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""" RobotsMaliAI: Bayelemaba """ | |
import datasets | |
_CITATION = """\ | |
@misc{bayelemaba2022 | |
title={Machine Learning Dataset Development for Manding Languages}, | |
author={ | |
Valentin Vydrin and | |
Christopher Homan and | |
Michael Leventhal and | |
Allashera Auguste Tapo and | |
Marco Z... and | |
INALCO Members | |
}, | |
howpublished = {url{https://github.com/robotsmali-ai/datasets}}, | |
year={2022} | |
} | |
""" | |
_DESCRIPTION = """\ | |
The Bayelemabaga dataset is a collection of 44160 aligned machine translation ready Bambara-French lines, | |
originating from Corpus Bambara de Reference. The dataset is constitued of text extracted from 231 source files, | |
varing from periodicals, books, short stories, blog posts, part of the Bible and the Quran. | |
""" | |
_URL = { | |
"parallel": "https://robotsmali-ai.github.io/datasets/bayelemabaga.tar.gz" | |
} | |
_LanguagePairs = [ | |
"bam-fr", "fr-bam"] | |
class BayelemabagaConfig(datasets.BuilderConfig): | |
""" BuilderConfig for Bayelemabaga """ | |
def __init__(self, language_pair, **kwargs) -> None: | |
""" | |
Args: | |
language_pair: language pair, you want to load | |
**kwargs: -> Super() | |
""" | |
super().__init__(**kwargs) | |
self.language_pair = language_pair | |
class Bayelemabaga(datasets.GeneratorBasedBuilder): | |
""" Bi-Lingual Bam, Fr text made for Machine Translation """ | |
VERSION = datasets.Version("1.0.0") | |
BUILDER_CONFIG_CLASS = BayelemabagaConfig | |
BUILDER_CONFIGS = [ | |
BayelemabagaConfig(name="bam-fr", description=_DESCRIPTION, language_pair="bam-fr"), | |
BayelemabagaConfig(name="fr-bam", description=_DESCRIPTION, language_pair="fr-bam") | |
] | |
def _info(self): | |
src_tag, tgt_tag = self.config.language_pair.split("-") | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features({"translation": datasets.features.Translation(languages=(src_tag, tgt_tag))}), | |
supervised_keys=(src_tag, tgt_tag), | |
homepage="https://robotsmali-ai.github.io/datasets", | |
citation=_CITATION | |
) | |
def _split_generators(self, dl_manager): | |
lang_pair = self.config.language_pair | |
src_tag, tgt_tag = lang_pair.split("-") | |
archive = dl_manager.download(_URL["parallel"]) | |
train_dir = "bayelemabaga/train" | |
valid_dir = "bayelemabaga/valid" | |
test_dir = "bayelemabaga/test" | |
train = datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs = { | |
"filepath": f"{train_dir}/train.{src_tag}", | |
"labelpath": f"{train_dir}/train.{tgt_tag}", | |
"files": dl_manager.iter_archive(archive) | |
} | |
) | |
valid = datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs = { | |
"filepath": f"{valid_dir}/dev.{src_tag}", | |
"labelpath": f"{valid_dir}/dev.{tgt_tag}", | |
"files": dl_manager.iter_archive(archive) | |
} | |
) | |
test = datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs = { | |
"filepath": f"{test_dir}/test.{src_tag}", | |
"labelpath": f"{test_dir}/test.{tgt_tag}", | |
"files": dl_manager.iter_archive(archive) | |
} | |
) | |
output = [] | |
output.append(train) | |
output.append(valid) | |
output.append(test) | |
return output | |
def _generate_examples(self, filepath, labelpath, files): | |
""" Yield examples """ | |
src_tag, tgt_tag = self.config.language_pair.split("-") | |
src, tgt = None, None | |
for path, f in files: | |
if(path == filepath): | |
src = f.read().decode("utf-8").split("\n")[:-1] | |
elif(path == labelpath): | |
tgt = f.read().decode("utf-8").split("\n")[:-1] | |
if(src is not None and tgt is not None): | |
for idx, (s,t) in enumerate(zip(src, tgt)): | |
yield idx, {"translation": {src_tag: s, tgt_tag: t}} | |
break | |