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burmese_romanize.py

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+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Tasks, Licenses
+
+_CITATION = """\
+@inproceedings{chenchen2017statistical,
+ author = {Ding Chenchen and
+ Chea Vichet and
+ Pa Win Pa and
+ Utiyama Masao and
+ Sumita Eiichiro},
+ title = {Statistical Romanization for Abugida Scripts: Data and Experiment on Khmer and Burmese},
+ booktitle = {Proceedings of the 23rd Annual Conference of the Association for Natural Language Processing,
+ {NLP2017}, Tsukuba, Japan, 13-17 March 2017},
+ year = {2017},
+ url = {https://www.anlp.jp/proceedings/annual_meeting/2017/pdf_dir/P5-7.pdf},
+}
+"""
+
+_DATASETNAME = "burmese_romanize"
+_DESCRIPTION = """\
+This dataset consists of 2,335 Burmese names from real university students and faculty,
+public figures, and minorities from Myanmar. Each entry includes the original name in
+Burmese script, its corresponding Romanization, and the aligned Burmese and Latin
+graphemes.
+"""
+
+_HOMEPAGE = "http://www.nlpresearch-ucsy.edu.mm/NLP_UCSY/name-db.html"
+_LANGUAGES = ["mya"]
+_LICENSE = Licenses.CC_BY_NC_SA_4_0.value
+_URLS = "http://www.nlpresearch-ucsy.edu.mm/NLP_UCSY/myanmaroma.zip"
+
+_SUPPORTED_TASKS = [Tasks.TRANSLITERATION]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+_LOCAL = False
+
+
+class BurmeseRomanizeDataset(datasets.GeneratorBasedBuilder):
+ """Romanization of names in Burmese script"""
+
+ 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=f"{_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=f"{_DATASETNAME}",
+ ),
+ ]
+
+ DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+ def _info(self) -> datasets.DatasetInfo:
+ if self.config.schema == "source":
+ features = datasets.Features(
+ {
+ "original": datasets.Value("string"),
+ "romanized": datasets.Value("string"),
+ "aligned_graphemes": datasets.Sequence(datasets.Value("string")),
+ }
+ )
+ elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+ 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]:
+ data_dir = Path(dl_manager.download_and_extract(_URLS)) / "myanmaroma"
+
+ return [
+ datasets.SplitGenerator(
+ name=datasets.Split.TRAIN,
+ gen_kwargs={
+ "filepath": data_dir / "myanmaroma.txt",
+ "split": "train",
+ },
+ ),
+ ]
+
+ def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+ df = pd.read_csv(filepath, sep=" \|\|\| ", engine='python', header=None, names=["ori", "roman", "seg"])
+ if self.config.schema == "source":
+ for i, row in df.iterrows():
+ yield i, {
+ "original": row["ori"],
+ "romanized": row["roman"],
+ "aligned_graphemes": row["seg"].strip().split(),
+ }
+ elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+ for i, row in df.iterrows():
+ yield i, {
+ "id": str(i),
+ "text_1": row["ori"],
+ "text_2": row["roman"],
+ "text_1_name": "original",
+ "text_2_name": "romanized",
+ }