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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",
+                }