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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
"""BigPatent Dataset."""
import glob
import gzip
import json
import os
import datasets
_HOMEPAGE = "https://evasharma.github.io/bigpatent/"
_CITATION = """
@misc{sharma2019bigpatent,
title={BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization},
author={Eva Sharma and Chen Li and Lu Wang},
year={2019},
eprint={1906.03741},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
_DESCRIPTION = """
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:
A (Human Necessities), B (Performing Operations; Transporting),
C (Chemistry; Metallurgy), D (Textiles; Paper), E (Fixed Constructions),
F (Mechanical Engineering; Lightning; Heating; Weapons; Blasting),
G (Physics), H (Electricity), and
Y (General tagging of new or cross-sectional technology)
There are two features:
- description: detailed description of patent.
- abstract: Patent abastract.
"""
_LICENSE = "Creative Commons Attribution 4.0 International"
_REPO = "https://huggingface.co/datasets/big_patent/resolve/main/data"
_URLS = {
"train": f"{_REPO}/train.zip",
"validation": f"{_REPO}/val.zip",
"test": f"{_REPO}/test.zip",
}
_DOCUMENT = "description"
_SUMMARY = "abstract"
_CPC_DESCRIPTION = {
"a": "Human Necessities",
"b": "Performing Operations; Transporting",
"c": "Chemistry; Metallurgy",
"d": "Textiles; Paper",
"e": "Fixed Constructions",
"f": "Mechanical Engineering; Lightning; Heating; Weapons; Blasting",
"g": "Physics",
"h": "Electricity",
"y": "General tagging of new or cross-sectional technology",
}
# Available versions:
# 1.0.0 lower cased tokenized words.
# 2.0.0 cased raw strings.
# 2.1.0 cased raw strings (fixed).
# TODO Add raw string versions
_VERSION = "1.0.0"
class BigPatentConfig(datasets.BuilderConfig):
"""BuilderConfig for BigPatent."""
def __init__(self, *args, cpc_codes=None, **kwargs):
"""BuilderConfig for BigPatent.
Args:
cpc_codes: str, cpc_codes
**kwargs: keyword arguments forwarded to super.
"""
super().__init__(*args, version=_VERSION, **kwargs)
self.cpc_codes = cpc_codes
class BigPatent(datasets.GeneratorBasedBuilder):
"""BigPatent datasets."""
BUILDER_CONFIGS = [
BigPatentConfig(
cpc_codes=list(_CPC_DESCRIPTION),
name="all",
description="Patents under all categories.",
),
] + [
BigPatentConfig( # pylint:disable=g-complex-comprehension
cpc_codes=[k],
name=k,
description=f"Patents under Cooperative Patent Classification (CPC) {k}: {v}",
)
for k, v in sorted(_CPC_DESCRIPTION.items())
]
DEFAULT_CONFIG_NAME = "all"
VERSION = _VERSION
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({_DOCUMENT: datasets.Value("string"), _SUMMARY: datasets.Value("string")}),
supervised_keys=(_DOCUMENT, _SUMMARY),
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
dl_paths = dl_manager.download_and_extract(_URLS)
split_dirs = {datasets.Split.TRAIN: "train", datasets.Split.VALIDATION: "val", datasets.Split.TEST: "test"}
return [
datasets.SplitGenerator(
name=split,
gen_kwargs={"path": dl_paths[split], "split_dir": split_dirs[split]},
)
for split in split_dirs
]
def _generate_examples(self, path=None, split_dir=None):
"""Yields examples."""
for cpc_code in self.config.cpc_codes:
filenames = glob.glob(os.path.join(path, split_dir, cpc_code, "*"))
for filename in sorted(filenames):
with open(filename, "rb") as fin:
fin = gzip.GzipFile(fileobj=fin)
for row in fin:
json_obj = json.loads(row)
yield json_obj["publication_number"], {
_DOCUMENT: json_obj[_DOCUMENT],
_SUMMARY: json_obj[_SUMMARY],
}
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