# coding=utf-8 # Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Dict, List, Tuple import datasets from seacrowd.utils import schemas from seacrowd.utils.configs import SEACrowdConfig from seacrowd.utils.constants import Licenses, Tasks # Keep blank; dataset has no associated paper _CITATION = """\ @article{, author = {}, title = {}, journal = {}, volume = {}, year = {}, url = {}, doi = {}, biburl = {}, bibsource = {} } """ _LOCAL = False _LANGUAGES = ["mya", "ceb", "gor", "hil", "ilo", "ind", "jav", "khm", "lao", "zlm", "nia", "tgl", "tha", "vie"] _DATASETNAME = "mozilla_pontoon" _DESCRIPTION = """ This dataset contains crowdsource translations of more than 200 languages for different Mozilla open-source projects from Mozilla's Pontoon localization platform. Source sentences are in English. """ _HOMEPAGE = "https://huggingface.co/datasets/ayymen/Pontoon-Translations" _LICENSE = Licenses.BSD_3_CLAUSE.value _URL = "https://huggingface.co/datasets/ayymen/Pontoon-Translations" _SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION] _SOURCE_VERSION = "1.0.0" _SEACROWD_VERSION = "2024.06.20" class MozillaPontoonDataset(datasets.GeneratorBasedBuilder): """Dataset of translations from Mozilla's Pontoon platform.""" # Two-letter ISO code is used when available # otherwise 3-letter one is used 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"} # Config to load individual datasets per language BUILDER_CONFIGS = [ SEACrowdConfig( name=f"{_DATASETNAME}_eng_{lang}_source", version=datasets.Version(_SOURCE_VERSION), description=f"{_DATASETNAME} source schema for {lang} language", schema="source", subset_id=f"{_DATASETNAME}_eng_{lang}", ) for lang in _LANGUAGES ] + [ SEACrowdConfig( name=f"{_DATASETNAME}_eng_{lang}_seacrowd_t2t", version=datasets.Version(_SEACROWD_VERSION), description=f"{_DATASETNAME} SEACrowd schema for {lang} language", schema="seacrowd_t2t", subset_id=f"{_DATASETNAME}_eng_{lang}", ) for lang in _LANGUAGES ] # Config to load all datasets BUILDER_CONFIGS.extend( [ SEACrowdConfig( name=f"{_DATASETNAME}_source", version=datasets.Version(_SOURCE_VERSION), description=f"{_DATASETNAME} source schema for all languages", schema="source", subset_id=_DATASETNAME, ), SEACrowdConfig( name=f"{_DATASETNAME}_seacrowd_t2t", version=datasets.Version(_SEACROWD_VERSION), description=f"{_DATASETNAME} SEACrowd schema for all languages", schema="seacrowd_t2t", subset_id=_DATASETNAME, ), ] ) DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "source_string": datasets.Value("string"), "target_string": 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]: """Returns SplitGenerators.""" # dl_manager not used since dataloader uses HF 'load_dataset' return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"split": "train"}, ), ] def _load_hf_data_from_remote(self, language: str) -> datasets.DatasetDict: """Load dataset from HuggingFace.""" hf_lang_code = self.LANG_CODE_MAPPER[language] hf_remote_ref = "/".join(_URL.split("/")[-2:]) return datasets.load_dataset(hf_remote_ref, f"en-{hf_lang_code}", split="train") def _generate_examples(self, split: str) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" languages = [] pontoon_datasets = [] lang = self.config.subset_id.split("_")[-1] if lang in _LANGUAGES: languages.append(lang) pontoon_datasets.append(self._load_hf_data_from_remote(lang)) else: for lang in _LANGUAGES: languages.append(lang) pontoon_datasets.append(self._load_hf_data_from_remote(lang)) index = 0 for lang, lang_subset in zip(languages, pontoon_datasets): for row in lang_subset: if self.config.schema == "source": example = row elif self.config.schema == "seacrowd_t2t": example = { "id": str(index), "text_1": row["source_string"], "text_2": row["target_string"], "text_1_name": "eng", "text_2_name": lang, } yield index, example index += 1