# coding=utf-8 # Copyright 2022 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. import gzip import json from datetime import datetime from functools import lru_cache from typing import Dict, List import datasets from datasets.tasks import LanguageModeling _CITATION = """\ @misc{BritishLibraryBooks2021, author = {British Library Labs}, title = {Digitised Books. c. 1510 - c. 1900. JSONL (OCR derived text + metadata)}, year = {2021}, publisher = {British Library}, howpublished={https://doi.org/10.23636/r7w6-zy15} """ _DESCRIPTION = """\ A dataset comprising of text created by OCR from the 49,455 digitised books, equating to 65,227 volumes (25+ million pages), published between c. 1510 - c. 1900. The books cover a wide range of subject areas including philosophy, history, poetry and literature. """ _BASE_URL = "https://bl.iro.bl.uk/downloads/" _DATA_URLS = { "1510_1699": _BASE_URL + "61f58234-b370-422f-8591-8f98e46c2757?locale=en", "1700_1799": _BASE_URL + "78b4a8ec-395e-4383-831c-809faff85ad7?locale=en", "1800_1809": _BASE_URL + "91ae15cb-e08f-4abf-8396-e4742d9d4e37?locale=en", "1810_1819": _BASE_URL + "6d1a6e17-f28d-45b9-8f7a-a03cf3a96491?locale=en", "1820_1829": _BASE_URL + "ec764dbd-1ed4-4fc2-8668-b4df5c8ec451?locale=en", "1830_1839": _BASE_URL + "eab68022-0418-4df7-a401-78972514ed20?locale=en", "1840_1849": _BASE_URL + "d16d88b0-aa3f-4dfe-b728-c58d168d7b4d?locale=en", "1850_1859": _BASE_URL + "a6a44ea8-8d33-4880-8b17-f89c90e3d89a?locale=en", "1860_1869": _BASE_URL + "2e17f00f-52e6-4259-962c-b88ad60dec23?locale=en", "1870_1879": _BASE_URL + "899c3719-030c-4517-abd3-b28fdc85eed4?locale=en", "1880_1889": _BASE_URL + "ec3b8545-775b-47bd-885d-ce895263709e?locale=en", "1890_1899": _BASE_URL + "54ed2842-089a-439a-b751-2179b3ffba28?locale=en", } _ALL = list(_DATA_URLS.values()) _1800_1899 = [ _DATA_URLS.get(subset) for subset in [ "1800_1809", "1810_1819", "1820_1829", "1830_1839", "1840_1849", "1850_1859", "1860_1869", "1870_1879", "1880_1889", "1890_1899", ] ] _1700_1799 = [_DATA_URLS.get(subset) for subset in ["1700_1799"]] _1510_1699 = [_DATA_URLS.get(subset) for subset in ["1510_1699"]] URL = "https://doi.org/10.23636/r7w6-zy15" features = datasets.Features( { "record_id": datasets.Value("string"), "date": datasets.Value("timestamp[s]"), "raw_date": datasets.Value("string"), "title": datasets.Value("string"), "place": datasets.Value("string"), "empty_pg": datasets.Value("bool"), "text": datasets.Value("string"), "pg": datasets.Value("int32"), "mean_wc_ocr": datasets.Value("float32"), "std_wc_ocr": datasets.Value("float64"), "name": datasets.Value("string"), "all_names": datasets.Value("string"), "Publisher": datasets.Value("string"), "Country of publication 1": datasets.Value("string"), "all Countries of publication": datasets.Value("string"), "Physical description": datasets.Value("string"), "Language_1": datasets.Value("string"), "Language_2": datasets.Value("string"), "Language_3": datasets.Value("string"), "Language_4": datasets.Value("string"), "multi_language": datasets.Value("bool"), } ) class BritishLibraryBooksConfig(datasets.BuilderConfig): """BuilderConfig for BritishLibraryBooks.""" def __init__(self, data_urls, citation, url, skip_empty=False, **kwargs): """BuilderConfig for BritishLibraryBooks. Args: data_url: `string`, url to download the zip file from. citation: `string`, citation for the data set. url: `string`, url for information about the data set. skip_empty: `bool`, whether to skip empty pages. **kwargs: keyword arguments forwarded to super. """ super(BritishLibraryBooksConfig, self).__init__(version=datasets.Version("1.0.2"), **kwargs) self.url: str = url self.data_urls: List[str] = data_urls self.citation: str = citation self.skip_empty: bool = skip_empty class BritishLibraryBooks(datasets.GeneratorBasedBuilder): """The BritishLibraryBooks dataset.""" BUILDER_CONFIGS = [ BritishLibraryBooksConfig( name="1500_1899", description="All periods of" + _DESCRIPTION, data_urls=_ALL, citation=_CITATION, url=URL, skip_empty=True, ), BritishLibraryBooksConfig( name="1800_1899", description="A subset covering texts published during the 1800-1899 of" + _DESCRIPTION, data_urls=_1800_1899, citation=_CITATION, url=URL, skip_empty=True, ), BritishLibraryBooksConfig( name="1700_1799", description="Subset covering 1700-1799 of" + _DESCRIPTION, data_urls=_1700_1799, citation=_CITATION, url=URL, skip_empty=True, ), BritishLibraryBooksConfig( name="1510_1699", description="Subset covering 1510-1699 of " + _DESCRIPTION, data_urls=_1510_1699, citation=_CITATION, url=URL, skip_empty=True, ), ] DEFAULT_CONFIG_NAME = "1500_1899" def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=features, supervised_keys=None, homepage="https://www.bl.uk/collection-guides/digitised-printed-books", citation=_CITATION, task_templates=[LanguageModeling(text_column="text")], ) def _split_generators(self, dl_manager: datasets.DownloadManager): urls_to_download = self.config.data_urls downloaded_archives = dl_manager.download(urls_to_download) downloaded_archives = [dl_manager.iter_archive(archive) for archive in downloaded_archives] return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"data_dirs": downloaded_archives})] @lru_cache(maxsize=512) def _parse_date(self, date): if date is not None: date = datetime.strptime(str(date), "%Y") return date def _parse_data(self, data: Dict) -> Dict: mean_wc_ocr = data["mean_wc_ocr"] mean_wc_ocr = float(mean_wc_ocr) if mean_wc_ocr else None std_wc_ocr = data["std_wc_ocr"] std_wc_ocr = float(data["std_wc_ocr"]) if std_wc_ocr else None date = data["date"] if date is not None: date = datetime.strptime(str(date), "%Y") return { "record_id": data["record_id"], "date": date, "raw_date": data["raw_date"], "title": data["title"], "place": data["place"], "text": data["text"], "pg": int(data["pg"]), "mean_wc_ocr": data["mean_wc_ocr"], "std_wc_ocr": std_wc_ocr, "name": data["Name"], "all_names": data["All names"], "Publisher": data["Publisher"], "Country of publication 1": data["Country of publication 1"], "all Countries of publication": data["All Countries of publication"], "Physical description": data["Physical description"], "Language_1": data["Language_1"], "Language_2": data["Language_2"], "Language_3": data["Language_3"], "Language_4": data["Language_4"], "multi_language": data["multi_language"], } def _generate_examples(self, data_dirs): skip_empty = self.config.skip_empty id_ = 0 for data_dir in data_dirs: for path, file in data_dir: if not path.endswith(".gz"): continue with gzip.open(file) as json_l: for row in json_l: data = json.loads(row) empty_pg = data["empty_pg"] if skip_empty and empty_pg: continue parsed_data = self._parse_data(data) yield id_, {**parsed_data, **{"empty_pg": empty_pg}} id_ += 1