twi_text_c3 / twi_text_c3.py
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Update files from the datasets library (from 1.6.0)
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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
"""The BookCorpus dataset."""
import datasets
_DESCRIPTION = """\
Twi Text C3 is the largest Twi texts collected and used to train FastText embeddings in the
YorubaTwi Embedding paper: https://www.aclweb.org/anthology/2020.lrec-1.335/
"""
_CITATION = """\
@inproceedings{alabi-etal-2020-massive,
title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of Yoruba and {T}wi",
author = "Alabi, Jesujoba and
Amponsah-Kaakyire, Kwabena and
Adelani, David and
Espa{\\~n}a-Bonet, Cristina",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://www.aclweb.org/anthology/2020.lrec-1.335",
pages = "2754--2762",
language = "English",
ISBN = "979-10-95546-34-4",
}
"""
URL = "https://drive.google.com/uc?export=download&id=1s8NSFT4Kz0caKZ4VybPNzt88F8ZanprY"
class TwiTextC3Config(datasets.BuilderConfig):
"""BuilderConfig for Twi Text C3."""
def __init__(self, **kwargs):
"""BuilderConfig for BookCorpus.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(TwiTextC3Config, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
class TwiTextC3(datasets.GeneratorBasedBuilder):
"""Twi Text C3 dataset."""
BUILDER_CONFIGS = [
TwiTextC3Config(
name="plain_text",
description="Plain text",
)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
}
),
supervised_keys=None,
homepage="https://www.aclweb.org/anthology/2020.lrec-1.335/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
arch_path = dl_manager.download_and_extract(URL)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": arch_path}),
]
def _generate_examples(self, filepath):
with open(filepath, mode="r", encoding="utf-8") as f:
lines = f.read().splitlines()
for id, line in enumerate(lines):
yield id, {"text": line.strip()}