Datasets:
Languages:
Twi
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
License:
# 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()} | |