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from typing import Dict, List, Tuple |
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import datasets |
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from seacrowd.utils import schemas |
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from seacrowd.utils.configs import SEACrowdConfig |
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from seacrowd.utils.constants import Licenses, Tasks |
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_CITATION = """\ |
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@article{, |
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author = {}, |
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title = {}, |
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journal = {}, |
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volume = {}, |
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year = {}, |
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url = {}, |
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doi = {}, |
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biburl = {}, |
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bibsource = {} |
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} |
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""" |
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_LOCAL = False |
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_LANGUAGES = ["mya", "ceb", "gor", "hil", "ilo", "ind", "jav", "khm", "lao", "zlm", "nia", "tgl", "tha", "vie"] |
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_DATASETNAME = "mozilla_pontoon" |
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_DESCRIPTION = """ |
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This dataset contains crowdsource translations of more than 200 languages for |
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different Mozilla open-source projects from Mozilla's Pontoon localization platform. |
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Source sentences are in English. |
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""" |
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_HOMEPAGE = "https://huggingface.co/datasets/ayymen/Pontoon-Translations" |
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_LICENSE = Licenses.BSD_3_CLAUSE.value |
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_URL = "https://huggingface.co/datasets/ayymen/Pontoon-Translations" |
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_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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class MozillaPontoonDataset(datasets.GeneratorBasedBuilder): |
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"""Dataset of translations from Mozilla's Pontoon platform.""" |
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LANG_CODE_MAPPER = {"mya": "my", "ceb": "ceb", "gor": "gor", "hil": "hil", "ilo": "ilo", "ind": "id", "jav": "jv", "khm": "km", "lao": "lo", "zlm": "ms", "nia": "nia", "tgl": "tl", "tha": "th", "vie": "vi"} |
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BUILDER_CONFIGS = [ |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_eng_{lang}_source", |
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version=datasets.Version(_SOURCE_VERSION), |
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description=f"{_DATASETNAME} source schema for {lang} language", |
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schema="source", |
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subset_id=f"{_DATASETNAME}_eng_{lang}", |
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) |
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for lang in _LANGUAGES |
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] + [ |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_eng_{lang}_seacrowd_t2t", |
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version=datasets.Version(_SEACROWD_VERSION), |
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description=f"{_DATASETNAME} SEACrowd schema for {lang} language", |
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schema="seacrowd_t2t", |
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subset_id=f"{_DATASETNAME}_eng_{lang}", |
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) |
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for lang in _LANGUAGES |
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] |
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BUILDER_CONFIGS.extend( |
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[ |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_source", |
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version=datasets.Version(_SOURCE_VERSION), |
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description=f"{_DATASETNAME} source schema for all languages", |
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schema="source", |
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subset_id=_DATASETNAME, |
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), |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_seacrowd_t2t", |
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version=datasets.Version(_SEACROWD_VERSION), |
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description=f"{_DATASETNAME} SEACrowd schema for all languages", |
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schema="seacrowd_t2t", |
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subset_id=_DATASETNAME, |
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), |
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] |
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) |
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source" |
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def _info(self) -> datasets.DatasetInfo: |
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if self.config.schema == "source": |
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features = datasets.Features( |
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{ |
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"source_string": datasets.Value("string"), |
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"target_string": datasets.Value("string"), |
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} |
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) |
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elif self.config.schema == "seacrowd_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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"""Returns SplitGenerators.""" |
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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={"split": "train"}, |
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), |
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] |
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def _load_hf_data_from_remote(self, language: str) -> datasets.DatasetDict: |
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"""Load dataset from HuggingFace.""" |
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hf_lang_code = self.LANG_CODE_MAPPER[language] |
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hf_remote_ref = "/".join(_URL.split("/")[-2:]) |
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return datasets.load_dataset(hf_remote_ref, f"en-{hf_lang_code}", split="train") |
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def _generate_examples(self, split: str) -> Tuple[int, Dict]: |
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"""Yields examples as (key, example) tuples.""" |
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languages = [] |
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pontoon_datasets = [] |
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lang = self.config.subset_id.split("_")[-1] |
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if lang in _LANGUAGES: |
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languages.append(lang) |
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pontoon_datasets.append(self._load_hf_data_from_remote(lang)) |
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else: |
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for lang in _LANGUAGES: |
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languages.append(lang) |
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pontoon_datasets.append(self._load_hf_data_from_remote(lang)) |
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index = 0 |
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for lang, lang_subset in zip(languages, pontoon_datasets): |
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for row in lang_subset: |
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if self.config.schema == "source": |
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example = row |
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elif self.config.schema == "seacrowd_t2t": |
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example = { |
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"id": str(index), |
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"text_1": row["source_string"], |
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"text_2": row["target_string"], |
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"text_1_name": "eng", |
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"text_2_name": lang, |
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} |
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yield index, example |
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index += 1 |
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