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Upload indo_general_mt_en_id.py with huggingface_hub
Browse files- indo_general_mt_en_id.py +168 -0
indo_general_mt_en_id.py
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from pathlib import Path
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from typing import List
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import datasets
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from nusacrowd.utils import schemas
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from nusacrowd.utils.configs import NusantaraConfig
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from nusacrowd.utils.constants import Tasks
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_CITATION = """\
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@inproceedings{guntara-etal-2020-benchmarking,
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title = "Benchmarking Multidomain {E}nglish-{I}ndonesian Machine Translation",
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author = "Guntara, Tri Wahyu and
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Aji, Alham Fikri and
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Prasojo, Radityo Eko",
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booktitle = "Proceedings of the 13th Workshop on Building and Using Comparable Corpora",
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month = may,
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year = "2020",
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address = "Marseille, France",
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publisher = "European Language Resources Association",
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url = "https://aclanthology.org/2020.bucc-1.6",
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pages = "35--43",
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language = "English",
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ISBN = "979-10-95546-42-9",
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}
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"""
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_LOCAL = False
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_LANGUAGES = ["ind"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
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_DATASETNAME = "indo_general_mt_en_id"
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_DESCRIPTION = """\
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"In the context of Machine Translation (MT) from-and-to English, Bahasa Indonesia has been considered a low-resource language,
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and therefore applying Neural Machine Translation (NMT) which typically requires large training dataset proves to be problematic.
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In this paper, we show otherwise by collecting large, publicly-available datasets from the Web, which we split into several domains: news, religion, general, and
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conversation,to train and benchmark some variants of transformer-based NMT models across the domains.
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We show using BLEU that our models perform well across them , outperform the baseline Statistical Machine Translation (SMT) models,
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and perform comparably with Google Translate. Our datasets (with the standard split for training, validation, and testing), code, and models are available on https://github.com/gunnxx/indonesian-mt-data."
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"""
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_HOMEPAGE = "https://github.com/gunnxx/indonesian-mt-data"
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+
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_LICENSE = "Creative Commons Attribution Share-Alike 4.0 International"
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+
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_URLS = {
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_DATASETNAME: "https://github.com/gunnxx/indonesian-mt-data/archive/refs/heads/master.zip",
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}
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_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION]
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# Dataset does not have versioning
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_SOURCE_VERSION = "1.0.0"
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_NUSANTARA_VERSION = "1.0.0"
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class IndoGeneralMTEnId(datasets.GeneratorBasedBuilder):
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"""Indonesian General Domain MT En-Id is a machine translation dataset containing English-Indonesian parallel sentences collected from the general manuscripts."""
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BUILDER_CONFIGS = [
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NusantaraConfig(
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name="indo_general_mt_en_id_source",
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version=datasets.Version(_SOURCE_VERSION),
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description="Indonesian General Domain MT En-Id source schema",
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schema="source",
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subset_id="indo_general_mt_en_id",
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),
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NusantaraConfig(
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name="indo_general_mt_en_id_nusantara_t2t",
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version=datasets.Version(_NUSANTARA_VERSION),
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description="Indonesian General Domain MT Nusantara schema",
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schema="nusantara_t2t",
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subset_id="indo_general_mt_en_id",
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),
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]
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DEFAULT_CONFIG_NAME = "indo_general_mt_en_id_source"
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def _info(self):
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if self.config.schema == "source":
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features = datasets.Features(
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{
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"id": datasets.Value("string"),
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"src": datasets.Value("string"),
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"tgt": datasets.Value("string"),
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}
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)
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elif self.config.schema == "nusantara_t2t":
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features = schemas.text2text_features
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
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urls = _URLS[_DATASETNAME]
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data_dir = Path(dl_manager.download_and_extract(urls)) / "indonesian-mt-data-master" / "general"
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"filepath": {
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"en": [data_dir / "train.en.0", data_dir / "train.en.1", data_dir / "train.en.2", data_dir / "train.en.3"],
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"id": [data_dir / "train.id.0", data_dir / "train.id.1", data_dir / "train.id.2", data_dir / "train.id.3"],
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}
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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"filepath": {
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"en": [data_dir / "test.en"],
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"id": [data_dir / "test.id"],
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}
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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"filepath": {
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"en": [data_dir / "valid.en"],
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"id": [data_dir / "valid.id"],
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}
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},
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),
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]
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def _generate_examples(self, filepath: dict):
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data_en = None
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for file in filepath["en"]:
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if data_en is None:
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data_en = open(file, "r").readlines()
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else:
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data_en += open(file, "r").readlines()
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data_id = None
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for file in filepath["id"]:
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if data_id is None:
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data_id = open(file, "r").readlines()
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else:
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data_id += open(file, "r").readlines()
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+
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data_en = list(map(str.strip, data_en))
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data_id = list(map(str.strip, data_id))
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+
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if self.config.schema == "source":
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for id, (src, tgt) in enumerate(zip(data_en, data_id)):
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row = {
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"id": str(id),
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"src": src,
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"tgt": tgt,
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}
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yield id, row
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elif self.config.schema == "nusantara_t2t":
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for id, (src, tgt) in enumerate(zip(data_en, data_id)):
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row = {
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"id": str(id),
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"text_1": src,
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"text_2": tgt,
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"text_1_name": "eng",
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"text_2_name": "ind",
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}
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yield id, row
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else:
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raise ValueError(f"Invalid config: {self.config.name}")
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