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"""PANL BPPT""" |
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import os |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """\ |
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@inproceedings{id_panl_bppt, |
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author = {PAN Localization - BPPT}, |
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title = {Parallel Text Corpora, English Indonesian}, |
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year = {2009}, |
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url = {http://digilib.bppt.go.id/sampul/p92-budiono.pdf}, |
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} |
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""" |
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_DESCRIPTION = """\ |
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Parallel Text Corpora for Multi-Domain Translation System created by BPPT (Indonesian Agency for the Assessment and |
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Application of Technology) for PAN Localization Project (A Regional Initiative to Develop Local Language Computing |
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Capacity in Asia). The dataset contains around 24K sentences divided in 4 difference topics (Economic, international, |
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Science and Technology and Sport). |
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""" |
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_HOMEPAGE = "http://digilib.bppt.go.id/sampul/p92-budiono.pdf" |
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_LICENSE = "" |
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_URLs = ["https://github.com/cahya-wirawan/indonesian-language-models/raw/master/data/BPPTIndToEngCorpusHalfM.zip"] |
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class IdPanlBpptConfig(datasets.BuilderConfig): |
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"""BuilderConfig for IdPanlBppt""" |
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def __init__(self, src_tag=None, tgt_tag=None, topics=None, **kwargs): |
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"""BuilderConfig for IdPanlBppt. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(IdPanlBpptConfig, self).__init__(**kwargs) |
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self.src_tag = src_tag |
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self.tgt_tag = tgt_tag |
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self.topics = topics |
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class IdPanlBppt(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [ |
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IdPanlBpptConfig( |
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name="id_panl_bppt", |
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version=VERSION, |
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description="IdPanlBppt dataset", |
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src_tag="en", |
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tgt_tag="id", |
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topics=[ |
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{"name": "Economy", "words": "150K"}, |
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{"name": "International", "words": "150K"}, |
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{"name": "Science", "words": "100K"}, |
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{"name": "Sport", "words": "100K"}, |
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], |
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), |
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] |
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BUILDER_CONFIG_CLASS = IdPanlBpptConfig |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"translation": datasets.features.Translation(languages=[self.config.src_tag, self.config.tgt_tag]), |
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"topic": datasets.features.ClassLabel(names=[topic["name"] for topic in self.config.topics]), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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supervised_keys=None, |
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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): |
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my_urls = _URLs[0] |
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data_dir = dl_manager.download_and_extract(my_urls) |
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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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"data_dir": os.path.join(data_dir, "plain"), |
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"split": "train", |
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}, |
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), |
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] |
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def _generate_examples(self, data_dir, split): |
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logger.info("⏳ Generating %s examples from = %s", split, data_dir) |
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id = 0 |
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for topic in self.config.topics: |
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src_path = f"PANL-BPPT-{topic['name'][:3].upper()}-{self.config.src_tag.upper()}-{topic['words']}w.txt" |
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tgt_path = f"PANL-BPPT-{topic['name'][:3].upper()}-{self.config.tgt_tag.upper()}-{topic['words']}w.txt" |
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with open(os.path.join(data_dir, src_path), encoding="utf-8") as f1, open( |
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os.path.join(data_dir, tgt_path), encoding="utf-8" |
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) as f2: |
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src = f1.read().split("\n")[:-1] |
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tgt = f2.read().split("\n")[:-1] |
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for idx, (s, t) in enumerate(zip(src, tgt)): |
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yield id, { |
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"id": str(id), |
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"translation": {self.config.src_tag: s, self.config.tgt_tag: t}, |
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"topic": topic["name"], |
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} |
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id += 1 |
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