Datasets:
Languages:
Yoruba
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
Tags:
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 Yoruba Text C3 dataset.""" | |
| import datasets | |
| _DESCRIPTION = """\ | |
| Yoruba Text C3 is the largest Yoruba texts collected and used to train FastText embeddings in the | |
| YorubaTwi Embedding paper: https://www.aclweb.org/anthology/2020.lrec-1.335/ | |
| """ | |
| _HOMEPAGE = "https://github.com/ajesujoba/YorubaTwi-Embedding/" | |
| _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 = "data/yo_C3_large_clean_plus_noisy.txt.gz" | |
| class YorubaTextC3(datasets.GeneratorBasedBuilder): | |
| """Yoruba Text C3 dataset.""" | |
| VERSION = datasets.Version("1.0.0") | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "text": datasets.Value("string"), | |
| } | |
| ), | |
| supervised_keys=None, | |
| homepage=_HOMEPAGE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| filepath = dl_manager.download_and_extract(URL) | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": filepath}), | |
| ] | |
| 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()} | |