turkic_xwmt / turkic_xwmt.py
# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
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# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# Lint as: python3
"""A Large-Scale Study of Machine Translation in Turkic Languages."""
import os
import datasets
_LANGUAGES = ["az", "ba", "en", "kaa", "kk", "ky", "ru", "sah", "tr", "uz"]
_DESCRIPTION = """\
A Large-Scale Study of Machine Translation in Turkic Languages
"""
_CITATION = """\
@inproceedings{mirzakhalov2021large,
title={A Large-Scale Study of Machine Translation in Turkic Languages},
author={Mirzakhalov, Jamshidbek and Babu, Anoop and Ataman, Duygu and Kariev, Sherzod and Tyers, Francis and Abduraufov, Otabek and Hajili, Mammad and Ivanova, Sardana and Khaytbaev, Abror and Laverghetta Jr, Antonio and others},
booktitle={Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
pages={5876--5890},
year={2021}
}
"""
_DATA_URL = (
"https://github.com/turkic-interlingua/til-mt/blob/6ac179350448895a63cc06fcfd1135882c8cc49b/xwmt/test.zip?raw=true"
)
class XWMTConfig(datasets.BuilderConfig):
"""BuilderConfig for XWMT."""
def __init__(self, language_pair=(None, None), **kwargs):
"""BuilderConfig for XWMT.
Args:
for the `datasets.features.text.TextEncoder` used for the features feature.
language_pair: pair of languages that will be used for translation. Should
contain 2-letter coded strings.
**kwargs: keyword arguments forwarded to super.
"""
name = "%s-%s" % (language_pair[0], language_pair[1])
description = ("Translation dataset from %s to %s") % (language_pair[0], language_pair[1])
super(XWMTConfig, self).__init__(
name=name,
description=description,
version=datasets.Version("1.1.0", ""),
**kwargs,
)
# Validate language pair.
source, target = language_pair
assert source in _LANGUAGES, ("Config language pair must be one of the supported languages, got: %s", source)
assert target in _LANGUAGES, ("Config language pair must be one of the supported languages, got: %s", source)
self.language_pair = language_pair
class TurkicXWMT(datasets.GeneratorBasedBuilder):
"""XWMT machine translation dataset."""
BUILDER_CONFIGS = [
XWMTConfig(
language_pair=(lang1, lang2),
)
for lang1 in _LANGUAGES
for lang2 in _LANGUAGES
if lang1 != lang2
]
def _info(self):
source, target = self.config.language_pair
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{"translation": datasets.features.Translation(languages=self.config.language_pair)}
),
supervised_keys=(source, target),
homepage="https://github.com/turkicinterlingua/til-mt",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
path = dl_manager.download_and_extract(_DATA_URL)
source, target = self.config.language_pair
source_path = os.path.join(path, "test", f"{source}-{target}", f"{source}-{target}.{source}.txt")
target_path = os.path.join(path, "test", f"{source}-{target}", f"{source}-{target}.{target}.txt")
files = {}
files["test"] = {
"source_file": source_path,
"target_file": target_path,
}
return [
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=files["test"]),
]
def _generate_examples(self, source_file, target_file):
"""This function returns the examples in the raw (text) form."""
source_sentences, target_sentences = None, None
source_sentences = open(source_file, encoding="utf-8").read().strip().split("\n")
target_sentences = open(target_file, encoding="utf-8").read().strip().split("\n")
assert len(target_sentences) == len(source_sentences), "Sizes do not match: %d vs %d for %s vs %s." % (
len(source_sentences),
len(target_sentences),
source_file,
target_file,
)
source, target = self.config.language_pair
for idx, (l1, l2) in enumerate(zip(source_sentences, target_sentences)):
result = {"translation": {source: l1, target: l2}}
# Make sure that both translations are non-empty.
if all(result.values()):
yield idx, result