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Update files from the datasets library (from 1.2.0)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

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README.md ADDED
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+ ---
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+ annotations_creators:
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+ - no-annotation
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+ language_creators:
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+ - found
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+ languages:
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+ - en
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+ licenses:
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+ - unknown
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+ multilinguality:
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+ - monolingual
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+ size_categories:
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+ - n>1M
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+ source_datasets:
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+ - original
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+ task_categories:
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+ - conditional-text-generation
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+ task_ids:
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+ - summarization
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+ ---
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+
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+ # Dataset Card for Big Patent
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+
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+ ## Table of Contents
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+
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+ - [Dataset Card for Big Patent](#dataset-card-for-big-patent)
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+ - [Table of Contents](#table-of-contents)
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+ - [Dataset Description](#dataset-description)
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+ - [Dataset Summary](#dataset-summary)
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+ - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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+ - [Languages](#languages)
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+ - [Dataset Structure](#dataset-structure)
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+ - [Data Instances](#data-instances)
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+ - [Data Fields](#data-fields)
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+ - [Data Splits](#data-splits)
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+ - [Dataset Creation](#dataset-creation)
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+ - [Curation Rationale](#curation-rationale)
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+ - [Source Data](#source-data)
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+ - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
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+ - [Who are the source language producers?](#who-are-the-source-language-producers)
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+ - [Annotations](#annotations)
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+ - [Annotation process](#annotation-process)
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+ - [Who are the annotators?](#who-are-the-annotators)
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+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
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+ - [Considerations for Using the Data](#considerations-for-using-the-data)
46
+ - [Social Impact of Dataset](#social-impact-of-dataset)
47
+ - [Discussion of Biases](#discussion-of-biases)
48
+ - [Other Known Limitations](#other-known-limitations)
49
+ - [Additional Information](#additional-information)
50
+ - [Dataset Curators](#dataset-curators)
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+ - [Licensing Information](#licensing-information)
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+ - [Citation Information](#citation-information)
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+
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+ ## Dataset Description
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+
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+ - **Homepage:[Big Patent](https://evasharma.github.io/bigpatent/)**
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+ - **Repository:**
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+ - **Paper:[BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization](https://arxiv.org/abs/1906.03741)**
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+ - **Leaderboard:**
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+ - **Point of Contact:**
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+
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+ ### Dataset Summary
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+
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+ BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries. Each US patent application is filed under a Cooperative Patent Classification (CPC) code. There are nine such classification categories:
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+
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+ - A (Human Necessities)
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+ - B (Performing Operations; Transporting)
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+ - C (Chemistry; Metallurgy)
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+ - D (Textiles; Paper)
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+ - E (Fixed Constructions)
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+ - F (Mechanical Engineering; Lightning; Heating; Weapons; Blasting)
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+ - G (Physics)
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+ - H (Electricity)
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+ - Y (General tagging of new or cross-sectional technology)
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+
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+ ### Supported Tasks and Leaderboards
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+
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+ [More Information Needed]
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+
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+ ### Languages
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+
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+ English
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+
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+ ## Dataset Structure
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+
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+ ### Data Instances
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+
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+ Each instance contains a pair of `description` and `abstract`. `description` is extracted from the Description section of the Patent while `abstract` is extracted from the Abstract section.
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+
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+ ### Data Fields
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+
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+ - `description`: detailed description of patent.
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+ - `abstract`: Patent abastract.
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+
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+ ### Data Splits
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+
97
+ [More Information Needed]
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+
99
+ ## Dataset Creation
100
+
101
+ ### Curation Rationale
102
+
103
+ [More Information Needed]
104
+
105
+ ### Source Data
106
+
107
+ #### Initial Data Collection and Normalization
108
+
109
+ [More Information Needed]
110
+
111
+ #### Who are the source language producers?
112
+
113
+ [More Information Needed]
114
+
115
+ ### Annotations
116
+
117
+ #### Annotation process
118
+
119
+ [More Information Needed]
120
+
121
+ #### Who are the annotators?
122
+
123
+ [More Information Needed]
124
+
125
+ ### Personal and Sensitive Information
126
+
127
+ [More Information Needed]
128
+
129
+ ## Considerations for Using the Data
130
+
131
+ ### Social Impact of Dataset
132
+
133
+ [More Information Needed]
134
+
135
+ ### Discussion of Biases
136
+
137
+ [More Information Needed]
138
+
139
+ ### Other Known Limitations
140
+
141
+ [More Information Needed]
142
+
143
+ ## Additional Information
144
+
145
+ ### Dataset Curators
146
+
147
+ [More Information Needed]
148
+
149
+ ### Licensing Information
150
+
151
+ [More Information Needed]
152
+
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+ ### Citation Information
154
+
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+ [More Information Needed]
big_patent.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
9
+ #
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+ # Unless required by applicable law or agreed to in writing, software
11
+ # 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
14
+ # limitations under the License.
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+
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+ # Lint as: python3
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+ """BigPatent Dataset."""
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+ import glob
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+ import gzip
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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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+ @misc{sharma2019bigpatent,
28
+ title={BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization},
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+ author={Eva Sharma and Chen Li and Lu Wang},
30
+ year={2019},
31
+ eprint={1906.03741},
32
+ archivePrefix={arXiv},
33
+ primaryClass={cs.CL}
34
+ }
35
+ """
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+
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+ _DESCRIPTION = """
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+ BIGPATENT, consisting of 1.3 million records of U.S. patent documents
39
+ along with human written abstractive summaries.
40
+ Each US patent application is filed under a Cooperative Patent Classification
41
+ (CPC) code. There are nine such classification categories:
42
+ A (Human Necessities), B (Performing Operations; Transporting),
43
+ C (Chemistry; Metallurgy), D (Textiles; Paper), E (Fixed Constructions),
44
+ F (Mechanical Engineering; Lightning; Heating; Weapons; Blasting),
45
+ G (Physics), H (Electricity), and
46
+ Y (General tagging of new or cross-sectional technology)
47
+ There are two features:
48
+ - description: detailed description of patent.
49
+ - abstract: Patent abastract.
50
+ """
51
+
52
+ _URL = "https://drive.google.com/uc?export=download&id=1J3mucMFTWrgAYa3LuBZoLRR3CzzYD3fa"
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+
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+ _DOCUMENT = "description"
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+ _SUMMARY = "abstract"
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+
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+ _CPC_DESCRIPTION = {
58
+ "a": "Human Necessities",
59
+ "b": "Performing Operations; Transporting",
60
+ "c": "Chemistry; Metallurgy",
61
+ "d": "Textiles; Paper",
62
+ "e": "Fixed Constructions",
63
+ "f": "Mechanical Engineering; Lightning; Heating; Weapons; Blasting",
64
+ "g": "Physics",
65
+ "h": "Electricity",
66
+ "y": "General tagging of new or cross-sectional technology",
67
+ }
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+
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+ # Available versions:
70
+ # 1.0.0 lower cased tokenized words.
71
+ # 2.0.0 cased raw strings.
72
+ # 2.1.0 cased raw strings (fixed).
73
+ # TODO Add raw string versions
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+
75
+ _VERSION = "1.0.0"
76
+
77
+
78
+ class BigPatentConfig(datasets.BuilderConfig):
79
+ """BuilderConfig for BigPatent."""
80
+
81
+ def __init__(self, *args, cpc_codes=None, **kwargs):
82
+ """BuilderConfig for BigPatent.
83
+ Args:
84
+ cpc_codes: str, cpc_codes
85
+ **kwargs: keyword arguments forwarded to super.
86
+ """
87
+ super().__init__(*args, version=_VERSION, **kwargs)
88
+ self.cpc_codes = cpc_codes
89
+
90
+
91
+ class BigPatent(datasets.GeneratorBasedBuilder):
92
+ """BigPatent datasets."""
93
+
94
+ BUILDER_CONFIGS = [
95
+ BigPatentConfig(
96
+ cpc_codes=list(_CPC_DESCRIPTION),
97
+ name="all",
98
+ description="Patents under all categories.",
99
+ ),
100
+ ] + [
101
+ BigPatentConfig( # pylint:disable=g-complex-comprehension
102
+ cpc_codes=[k],
103
+ name=k,
104
+ description=("Patents under Cooperative Patent Classification (CPC)" "{0}: {1}".format(k, v)),
105
+ )
106
+ for k, v in sorted(_CPC_DESCRIPTION.items())
107
+ ]
108
+ DEFAULT_CONFIG_NAME = "all"
109
+ VERSION = _VERSION
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+
111
+ def _info(self):
112
+ return datasets.DatasetInfo(
113
+ description=_DESCRIPTION,
114
+ features=datasets.Features({_DOCUMENT: datasets.Value("string"), _SUMMARY: datasets.Value("string")}),
115
+ supervised_keys=(_DOCUMENT, _SUMMARY),
116
+ homepage="https://evasharma.github.io/bigpatent/",
117
+ citation=_CITATION,
118
+ )
119
+
120
+ def _split_generators(self, dl_manager):
121
+ """Returns SplitGenerators."""
122
+ dl_path = dl_manager.download_and_extract(_URL)
123
+ split_types = ["train", "val", "test"]
124
+ extract_paths = dl_manager.extract(
125
+ {k: os.path.join(dl_path, "bigPatentData", k + ".tar.gz") for k in split_types}
126
+ )
127
+ extract_paths = {k: os.path.join(extract_paths[k], k) for k in split_types}
128
+
129
+ return [
130
+ datasets.SplitGenerator(
131
+ name=datasets.Split.TRAIN,
132
+ gen_kwargs={"path": extract_paths["train"]},
133
+ ),
134
+ datasets.SplitGenerator(
135
+ name=datasets.Split.VALIDATION,
136
+ gen_kwargs={"path": extract_paths["val"]},
137
+ ),
138
+ datasets.SplitGenerator(
139
+ name=datasets.Split.TEST,
140
+ gen_kwargs={"path": extract_paths["test"]},
141
+ ),
142
+ ]
143
+
144
+ def _generate_examples(self, path=None):
145
+ """Yields examples."""
146
+ for cpc_code in self.config.cpc_codes:
147
+ filenames = glob.glob(os.path.join(path, cpc_code, "*"))
148
+ for filename in sorted(filenames):
149
+ with open(filename, "rb") as fin:
150
+ fin = gzip.GzipFile(fileobj=fin)
151
+ for row in fin:
152
+ json_obj = json.loads(row)
153
+ yield json_obj["publication_number"], {
154
+ _DOCUMENT: json_obj[_DOCUMENT],
155
+ _SUMMARY: json_obj[_SUMMARY],
156
+ }
dataset_infos.json ADDED
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+ {"all": {"description": "\nBIGPATENT, consisting of 1.3 million records of U.S. patent documents\nalong with human written abstractive summaries.\nEach US patent application is filed under a Cooperative Patent Classification\n(CPC) code. There are nine such classification categories:\nA (Human Necessities), B (Performing Operations; Transporting),\nC (Chemistry; Metallurgy), D (Textiles; Paper), E (Fixed Constructions),\nF (Mechanical Engineering; Lightning; Heating; Weapons; Blasting),\nG (Physics), H (Electricity), and\nY (General tagging of new or cross-sectional technology)\nThere are two features:\n - description: detailed description of patent.\n - summary: Patent abastract.\n", "citation": "\n@misc{sharma2019bigpatent,\n title={BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization},\n author={Eva Sharma and Chen Li and Lu Wang},\n year={2019},\n eprint={1906.03741},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n", "homepage": "https://evasharma.github.io/bigpatent/", "license": "", "features": {"description": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "description", "output": "abstract"}, "builder_name": "big_patent", "config_name": "all", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 23363518650, "num_examples": 1207222, "dataset_name": "big_patent"}, "validation": {"name": "validation", "num_bytes": 1290154487, "num_examples": 67068, "dataset_name": "big_patent"}, "test": {"name": "test", "num_bytes": 1296234391, "num_examples": 67072, "dataset_name": "big_patent"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1J3mucMFTWrgAYa3LuBZoLRR3CzzYD3fa": {"num_bytes": 6448045871, "checksum": "7e1093c7e0d09677c79bd872a07b6a6dd2b3235633207e9918b75056205f04dc"}}, "download_size": 6448045871, "post_processing_size": null, "dataset_size": 25949907528, "size_in_bytes": 32397953399}, "a": {"description": "\nBIGPATENT, consisting of 1.3 million records of U.S. patent documents\nalong with human written abstractive summaries.\nEach US patent application is filed under a Cooperative Patent Classification\n(CPC) code. There are nine such classification categories:\nA (Human Necessities), B (Performing Operations; Transporting),\nC (Chemistry; Metallurgy), D (Textiles; Paper), E (Fixed Constructions),\nF (Mechanical Engineering; Lightning; Heating; Weapons; Blasting),\nG (Physics), H (Electricity), and\nY (General tagging of new or cross-sectional technology)\nThere are two features:\n - description: detailed description of patent.\n - summary: Patent abastract.\n", "citation": "\n@misc{sharma2019bigpatent,\n title={BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization},\n author={Eva Sharma and Chen Li and Lu Wang},\n year={2019},\n eprint={1906.03741},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n", "homepage": "https://evasharma.github.io/bigpatent/", "license": "", "features": {"description": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "description", "output": "abstract"}, "builder_name": "big_patent", "config_name": "a", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3329778447, "num_examples": 174134, "dataset_name": "big_patent"}, "validation": {"name": "validation", "num_bytes": 184116486, "num_examples": 9674, "dataset_name": "big_patent"}, "test": {"name": "test", "num_bytes": 185987552, "num_examples": 9675, "dataset_name": "big_patent"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1J3mucMFTWrgAYa3LuBZoLRR3CzzYD3fa": {"num_bytes": 6448045871, "checksum": "7e1093c7e0d09677c79bd872a07b6a6dd2b3235633207e9918b75056205f04dc"}}, "download_size": 6448045871, "post_processing_size": null, "dataset_size": 3699882485, "size_in_bytes": 10147928356}, "b": {"description": "\nBIGPATENT, consisting of 1.3 million records of U.S. patent documents\nalong with human written abstractive summaries.\nEach US patent application is filed under a Cooperative Patent Classification\n(CPC) code. There are nine such classification categories:\nA (Human Necessities), B (Performing Operations; Transporting),\nC (Chemistry; Metallurgy), D (Textiles; Paper), E (Fixed Constructions),\nF (Mechanical Engineering; Lightning; Heating; Weapons; Blasting),\nG (Physics), H (Electricity), and\nY (General tagging of new or cross-sectional technology)\nThere are two features:\n - description: detailed description of patent.\n - summary: Patent abastract.\n", "citation": "\n@misc{sharma2019bigpatent,\n title={BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization},\n author={Eva Sharma and Chen Li and Lu Wang},\n year={2019},\n eprint={1906.03741},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n", "homepage": "https://evasharma.github.io/bigpatent/", "license": "", "features": {"description": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "description", "output": "abstract"}, "builder_name": "big_patent", "config_name": "b", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2574594655, "num_examples": 161520, "dataset_name": "big_patent"}, "validation": {"name": "validation", "num_bytes": 143029380, "num_examples": 8973, "dataset_name": "big_patent"}, "test": {"name": "test", "num_bytes": 140741033, "num_examples": 8974, "dataset_name": "big_patent"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1J3mucMFTWrgAYa3LuBZoLRR3CzzYD3fa": {"num_bytes": 6448045871, "checksum": "7e1093c7e0d09677c79bd872a07b6a6dd2b3235633207e9918b75056205f04dc"}}, "download_size": 6448045871, "post_processing_size": null, "dataset_size": 2858365068, "size_in_bytes": 9306410939}, "c": {"description": "\nBIGPATENT, consisting of 1.3 million records of U.S. patent documents\nalong with human written abstractive summaries.\nEach US patent application is filed under a Cooperative Patent Classification\n(CPC) code. There are nine such classification categories:\nA (Human Necessities), B (Performing Operations; Transporting),\nC (Chemistry; Metallurgy), D (Textiles; Paper), E (Fixed Constructions),\nF (Mechanical Engineering; Lightning; Heating; Weapons; Blasting),\nG (Physics), H (Electricity), and\nY (General tagging of new or cross-sectional technology)\nThere are two features:\n - description: detailed description of patent.\n - summary: Patent abastract.\n", "citation": "\n@misc{sharma2019bigpatent,\n title={BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization},\n author={Eva Sharma and Chen Li and Lu Wang},\n year={2019},\n eprint={1906.03741},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n", "homepage": "https://evasharma.github.io/bigpatent/", "license": "", "features": {"description": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "description", "output": "abstract"}, "builder_name": "big_patent", "config_name": "c", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2641973267, "num_examples": 101042, "dataset_name": "big_patent"}, "validation": {"name": "validation", "num_bytes": 145441704, "num_examples": 5613, "dataset_name": "big_patent"}, "test": {"name": "test", "num_bytes": 149052258, "num_examples": 5614, "dataset_name": "big_patent"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1J3mucMFTWrgAYa3LuBZoLRR3CzzYD3fa": {"num_bytes": 6448045871, "checksum": "7e1093c7e0d09677c79bd872a07b6a6dd2b3235633207e9918b75056205f04dc"}}, "download_size": 6448045871, "post_processing_size": null, "dataset_size": 2936467229, "size_in_bytes": 9384513100}, "d": {"description": "\nBIGPATENT, consisting of 1.3 million records of U.S. patent documents\nalong with human written abstractive summaries.\nEach US patent application is filed under a Cooperative Patent Classification\n(CPC) code. 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