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"""KoPI-NLLB corpus.""" |
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import json |
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import datasets |
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import zstandard as zstd |
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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 (DEFAULT_NUSANTARA_VIEW_NAME, |
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DEFAULT_SOURCE_VIEW_NAME, Tasks) |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """ |
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Hefferman et al, Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages. Arxiv https://arxiv.org/abs/2205.12654, 2022. |
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NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv https://arxiv.org/abs/2207.04672, 2022. |
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""" |
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_DESCRIPTION = """\ |
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KopI(Korpus Perayapan Indonesia)-NLLB, is Indonesian family language(aceh,bali,banjar,indonesia,jawa,minang,sunda) only extracted from NLLB Dataset, allenai/nllb |
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each language set also filtered using some some deduplicate technique such as exact hash(md5) dedup technique and minhash LSH neardup |
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""" |
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_TYPE = ["raw", "dedup", "neardup"] |
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_CONF_LANG = ["ace_Latn", "ban_Latn", "bjn_Latn", "ind_Latn", "jav_Latn", "min_Latn", "sun_Latn"] |
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_CONFIGS = [] |
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for j in _CONF_LANG: |
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for m in _TYPE: |
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_CONFIGS.append(j + "-" + m) |
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_ALL_CONFIG = ["all-raw", "all-dedup", "all-neardup"] + _CONFIGS |
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_HOMEPAGE = "https://huggingface.co/datasets/munggok/KoPI-NLLB" |
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_LICENSE = "ODC_C" |
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_BASE_URL = "https://huggingface.co/datasets/munggok/KoPI-NLLB/resolve/main/{tipe}/{lang}.json.zst" |
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_DATASETNAME = "kopi_nllb" |
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_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING] |
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_LANGUAGES = ["ind", "jav", "ace", "ban", "bjn", "min", "sun"] |
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_NUSANTARA_VERSION = "1.0.0" |
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_SOURCE_VERSION = "2022.09.13" |
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_LOCAL = False |
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_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME |
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_UNIFIED_VIEW_NAME = DEFAULT_NUSANTARA_VIEW_NAME |
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_URL = "https://huggingface.co/datasets/allenai/nllb" |
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def nusantara_config_constructor(lang, schema, version): |
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"""Construct NusantaraConfig""" |
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if schema != "source" and schema != "nusantara_ssp": |
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raise ValueError(f"Invalid schema: {schema}") |
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if lang == "": |
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raise ValueError(f"Snapshot is required. Choose one of these Snapshot: {_ALL_CONFIG}.") |
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elif lang in _ALL_CONFIG: |
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return NusantaraConfig( |
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name=f"{_DATASETNAME}_{lang}_{schema}", |
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version=datasets.Version(version), |
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description=f"KoPI-NLLB with {schema} schema for {lang}", |
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schema=schema, |
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subset_id="kopi_nllb", |
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) |
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else: |
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raise ValueError(f"Invalid language: {lang}. Choose one of these snapshots: {_ALL_CONFIG}.") |
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class KoPINLLBConfig(datasets.BuilderConfig): |
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"""BuilderConfig for the Clean KoPI corpus.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for Clean KoPI corpus. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super().__init__(**kwargs) |
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class KoPINLLB(datasets.GeneratorBasedBuilder): |
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"""KoPI NLLB corpus.""" |
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BUILDER_CONFIGS = [nusantara_config_constructor(sn, "source", _SOURCE_VERSION) for sn in _ALL_CONFIG] + [nusantara_config_constructor(sn, "nusantara_ssp", _NUSANTARA_VERSION) for sn in _ALL_CONFIG] |
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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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"text": datasets.Value("string"), |
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"url": datasets.Value("string"), |
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"score": datasets.Value("float32"), |
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"source": datasets.Value("string"), |
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} |
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) |
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elif self.config.schema == "nusantara_ssp": |
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features = schemas.self_supervised_pretraining.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): |
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name = self.config.name.replace("_" + self.config.schema, "") |
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name = name.replace(_DATASETNAME + "_", "") |
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split_name = name.split("-") |
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if split_name[0] == "all": |
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train = [_BASE_URL.format(tipe=split_name[1], lang=m) for m in _CONF_LANG] |
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else: |
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train = [_BASE_URL.format(tipe=split_name[1], lang=split_name[0])] |
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train_downloaded_files = dl_manager.download(train) |
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return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_downloaded_files})] |
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def _generate_examples(self, filepaths): |
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"""This function returns the examples in the raw (text) form by iterating on all the files.""" |
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id_ = 0 |
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for filepath in filepaths: |
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logger.info(f"Generating examples from {filepath}") |
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with zstd.open(open(filepath, "rb"), "rt", encoding="utf-8") as f: |
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for line in f: |
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if line: |
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example = json.loads(line) |
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if self.config.schema == "nusantara_ssp": |
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yield id_, {"id": str(id_), "text": example["text"]} |
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id_ += 1 |
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else: |
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yield id_, {"text": example["text"], "url": example["url"], "source": example["source"], "score": float(example["score"])} |
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id_ += 1 |
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