Add loading script

mtet.py

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+"""MTet dataset."""
+
+import json
+
+import datasets
+
+
+_CITATION = """\
+@article{mTet2022,
+    author  = {Chinh Ngo, Hieu Tran, Long Phan, Trieu H. Trinh, Hieu Nguyen, Minh Nguyen, Minh-Thang Luong},
+    title   = {MTet: Multi-domain Translation for English and Vietnamese},
+    journal = {https://github.com/vietai/mTet},
+    year    = {2022},
+}
+"""
+
+_DESCRIPTION = """\
+MTet (Multi-domain Translation for English-Vietnamese) dataset contains roughly 4.2 million English-Vietnamese pairs of
+texts, ranging across multiple different domains such as medical publications, religious texts, engineering articles,
+literature, news, and poems.
+
+This dataset extends our previous SAT (Style Augmented Translation) dataset (v1.0) by adding more high-quality
+English-Vietnamese sentence pairs on various domains.
+"""
+
+_HOMEPAGE = "https://github.com/vietai/mTet"
+
+_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)"
+
+_URLS = {
+    "en": "https://storage.googleapis.com/vietai_public/best_vi_translation/v2/train.en",
+    "vi": "https://storage.googleapis.com/vietai_public/best_vi_translation/v2/train.vi",
+}
+
+
+class Mtet(datasets.GeneratorBasedBuilder):
+    """MTet dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({"translation": datasets.features.Translation(languages=["en", "vi"])}),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_paths = dl_manager.download(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_paths": data_paths,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_paths):
+        with open(data_paths["en"], encoding="utf-8") as f_en, open(data_paths["vi"], encoding="utf-8") as f_vi:
+            for key, (sentence_en, sentence_vi) in enumerate(zip(f_en, f_vi)):
+                yield key, {"translation": {"en": sentence_en.strip(), "vi": sentence_vi.strip()}}