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import gzip
from datasets import (
BuilderConfig,
GeneratorBasedBuilder,
DownloadManager,
StreamingDownloadManager,
Version,
SplitGenerator,
Split,
DatasetInfo,
Features,
Value,
)
from typing import Union
from pathlib import Path
class TatoebaChallengeConfig(BuilderConfig):
"""Builder config for Tatoeba challenge dataset."""
def __init__(self, name: str, version: str, **kwargs):
assert version == "2023-09-26", "Only v2023-09-26 is supported"
super().__init__(
name=name, version=Version(version.replace("-", ".")), **kwargs
)
self.version_str = version
self.data_url = (
f"https://object.pouta.csc.fi/Tatoeba-Challenge-v{version}/{name}.tar"
)
class TatoebaChallenge(GeneratorBasedBuilder):
"""Tatoeba challenge dataset."""
BUILDER_CONFIG_CLASS = TatoebaChallengeConfig
BUILDER_CONFIGS = [
TatoebaChallengeConfig(name="chv-eng", version="2023-09-26"),
TatoebaChallengeConfig(name="chv-rus", version="2023-09-26"),
]
def _info(self) -> DatasetInfo:
src, trg = self.config.name.split("-")
return DatasetInfo(
description="""
The Tatoeba Translation Challenge.
You can find more about the data here: https://github.com/Helsinki-NLP/Tatoeba-Challenge
Here we have only Chuvash-English subset.
We do not use official dataset https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt
here because chv-eng of the dataset contains only test split.
""",
features=Features(
{
"id": Value("string"),
src: Value("string"),
trg: Value("string"),
}
),
supervised_keys=None,
homepage="https://github.com/Helsinki-NLP/Tatoeba-Challenge",
citation="""
@inproceedings{tiedemann-2020-tatoeba,
title = "The {T}atoeba {T}ranslation {C}hallenge {--} {R}ealistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.wmt-1.139",
pages = "1174--1182"
}
""",
)
def _split_generators(
self, dl_manager: Union[DownloadManager, StreamingDownloadManager]
):
dl_dir = dl_manager.download_and_extract(self.config.data_url)
assert isinstance(dl_dir, str)
path_to_folder = (
Path(dl_dir)
/ "data"
/ "release"
/ f"v{self.config.version_str}"
/ self.config.name
)
return [
SplitGenerator(
name=Split.TRAIN._name,
gen_kwargs={
"langs": path_to_folder / "train.id.gz",
"src": path_to_folder / "train.src.gz",
"trg": path_to_folder / "train.trg.gz",
},
),
SplitGenerator(
name=Split.TEST._name,
gen_kwargs={
"langs": path_to_folder / "test.id",
"src": path_to_folder / "test.src",
"trg": path_to_folder / "test.trg",
},
),
]
def _generate_examples(self, langs: Path, src: Path, trg: Path):
if langs.suffix == ".gz":
assert src.suffix == trg.suffix
assert trg.suffix == langs.suffix
opener = gzip.open
else:
opener = open
with opener(langs, "rb") as langs_src:
with opener(src, "rb") as src_src:
with opener(trg, "rb") as trg_src:
for id_, (langs_line, src_line, trg_line) in enumerate(
zip(langs_src, src_src, trg_src)
):
langs_row = langs_line.decode("utf8").strip().split("\t")
# train contains 3 symbols
if len(langs_row) == 3:
_, src_lang, trg_lang = langs_row
else:
src_lang, trg_lang = langs_row
yield (
id_,
{
"id": id_,
src_lang: src_line.decode("utf8").strip(),
trg_lang: trg_line.decode("utf8").strip(),
},
)
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