| 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 Tasks |
|
|
| _CITATION = """\ |
| @inproceedings{koto-koto-2020-towards, |
| title = "Towards Computational Linguistics in {M}inangkabau Language: Studies on Sentiment Analysis and Machine Translation", |
| author = "Koto, Fajri and |
| Koto, Ikhwan", |
| booktitle = "Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation", |
| month = oct, |
| year = "2020", |
| address = "Hanoi, Vietnam", |
| publisher = "Association for Computational Linguistics", |
| url = "https://aclanthology.org/2020.paclic-1.17", |
| pages = "138--148", |
| } |
| """ |
|
|
| _LOCAL = False |
| _LANGUAGES = ["min", "ind"] |
| _DATASETNAME = "minangnlp_mt" |
| _DESCRIPTION = """\ |
| In this work, we create Minangkabau–Indonesian (MIN-ID) parallel corpus by using Wikipedia. We obtain 224,180 Minangkabau and |
| 510,258 Indonesian articles, and align documents through title matching, resulting in 111,430 MINID document pairs. |
| After that, we do sentence segmentation based on simple punctuation heuristics and obtain 4,323,315 Minangkabau sentences. We |
| then use the bilingual dictionary to translate Minangkabau article (MIN) into Indonesian language (ID'). Sentence alignment is conducted using |
| ROUGE-1 (F1) score (unigram overlap) (Lin, 2004) between ID’ and ID, and we pair each MIN sentencewith an ID sentence based on the highest ROUGE1. |
| We then discard sentence pairs with a score of less than 0.5 to result in 345,146 MIN-ID parallel sentences. |
| We observe that the sentence pattern in the collection is highly repetitive (e.g. 100k sentences are about biological term definition). Therefore, |
| we conduct final filtering based on top-1000 trigram by iteratively discarding sentences until the frequency of each trigram equals to 100. Finally, we |
| obtain 16,371 MIN-ID parallel sentences and conducted manual evaluation by asking two native Minangkabau speakers to assess the adequacy and |
| fluency (Koehn and Monz, 2006). The human judgement is based on scale 1–5 (1 means poor quality and 5 otherwise) and conducted against 100 random |
| samples. We average the weights of two annotators before computing the overall score, and we achieve 4.98 and 4.87 for adequacy and fluency respectively. |
| This indicates that the resulting corpus is high-quality for machine translation training. |
| """ |
|
|
| _HOMEPAGE = "https://github.com/fajri91/minangNLP" |
| _LICENSE = "MIT" |
| _URLS = { |
| _DATASETNAME: "https://github.com/fajri91/minangNLP/archive/refs/heads/master.zip", |
| } |
| _SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION] |
| |
| _SOURCE_VERSION = "1.0.0" |
| _SEACROWD_VERSION = "2024.06.20" |
|
|
|
|
| class MinangNLPmt(datasets.GeneratorBasedBuilder): |
| """16,371-size parallel Minangkabau-Indonesian sentence pairs.""" |
|
|
| SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
| SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) |
|
|
| BUILDER_CONFIGS = [ |
| SEACrowdConfig( |
| name="minangnlp_mt_source", |
| version=SOURCE_VERSION, |
| description="MinangNLP Machine Translation source schema", |
| schema="source", |
| subset_id="minangnlp_mt", |
| ), |
| SEACrowdConfig( |
| name="minangnlp_mt_seacrowd_t2t", |
| version=SEACROWD_VERSION, |
| description="MinangNLP Machine Translation Nusantara schema", |
| schema="seacrowd_t2t", |
| subset_id="minangnlp_mt", |
| ), |
| ] |
|
|
| DEFAULT_CONFIG_NAME = "minangnlp_mt_source" |
|
|
| def _info(self) -> datasets.DatasetInfo: |
| if self.config.schema == "source": |
| features = datasets.Features( |
| { |
| "id": datasets.Value("string"), |
| "src": datasets.Value("string"), |
| "tgt": 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.""" |
| urls = _URLS[_DATASETNAME] |
| data_dir = Path(dl_manager.download_and_extract(urls)) / "minangNLP-master" / "translation" / "wiki_data" |
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "src_filepath": data_dir / "src_train.txt", |
| "tgt_filepath": data_dir / "tgt_train.txt", |
| "split": "train", |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| gen_kwargs={ |
| "src_filepath": data_dir / "src_test.txt", |
| "tgt_filepath": data_dir / "tgt_test.txt", |
| "split": "test", |
| }, |
| ), |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| datasets.SplitGenerator( |
| name=datasets.Split.VALIDATION, |
| gen_kwargs={ |
| "src_filepath": data_dir / "src_dev.txt", |
| "tgt_filepath": data_dir / "tgt_dev.txt", |
| "split": "dev", |
| }, |
| ), |
| ] |
|
|
| def _generate_examples(self, src_filepath: Path, tgt_filepath: Path, split: str) -> Tuple[int, Dict]: |
| with open(src_filepath, encoding="utf-8") as fsrc, open(tgt_filepath, encoding="utf-8") as ftgt: |
| for idx, pair in enumerate(zip(fsrc, ftgt)): |
| src, tgt = pair |
| if self.config.schema == "source": |
| row = { |
| "id": str(idx), |
| "src": src, |
| "tgt": tgt, |
| } |
| yield idx, row |
|
|
| elif self.config.schema == "seacrowd_t2t": |
| row = { |
| "id": str(idx), |
| "text_1": src, |
| "text_2": tgt, |
| "text_1_name": "min", |
| "text_2_name": "id", |
| } |
| yield idx, row |
|
|
