Datasets:
Multilinguality:
translation
Size Categories:
1M<n<10M
Language Creators:
found
Annotations Creators:
no-annotation
License:
| # 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) | |