Upload tha_lao_embassy_parcor.py with huggingface_hub

tha_lao_embassy_parcor.py

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+# 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.
+
+import os
+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 Licenses, Tasks
+
+_CITATION = """
+Wannaphong Phatthiyaphaibun. (2021). PyThaiNLP/Thai-Lao-Parallel-Corpus: \
+Thai Lao Parallel corpus v0.7 (v0.7). Zenodo \
+https://doi.org/10.5281/zenodo.5807093"""
+
+_DATASETNAME = "tha_lao_embassy_parcor"
+
+_DESCRIPTION = """\
+Thai-Lao Parallel Corpus contains equivalent Thai and Lao sentence pairs \
+derived from the website of the Royal Thai Embassy in Vientiane, Laos.
+"""
+
+_HOMEPAGE = "https://github.com/PyThaiNLP/Thai-Lao-Parallel-Corpus/tree/master"
+_LANGUAGES = ["tha", "lao"]
+_LICENSE = Licenses.CC0_1_0.value
+
+_LOCAL = False
+_URLS = {_DATASETNAME: "https://github.com/PyThaiNLP/Thai-Lao-Parallel-Corpus/raw/master/vientiane-thaiembassy-sent.csv"}
+
+_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION]
+_SOURCE_VERSION = "0.7.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class ThaLaoEmbassyParcorDataset(datasets.GeneratorBasedBuilder):
+    """Thai-Lao Parallel Corpus contains equivalent Thai and Lao sentence pairs \
+    derived from the website of the Royal Thai Embassy in Vientiane, Laos."""
+
+    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(
+                {
+                    "lao_sent": datasets.Value("string"),
+                    "thai_sent": 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]:
+        urls = _URLS[_DATASETNAME]
+        filename = dl_manager.download(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(filename),
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        dataset = pd.read_csv(filepath)
+
+        if self.config.schema == "source":
+            for i, row in dataset.iterrows():
+                yield i, {"lao_sent": row["lao_sent"], "thai_sent": row["thai_sent"]}
+
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            for i, row in dataset.iterrows():
+                yield i, {
+                    "id": i,
+                    "text_1": row["lao_sent"],
+                    "text_2": row["thai_sent"],
+                    "text_1_name": "lao",
+                    "text_2_name": "tha",
+                }