi.org/10.18653/v1/n18-2097},\n DOI={10.18653/v1/n18-2097},\n journal={Proceedings of the 2018 Conference of the North American Chapter of\n the Association for Computational Linguistics: Human Language\n Technologies, Volume 2 (Short Papers)},\n publisher={Association for Computational Linguistics},\n author={Cohan, Arman and Dernoncourt, Franck and Kim, Doo Soon and Bui, Trung and Kim, Seokhwan and Chang, Walter and Goharian, Nazli},\n year={2018}\n}\n", "homepage": "https://github.com/armancohan/long-summarization", "license": "", "features": {"article": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}, "section_names": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "scientific_papers", "config_name": "arxiv", "version": {"version_str": "1.1.1", "description": null, "datasets_version_to_prepare": null, "major": 1, "minor": 1, "patch": 1}, "splits": {"test": {"name": "test", "num_bytes": 217518181, "num_examples": 6440, "dataset_name": "scientific_papers"}, "train": {"name": "train", "num_bytes": 7148443320, "num_examples": 203037, "dataset_name": "scientific_papers"}, "validation": {"name": "validation", "num_bytes": 217128744, "num_examples": 6436, "dataset_name": "scientific_papers"}}, "download_checksums": {"https://drive.google.com/uc?id=1b3rmCSIoh6VhD4HKWjI4HOW-cSwcwbeC&export=download": {"num_bytes": 3624420843, "checksum": "82ed30dd7c66a6497eeb3d7c3090c274e9e32c012438f8e0bb3cce3e6c1fcada"}, "https://drive.google.com/uc?id=1lvsqvsFi3W-pE1SqNZI0s8NR9rC1tsja&export=download": {"num_bytes": 880225504, "checksum": "d424074726a5e29e20bf834055fe7efe90f8a37bce0a2b512e4ab7e487013c04"}}, "download_size": 4504646347, "dataset_size": 7583090245, "size_in_bytes": 12087736592}, "pubmed": {"description": "\nScientific papers datasets contains two sets of long and structured documents.\nThe datasets are obtained from ArXiv and PubMed OpenAccess repositories.\n\nBoth \"arxiv\" and \"pubmed\" have two features:\n - article: the body of the document, pagragraphs seperated by \"/n\".\n - abstract: the abstract of the document, pagragraphs seperated by \"/n\".\n - section_names: titles of sections, seperated by \"/n\".\n\n", "citation": "\n@article{Cohan_2018,\n title={A Discourse-Aware Attention Model for Abstractive Summarization of\n Long Documents},\n url={http://dx.doi.org/10.18653/v1/n18-2097},\n DOI={10.18653/v1/n18-2097},\n journal={Proceedings of the 2018 Conference of the North American Chapter of\n the Association for Computational Linguistics: Human Language\n Technologies, Volume 2 (Short Papers)},\n publisher={Association for Computational Linguistics},\n author={Cohan, Arman and Dernoncourt, Franck and Kim, Doo Soon and Bui, Trung and Kim, Seokhwan and Chang, Walter and Goharian, Nazli},\n year={2018}\n}\n", "homepage": "https://github.com/armancohan/long-summarization", "license": "", "features": {"article": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}, "section_names": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "scientific_papers", "config_name": "pubmed", "version": {"version_str": "1.1.1", "description": null, "datasets_version_to_prepare": null, "major": 1, "minor": 1, "patch": 1}, "splits": {"test": {"name": "test", "num_bytes": 127187780, "num_examples": 6658, "dataset_name": "scientific_papers"}, "train": {"name": "train", "num_bytes": 2252087227, "num_examples": 119924, "dataset_name": "scientific_papers"}, "validation": {"name": "validation", "num_bytes": 127406718, "num_examples": 6633, "dataset_name": "scientific_papers"}}, "download_checksums": {"https://drive.google.com/uc?id=1b3rmCSIoh6VhD4HKWjI4HOW-cSwcwbeC&export=download": {"num_bytes": 3624420843, "checksum": "82ed30dd7c66a6497eeb3d7c3090c274e9e32c012438f8e0bb3cce3e6c1fcada"}, "https://drive.google.com/uc?id=1lvsqvsFi3W-pE1SqNZI0s8NR9rC1tsja&export=download": {"num_bytes": 880225504, "checksum": "d424074726a5e29e20bf834055fe7efe90f8a37bce0a2b512e4ab7e487013c04"}}, "download_size": 4504646347, "dataset_size": 2506681725, "size_in_bytes": 7011328072}}
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dummy/arxiv/1.1.1/dummy_data.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:bdb4ffacf8d2d0950f715aae4702c00c20c8ef2edc16dbfd99be80343804a701
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size 3497
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dummy/pubmed/1.1.1/dummy_data.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:c2c8b90389b4948b07a51da1ec147b47eb40f2227b88e64fbd47d92d1b468fd1
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size 3520
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scientific_papers.py
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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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# Lint as: python3
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"""Scientific Papers Dataset."""
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from __future__ import absolute_import, division, print_function
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import json
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import os
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import datasets
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_CITATION = """
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@article{Cohan_2018,
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title={A Discourse-Aware Attention Model for Abstractive Summarization of
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Long Documents},
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url={http://dx.doi.org/10.18653/v1/n18-2097},
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DOI={10.18653/v1/n18-2097},
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journal={Proceedings of the 2018 Conference of the North American Chapter of
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the Association for Computational Linguistics: Human Language
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Technologies, Volume 2 (Short Papers)},
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publisher={Association for Computational Linguistics},
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author={Cohan, Arman and Dernoncourt, Franck and Kim, Doo Soon and Bui, Trung and Kim, Seokhwan and Chang, Walter and Goharian, Nazli},
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year={2018}
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}
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"""
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_DESCRIPTION = """
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Scientific papers datasets contains two sets of long and structured documents.
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The datasets are obtained from ArXiv and PubMed OpenAccess repositories.
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Both "arxiv" and "pubmed" have two features:
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- article: the body of the document, pagragraphs seperated by "/n".
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- abstract: the abstract of the document, pagragraphs seperated by "/n".
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- section_names: titles of sections, seperated by "/n".
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"""
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_DOCUMENT = "article"
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_SUMMARY = "abstract"
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_URLS = {
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"arxiv": "https://drive.google.com/uc?id=1b3rmCSIoh6VhD4HKWjI4HOW-cSwcwbeC&export=download",
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"pubmed": "https://drive.google.com/uc?id=1lvsqvsFi3W-pE1SqNZI0s8NR9rC1tsja&export=download",
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}
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class ScientificPapersConfig(datasets.BuilderConfig):
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"""BuilderConfig for Scientific Papers."""
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def __init__(self, filename=None, **kwargs):
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"""BuilderConfig for Wikihow.
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Args:
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filename: filename of different configs for the dataset.
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**kwargs: keyword arguments forwarded to super.
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"""
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# 1.1.0 remove sentence breaker <S> and </S> in summary.
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super(ScientificPapersConfig, self).__init__(version=datasets.Version("1.1.1"), **kwargs)
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self.filename = filename
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class ScientificPapers(datasets.GeneratorBasedBuilder):
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"""Scientific Papers."""
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BUILDER_CONFIGS = [
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ScientificPapersConfig(name="pubmed", description="Documents from PubMed repository."),
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ScientificPapersConfig(name="arxiv", description="Documents from ArXiv repository."),
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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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_DOCUMENT: datasets.Value("string"),
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_SUMMARY: datasets.Value("string"),
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"section_names": datasets.Value("string"),
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}
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),
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supervised_keys=None,
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homepage="https://github.com/armancohan/long-summarization",
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citation=_CITATION,
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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_paths = dl_manager.download_and_extract(_URLS)
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path = os.path.join(dl_paths[self.config.name], self.config.name + "-dataset")
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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(path, "train.txt")},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={"path": os.path.join(path, "val.txt")},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={"path": os.path.join(path, "test.txt")},
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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, encoding="utf-8") as f:
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for line in f:
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# Possible keys are:
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# "article_id": str
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# "article_text": list[str] article (list of paragraphs).
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# "abstract_text": list[str], abstract (list of paragraphs).
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# "section_names": list[str], list of section names.
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# "sections": list[list[str]], list of sections (list of paragraphs)
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d = json.loads(line)
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summary = "\n".join(d["abstract_text"])
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# In original paper, <S> and </S> are not used in vocab during training
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# or during decoding.
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# https://github.com/armancohan/long-summarization/blob/master/data.py#L27
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summary = summary.replace("<S>", "").replace("</S>", "")
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yield d["article_id"], {
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_DOCUMENT: "\n".join(d["article_text"]),
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_SUMMARY: summary,
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"section_names": "\n".join(d["section_names"]),
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}
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