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medev.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.
+
+"""
+A high-quality Vietnamese-English parallel dataset constructed specifically for the medical domain, comprising approximately 360K sentence pairs
+"""
+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{medev,
+    title     = {{Improving Vietnamese-English Medical Machine Translation}},
+    author    = {Nhu Vo and Dat Quoc Nguyen and Dung D. Le and Massimo Piccardi and Wray Buntine},
+    booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING)},
+    year      = {2024}
+}
+"""
+
+_DATASETNAME = "medev"
+
+_DESCRIPTION = """\
+A high-quality Vietnamese-English parallel dataset constructed specifically for the medical domain, comprising approximately 360K sentence pairs
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/nhuvo/MedEV"
+
+_LANGUAGES = ["vie"]
+
+_LICENSE = Licenses.UNKNOWN.value
+
+_LOCAL = False
+
+_URLS = {
+    "train_en": "https://huggingface.co/datasets/nhuvo/MedEV/resolve/main/train.en.txt?download=true", 
+    "train_vie": "https://huggingface.co/datasets/nhuvo/MedEV/resolve/main/train.vi.txt?download=true",
+    "val_en": "https://huggingface.co/datasets/nhuvo/MedEV/resolve/main/val.en.new.txt?download=true", 
+    "val_vie": "https://huggingface.co/datasets/nhuvo/MedEV/resolve/main/val.vi.new.txt?download=true",
+    "test_en": "https://huggingface.co/datasets/nhuvo/MedEV/resolve/main/test.en.new.txt?download=true",
+    "test_vie": "https://huggingface.co/datasets/nhuvo/MedEV/resolve/main/test.vi.new.txt?download=true",
+}
+
+_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class MedEVDataset(datasets.GeneratorBasedBuilder):
+    """A high-quality Vietnamese-English parallel dataset constructed specifically for the medical domain, comprising approximately 360K sentence pairs"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    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_t2t",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_t2t",
+            subset_id=_DATASETNAME,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "vie_text": datasets.Value("string"),
+                    "eng_text": 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."""
+        data_dir = dl_manager.download_and_extract(_URLS)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath_en": data_dir["train_en"],
+                    "filepath_vie": data_dir["train_vie"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath_en": data_dir["test_en"],
+                    "filepath_vie": data_dir["test_vie"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath_en": data_dir["val_en"],
+                    "filepath_vie": data_dir["val_vie"],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath_en: Path, filepath_vie: Path) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        with open(filepath_en, "r", encoding="utf-8") as f:
+            en_lines = f.readlines()
+        with open(filepath_vie, "r", encoding="utf-8") as f:
+            vie_lines = f.readlines()
+
+        if self.config.schema == "source":
+            for i in range(len(vie_lines)):
+                yield i, {
+                    "id": str(i),
+                    "vie_text": vie_lines[i],
+                    "eng_text": en_lines[i],
+                }
+
+        elif self.config.schema == "seacrowd_t2t":
+            for i, (en_line, vie_line) in enumerate(list(zip(en_lines, vie_lines))):
+                yield i, {
+                    "id": str(i),
+                    "text_1": en_line,
+                    "text_2": vie_line,
+                    "text_1_name": "eng",
+                    "text_2_name": "vie",
+                }