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from pathlib import Path
from typing import List
import datasets
import json
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Tasks, DEFAULT_SOURCE_VIEW_NAME, DEFAULT_SEACROWD_VIEW_NAME
_DATASETNAME = "news_en_id"
_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
_UNIFIED_VIEW_NAME = DEFAULT_SEACROWD_VIEW_NAME
_LANGUAGES = ["ind", "eng"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
_LOCAL = False
_CITATION = """\
@inproceedings{guntara-etal-2020-benchmarking,
title = "Benchmarking Multidomain {E}nglish-{I}ndonesian Machine Translation",
author = "Guntara, Tri Wahyu and
Aji, Alham Fikri and
Prasojo, Radityo Eko",
booktitle = "Proceedings of the 13th Workshop on Building and Using Comparable Corpora",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.bucc-1.6",
pages = "35--43",
language = "English",
ISBN = "979-10-95546-42-9",
}
"""
_DESCRIPTION = """\
News En-Id is a machine translation dataset containing Indonesian-English parallel sentences collected from the news. The news dataset is collected from multiple sources: Pan Asia Networking Localization (PANL), Bilingual BBC news articles, Berita Jakarta, and GlobalVoices. We split the dataset and use 75% as the training set, 10% as the validation set, and 15% as the test set. Each of the datasets is evaluated in both directions, i.e., English to Indonesian (En → Id) and Indonesian to English (Id → En) translations.
"""
_HOMEPAGE = "https://github.com/gunnxx/indonesian-mt-data"
_LICENSE = "Creative Commons Attribution Share-Alike 4.0 International"
_URLs = {"indonlg": "https://storage.googleapis.com/babert-pretraining/IndoNLG_finals/downstream_task/downstream_task_datasets.zip"}
_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class NewsEnId(datasets.GeneratorBasedBuilder):
"""Bible Su-Id is a machine translation dataset containing Indonesian-Sundanese parallel sentences collected from the bible.."""
BUILDER_CONFIGS = [
SEACrowdConfig(
name="news_en_id_source",
version=datasets.Version(_SOURCE_VERSION),
description="News En-Id source schema",
schema="source",
subset_id="news_en_id",
),
SEACrowdConfig(
name="news_en_id_seacrowd_t2t",
version=datasets.Version(_SEACROWD_VERSION),
description="News En-Id Nusantara schema",
schema="seacrowd_t2t",
subset_id="news_en_id",
),
]
DEFAULT_CONFIG_NAME = "news_en_id_source"
def _info(self):
if self.config.schema == "source":
features = datasets.Features({"id": datasets.Value("string"), "text": datasets.Value("string"), "label": datasets.Value("string")})
elif self.config.schema == "seacrowd_t2t":
features = schemas.text2text_features
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
base_path = Path(dl_manager.download_and_extract(_URLs["indonlg"])) / "IndoNLG_downstream_tasks" / "MT_IMD_NEWS"
data_files = {
"train": base_path / "train_preprocess.json",
"validation": base_path / "valid_preprocess.json",
"test": base_path / "test_preprocess.json",
}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": data_files["train"]},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": data_files["validation"]},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": data_files["test"]},
),
]
def _generate_examples(self, filepath: Path):
data = json.load(open(filepath, "r"))
if self.config.schema == "source":
for row in data:
ex = {"id": row["id"], "text": row["text"], "label": row["label"]}
yield row["id"], ex
elif self.config.schema == "seacrowd_t2t":
for row in data:
ex = {
"id": row["id"],
"text_1": row["text"],
"text_2": row["label"],
"text_1_name": "eng",
"text_2_name": "ind",
}
yield row["id"], ex
else:
raise ValueError(f"Invalid config: {self.config.name}")
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