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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+{"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}}

dummy/arxiv/1.1.1/dummy_data.zip

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dummy/pubmed/1.1.1/dummy_data.zip

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scientific_papers.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
+"""Scientific Papers Dataset."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """
+@article{Cohan_2018,
+   title={A Discourse-Aware Attention Model for Abstractive Summarization of
+            Long Documents},
+   url={http://dx.doi.org/10.18653/v1/n18-2097},
+   DOI={10.18653/v1/n18-2097},
+   journal={Proceedings of the 2018 Conference of the North American Chapter of
+          the Association for Computational Linguistics: Human Language
+          Technologies, Volume 2 (Short Papers)},
+   publisher={Association for Computational Linguistics},
+   author={Cohan, Arman and Dernoncourt, Franck and Kim, Doo Soon and Bui, Trung and Kim, Seokhwan and Chang, Walter and Goharian, Nazli},
+   year={2018}
+}
+"""
+
+_DESCRIPTION = """
+Scientific papers datasets contains two sets of long and structured documents.
+The datasets are obtained from ArXiv and PubMed OpenAccess repositories.
+
+Both "arxiv" and "pubmed" have two features:
+  - article: the body of the document, pagragraphs seperated by "/n".
+  - abstract: the abstract of the document, pagragraphs seperated by "/n".
+  - section_names: titles of sections, seperated by "/n".
+
+"""
+
+_DOCUMENT = "article"
+_SUMMARY = "abstract"
+
+_URLS = {
+    "arxiv": "https://drive.google.com/uc?id=1b3rmCSIoh6VhD4HKWjI4HOW-cSwcwbeC&export=download",
+    "pubmed": "https://drive.google.com/uc?id=1lvsqvsFi3W-pE1SqNZI0s8NR9rC1tsja&export=download",
+}
+
+
+class ScientificPapersConfig(datasets.BuilderConfig):
+    """BuilderConfig for Scientific Papers."""
+
+    def __init__(self, filename=None, **kwargs):
+        """BuilderConfig for Wikihow.
+
+        Args:
+          filename: filename of different configs for the dataset.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        # 1.1.0 remove sentence breaker <S> and </S> in summary.
+        super(ScientificPapersConfig, self).__init__(version=datasets.Version("1.1.1"), **kwargs)
+        self.filename = filename
+
+
+class ScientificPapers(datasets.GeneratorBasedBuilder):
+    """Scientific Papers."""
+
+    BUILDER_CONFIGS = [
+        ScientificPapersConfig(name="pubmed", description="Documents from PubMed repository."),
+        ScientificPapersConfig(name="arxiv", description="Documents from ArXiv repository."),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    _DOCUMENT: datasets.Value("string"),
+                    _SUMMARY: datasets.Value("string"),
+                    "section_names": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/armancohan/long-summarization",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        dl_paths = dl_manager.download_and_extract(_URLS)
+        path = os.path.join(dl_paths[self.config.name], self.config.name + "-dataset")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"path": os.path.join(path, "train.txt")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"path": os.path.join(path, "val.txt")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"path": os.path.join(path, "test.txt")},
+            ),
+        ]
+
+    def _generate_examples(self, path=None):
+        """Yields examples."""
+        with open(path, encoding="utf-8") as f:
+            for line in f:
+                # Possible keys are:
+                # "article_id": str
+                # "article_text": list[str] article (list of paragraphs).
+                # "abstract_text": list[str], abstract (list of paragraphs).
+                # "section_names": list[str], list of section names.
+                # "sections": list[list[str]], list of sections (list of paragraphs)
+                d = json.loads(line)
+                summary = "\n".join(d["abstract_text"])
+                # In original paper, <S> and </S> are not used in vocab during training
+                # or during decoding.
+                # https://github.com/armancohan/long-summarization/blob/master/data.py#L27
+                summary = summary.replace("<S>", "").replace("</S>", "")
+                yield d["article_id"], {
+                    _DOCUMENT: "\n".join(d["article_text"]),
+                    _SUMMARY: summary,
+                    "section_names": "\n".join(d["section_names"]),
+                }