diff --git a/seacrowd/sea_datasets/myanmar_rakhine_parallel/__init__.py b/seacrowd/sea_datasets/myanmar_rakhine_parallel/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/seacrowd/sea_datasets/myanmar_rakhine_parallel/myanmar_rakhine_parallel.py b/seacrowd/sea_datasets/myanmar_rakhine_parallel/myanmar_rakhine_parallel.py new file mode 100644 index 000000000..3a351d19e --- /dev/null +++ b/seacrowd/sea_datasets/myanmar_rakhine_parallel/myanmar_rakhine_parallel.py @@ -0,0 +1,179 @@ +# 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 = "1.0.0" + + +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