Create arxiv.py

arxiv.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+
+"""arXiv Dataset."""
+
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """\
+@misc{clement2019arxiv,
+    title={On the Use of ArXiv as a Dataset},
+    author={Colin B. Clement and Matthew Bierbaum and Kevin P. O'Keeffe and Alexander A. Alemi},
+    year={2019},
+    eprint={1905.00075},
+    archivePrefix={arXiv},
+    primaryClass={cs.IR}
+}
+"""
+
+_DESCRIPTION = """\
+A dataset of 1.7 million arXiv articles for applications like trend analysis, paper recommender engines, category prediction, co-citation networks, knowledge graph construction and semantic search interfaces.
+"""
+
+_HOMEPAGE = "https://www.kaggle.com/Cornell-University/arxiv"
+_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"
+
+_ID = "id"
+_SUBMITTER = "submitter"
+_AUTHORS = "authors"
+_TITLE = "title"
+_COMMENTS = "comments"
+_JOURNAL_REF = "journal-ref"
+_DOI = "doi"
+_REPORT_NO = "report-no"
+_CATEGORIES = "categories"
+_LICENSE = "license"
+_ABSTRACT = "abstract"
+_UPDATE_DATE = "update_date"
+
+_FILENAME = "arxiv-metadata-oai-snapshot.json"
+
+
+class ArxivDataset(datasets.GeneratorBasedBuilder):
+    """arXiv Dataset: arXiv dataset and metadata of 1.7M+ scholarly papers across STEM"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    @property
+    def manual_download_instructions(self):
+        return """\
+    You need to go to https://www.kaggle.com/Cornell-University/arxiv,
+    and manually download the dataset. Once it is completed,
+    a zip folder named archive.zip will be appeared in your Downloads folder
+    or whichever folder your browser chooses to save files to. Extract that folder
+    and you would get a arxiv-metadata-oai-snapshot.json file
+    You can then move that file under <path/to/folder>.
+    The <path/to/folder> can e.g. be "~/manual_data".
+    arxiv_dataset can then be loaded using the following command `datasets.load_dataset("arxiv_dataset", data_dir="<path/to/folder>")`.
+    """
+
+    def _info(self):
+        feature_names = [
+            _ID,
+            _SUBMITTER,
+            _AUTHORS,
+            _TITLE,
+            _COMMENTS,
+            _JOURNAL_REF,
+            _DOI,
+            _REPORT_NO,
+            _CATEGORIES,
+            _LICENSE,
+            _ABSTRACT,
+            _UPDATE_DATE,
+        ]
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({k: datasets.Value("string") for k in feature_names}),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        path_to_manual_file = os.path.join(os.path.abspath(os.path.expanduser(dl_manager.manual_dir)), _FILENAME)
+        if not os.path.exists(path_to_manual_file):
+            raise FileNotFoundError(
+                "{path_to_manual_file} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('arxiv_dataset', data_dir=...)` that includes a file name {_FILENAME}. Manual download instructions: {self.manual_download_instructions})"
+            )
+        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"path": path_to_manual_file})]
+
+    def _generate_examples(self, path=None, title_set=None):
+        """Yields examples."""
+        with open(path, encoding="utf8") as f:
+            for i, entry in enumerate(f):
+                data = dict(json.loads(entry))
+                yield i, {
+                    _ID: data["id"],
+                    _SUBMITTER: data["submitter"],
+                    _AUTHORS: data["authors"],
+                    _TITLE: data["title"],
+                    _COMMENTS: data["comments"],
+                    _JOURNAL_REF: data["journal-ref"],
+                    _DOI: data["doi"],
+                    _REPORT_NO: data["report-no"],
+                    _CATEGORIES: data["categories"],
+                    _LICENSE: data["license"],
+                    _ABSTRACT: data["abstract"],
+                    _UPDATE_DATE: data["update_date"],
+                }