Delete loading script

big_patent.py

@@ -1,163 +0,0 @@
-# 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 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"
-
-_SPLIT_NAMES = {datasets.Split.TRAIN: "train", datasets.Split.VALIDATION: "val", datasets.Split.TEST: "test"}
-_URL = "data/{version}/{split_name}.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.
-# 2.1.2 cased raw strings (fixed).
-
-_VERSION = "2.1.2"
-
-
-class BigPatentConfig(datasets.BuilderConfig):
-    """BuilderConfig for BigPatent."""
-
-    def __init__(self, codes="all", version=_VERSION, **kwargs):
-        """BuilderConfig for BigPatent.
-        Args:
-            codes (str or list, default 'all'): CPC codes. Either 'all' or a combination
-                of {'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'y'}.
-            **kwargs: keyword arguments forwarded to super.
-        """
-        if isinstance(codes, str):
-            codes = [codes]
-        name = "+".join(codes)
-        if name == "all":
-            codes = list(_CPC_DESCRIPTION)
-        if version != _VERSION:
-            name = f"{name}-{version}"
-        super().__init__(name=name, version=version, **kwargs)
-        self.codes = codes
-
-
-class BigPatent(datasets.GeneratorBasedBuilder):
-    """BigPatent datasets."""
-
-    BUILDER_CONFIG_CLASS = BigPatentConfig
-    BUILDER_CONFIGS = [
-        BigPatentConfig(
-            codes="all",
-            description="Patents under all categories.",
-        ),
-    ] + [
-        BigPatentConfig(
-            codes=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."""
-        urls = {
-            split: _URL.format(version=self.config.version, split_name=split_name)
-            for split, split_name in _SPLIT_NAMES.items()
-        }
-        dl_paths = dl_manager.download_and_extract(urls)
-        paths = {
-            split: [
-                dl_manager.iter_files(os.path.join(dl_paths[split], split_name, code)) for code in self.config.codes
-            ]
-            for split, split_name in _SPLIT_NAMES.items()
-        }
-        return [
-            datasets.SplitGenerator(
-                name=split,
-                gen_kwargs={"paths": paths[split]},
-            )
-            for split in _SPLIT_NAMES
-        ]
-
-    def _generate_examples(self, paths=None):
-        """Yields examples."""
-        for paths_per_code in paths:
-            for path in paths_per_code:
-                with open(path, "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],
-                        }