Add dataset loading script

sat.py

@@ -0,0 +1,74 @@
+# coding=utf-8
+# Copyright 2020 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.
+"""SAT dataset."""
+
+import json
+
+import datasets
+
+# TODO
+_CITATION = """\
+"""
+
+_DESCRIPTION = """\
+SAT (Style Augmented Translation) dataset contains roughly 3.3 million English-Vietnamese pairs of texts.
+"""
+
+_HOMEPAGE = "https://github.com/vietai/sat"
+
+# TODO
+_LICENSE = "Unknown"
+
+_URL = {
+    "train": "https://storage.googleapis.com/vietai_public/best_vi_translation/v1/train.en-vi.json",
+    "test": "https://storage.googleapis.com/vietai_public/best_vi_translation/v1/test.en-vi.json",
+}
+
+
+class Sat(datasets.GeneratorBasedBuilder):
+    """SAT 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_path = dl_manager.download(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_path": data_path["train"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "data_path": data_path["test"],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_path):
+        with open(data_path, encoding="utf-8") as f:
+            for key, line in enumerate(f):
+                yield key, json.loads(line)