Datasets:
Tasks:
Text Classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
n<1K
Language Creators:
found
Annotations Creators:
no-annotation
Source Datasets:
original
Tags:
License:
File size: 4,684 Bytes
00e18ca |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""The Lampeter Corpus of Early Modern English Tracts is a collection of texts
on various subject matter published between 1640 and 1740 – a time that is marked
by the rise of mass publication, the development of a public discourse in many
areas of everyday life and, last but not least, the standardisation of British English."""
from bs4 import BeautifulSoup
import datasets
_CITATION = """ @misc{20.500.12024/3193,
title = {The Lampeter Corpus of Early Modern English Tracts},
url = {http://hdl.handle.net/20.500.12024/3193},
note = {Oxford Text Archive},
copyright = {Distributed by the University of Oxford under a Creative Commons Attribution-{ShareAlike} 3.0 Unported License},
"""
_DESCRIPTION = """The Lampeter Corpus of Early Modern English Tracts is a collection of texts on
various subject matter published between 1640 and 1740 – a time that is marked by the rise of mass
publication, the development of a public discourse in many areas of everyday life
and, last but not least, the standardisation of British English.
"""
_HOMEPAGE = "https://ota.bodleian.ox.ac.uk/repository/xmlui/handle/20.500.12024/3193"
_LICENSE = "Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)"
_URL = "https://ota.bodleian.ox.ac.uk/repository/xmlui/bitstream/handle/20.500.12024/3193/3193.xml?sequence=9&isAllowed=y"
_CLASS_MAP = {"L": "Law", "E": "Economy", "M": "Miscellaneous", "P": "Politics", "S": "Science", "R": "Religion"}
class LampeterCorpus(datasets.GeneratorBasedBuilder):
""" The Lampeter Corpus of Early Modern English Tracts is a collection of texts on
various subject matter published between 1640 and 1740. Each text is associated with a year
and one of the following topics: Law, Economy, Religion, Poitics, Science, Miscellaneous
"""
VERSION = datasets.Version("1.0.0")
def _info(self):
features = datasets.Features(
{
"id": datasets.Value("string"),
"text": datasets.Value("string"),
"date": datasets.Value("string"),
"genre": datasets.Value("string"),
"head": datasets.Value("string"),
"title": datasets.Value("string")
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_file = dl_manager.download(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_file,
"split": "train",
},
),
]
def _generate_examples(self, filepath, split):
with open(filepath, encoding="utf-8") as f:
soup=BeautifulSoup(f, features='xml')
for entry in soup.find_all("TEI"):
text_parts = []
title_with_id = entry.teiHeader.fileDesc.titleStmt.title.text
id, title = title_with_id.split(":", maxsplit=1)
id = id.strip()
title=title.strip()
date=id[-4:]
content = entry.find("text")
head=content.find("body").find("head")
if head:
head=head.text
else:
head=""
body_parts=content.find("body").find_all("p")
for body_part in body_parts:
text_parts.append(body_part.text)
full_text = " ".join(text_parts)
genre=_CLASS_MAP[id[0]]
data_point = {
"id": id,
"text": full_text,
"genre": genre,
"date": date,
"head": head,
"title": title
}
yield id, data_point |