Datasets:

TheBritishLibrary
/

blbooks

Tasks:

Multilinguality: multilingual

Size Categories: 100K<n<1M

Language Creators: machine-generated

Annotations Creators: no-annotation

Source Datasets: original

Tags: digital-humanities-research

License:

Dataset card Viewer Files Files and versions Community

blbooks / blbooks.py

davanstrien HF staff

Add British Library books dataset (#3603)

3c0072d about 2 years ago

raw history blame contribute delete

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8.95 kB

	# 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