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import os
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Licenses, Tasks
_CITATION = """\
@inproceedings{PhoMT,
title = {{PhoMT: A High-Quality and Large-Scale Benchmark Dataset for Vietnamese-English Machine Translation}},
author = {Long Doan and Linh The Nguyen and Nguyen Luong Tran and Thai Hoang and Dat Quoc Nguyen},
booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
year = {2021},
pages = {4495--4503}
}
"""
_DATASETNAME = "phomt"
_DESCRIPTION = """\
PhoMT is a high-quality and large-scale Vietnamese-English parallel dataset of 3.02M sentence pairs, which is 2.9M
pairs larger than the benchmark Vietnamese-English machine translation corpus IWSLT15. This is the first large-scale
Vietnamese-English machine translation study.
"""
_LANGUAGES = ["vie", "eng"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
_LOCAL = True
_HOMEPAGE = "https://github.com/VinAIResearch/PhoMT"
_LICENSE = Licenses.MIT.value
_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
MAP_LANG = {"eng": "en", "vie": "vi"}
def seacrowd_config_constructor(src_lang, tgt_lang, schema, version):
if src_lang == "" or tgt_lang == "":
raise ValueError(f"Invalid src_lang {src_lang} or tgt_lang {tgt_lang}")
if schema not in ["source", "seacrowd_t2t"]:
raise ValueError(f"Invalid schema: {schema}")
return SEACrowdConfig(
name="phomt_{src}_{tgt}_{schema}".format(src=src_lang, tgt=tgt_lang, schema=schema),
version=datasets.Version(version),
description="phomt schema for {schema} from {src} to {tgt}".format(schema=schema, src=src_lang, tgt=tgt_lang),
schema=schema,
subset_id="phomt_{src}_{tgt}".format(src=src_lang, tgt=tgt_lang),
)
class PhoMT(datasets.GeneratorBasedBuilder):
"""
PhoMT is a high-quality and large-scale Vietnamese-English parallel dataset of 3.02M sentence pairs, which is
2.9M pairs larger than the benchmark Vietnamese-English machine translation corpus IWSLT15.
"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
BUILDER_CONFIGS = [
seacrowd_config_constructor("eng", "vie", "source", _SOURCE_VERSION),
seacrowd_config_constructor("eng", "vie", "seacrowd_t2t", _SEACROWD_VERSION),
]
DEFAULT_CONFIG_NAME = "phomt_eng_vie_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema in ("source", "seacrowd_t2t"):
features = schemas.text2text_features
else:
raise ValueError(f"Invalid config schema: {self.config.schema}")
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
if self.config.data_dir is None:
raise ValueError("This is a local dataset. Please pass the data_dir kwarg to load_dataset.")
else:
data_dir = self.config.data_dir
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": os.path.join(data_dir, "detokenization", "train", "train.{lang}")},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": os.path.join(data_dir, "detokenization", "dev", "dev.{lang}")},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": os.path.join(data_dir, "detokenization", "test", "test.{lang}")},
),
]
def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
config_names_split = self.config.name.split("_")
src_lang = config_names_split[1]
tgt_lang = config_names_split[2]
src_path = filepath.format(lang=MAP_LANG[src_lang])
tgt_path = filepath.format(lang=MAP_LANG[tgt_lang])
with open(src_path, "r", encoding="utf8") as f:
src_lines = f.readlines()
with open(tgt_path, "r", encoding="utf8") as f:
tgt_lines = f.readlines()
if self.config.schema in ("source", "seacrowd_t2t"):
for idx, (src_line, tgt_line) in enumerate(zip(src_lines, tgt_lines)):
ex = {
"id": str(idx),
"text_1": src_line.strip(),
"text_2": tgt_line.strip(),
"text_1_name": src_lang,
"text_2_name": tgt_lang,
}
yield idx, ex
else:
raise NotImplementedError(f"Schema '{self.config.schema}' is not defined.")
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