# 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 pathlib import Path 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 _CITATION = """\ @inproceedings{myint-oo-etal-2019-neural, title = "Neural Machine Translation between {M}yanmar ({B}urmese) and {R}akhine ({A}rakanese)", author = "Myint Oo, Thazin and Kyaw Thu, Ye and Mar Soe, Khin", editor = {Zampieri, Marcos and Nakov, Preslav and Malmasi, Shervin and Ljube{\v{s}}i{\'c}, Nikola and Tiedemann, J{\"o}rg and Ali, Ahmed}, booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects", month = jun, year = "2019", address = "Ann Arbor, Michigan", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W19-1408", doi = "10.18653/v1/W19-1408", pages = "80--88", } """ _DATASETNAME = "myanmar_rakhine_parallel" _DESCRIPTION = """\ The data contains 18,373 Myanmar sentences of the ASEAN-MT Parallel Corpus, which is a parallel corpus in the travel domain. It contains six main categories: people (greeting, introduction, and communication), survival (transportation, accommodation, and finance), food (food, beverages, and restaurants), fun (recreation, traveling, shopping, and nightlife), resource (number, time, and accuracy), special needs (emergency and health). Manual translation into the Rakhine language was done by native Rakhine students from two Myanmar universities, and the translated corpus was checked by the editor of a Rakhine newspaper. Word segmentation for Rakhine was done manually, and there are exactly 123,018 words in total. """ _HOMEPAGE = "https://github.com/ye-kyaw-thu/myPar/tree/master/my-rk" _LANGUAGES = ["mya", "rki"] _LICENSE = Licenses.GPL_3_0.value _LOCAL = False _URLS = { "train_mya": "https://raw.githubusercontent.com/ye-kyaw-thu/myPar/master/my-rk/ver-0.1/train.my", "dev_mya": "https://raw.githubusercontent.com/ye-kyaw-thu/myPar/master/my-rk/ver-0.1/dev.my", "test_mya": "https://raw.githubusercontent.com/ye-kyaw-thu/myPar/master/my-rk/ver-0.1/test.my", "train_rki": "https://raw.githubusercontent.com/ye-kyaw-thu/myPar/master/my-rk/ver-0.1/train.rk", "dev_rki": "https://raw.githubusercontent.com/ye-kyaw-thu/myPar/master/my-rk/ver-0.1/dev.rk", "test_rki": "https://raw.githubusercontent.com/ye-kyaw-thu/myPar/master/my-rk/ver-0.1/test.rk", } _SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION] _SOURCE_VERSION = "0.1.0" _SEACROWD_VERSION = "2024.06.20" class MyanmarRakhineParallel(datasets.GeneratorBasedBuilder): """Myanmar-Rakhine Parallel dataset from https://github.com/ye-kyaw-thu/myPar/tree/master/my-rk""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) SEACROWD_SCHEMA_NAME = "t2t" BUILDER_CONFIGS = [ SEACrowdConfig( name=f"{_DATASETNAME}_source", version=SOURCE_VERSION, description=f"{_DATASETNAME} source schema", schema="source", subset_id=_DATASETNAME, ), SEACrowdConfig( name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}", version=SEACROWD_VERSION, description=f"{_DATASETNAME} SEACrowd schema", schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}", subset_id=_DATASETNAME, ), ] DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source" or self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}": features = schemas.text2text_features else: raise ValueError(f"Invalid config schema: {self.config.schema}") 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.""" data_paths = { "train_mya": Path(dl_manager.download_and_extract(_URLS["train_mya"])), "dev_mya": Path(dl_manager.download_and_extract(_URLS["dev_mya"])), "test_mya": Path(dl_manager.download_and_extract(_URLS["test_mya"])), "train_rki": Path(dl_manager.download_and_extract(_URLS["train_rki"])), "dev_rki": Path(dl_manager.download_and_extract(_URLS["dev_rki"])), "test_rki": Path(dl_manager.download_and_extract(_URLS["test_rki"])), } return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "mya_filepath": data_paths["train_mya"], "rki_filepath": data_paths["train_rki"], "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "mya_filepath": data_paths["test_mya"], "rki_filepath": data_paths["test_rki"], "split": "test", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "mya_filepath": data_paths["dev_mya"], "rki_filepath": data_paths["dev_rki"], "split": "dev", }, ), ] def _generate_examples(self, mya_filepath: Path, rki_filepath: Path, split: str) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" # read mya file with open(mya_filepath, "r", encoding="utf-8") as mya_file: mya_data = mya_file.readlines() mya_data = [s.strip("\n") for s in mya_data] # read rki file with open(rki_filepath, "r", encoding="utf-8") as rki_file: rki_data = rki_file.readlines() rki_data = [s.strip("\n") for s in rki_data] num_sample = len(mya_data) for i in range(num_sample): if self.config.schema == "source" or self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}": example = {"id": str(i), "text_1": mya_data[i], "text_2": rki_data[i], "text_1_name": "mya", "text_2_name": "rki"} yield i, example