Datasets:
Tasks:
Translation
Multilinguality:
translation
Size Categories:
10K<n<100K
Language Creators:
found
Source Datasets:
original
Tags:
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. | |
| """ASLG-PC12: Synthetic English-ASL Gloss Parallel Corpus 2012""" | |
| from __future__ import absolute_import, division, print_function | |
| import datasets | |
| _DESCRIPTION = """\ | |
| A large synthetic collection of parallel English and ASL-Gloss texts. | |
| There are two string features: text, and gloss. | |
| """ | |
| _CITATION = """\ | |
| @inproceedings{othman2012english, | |
| title={English-asl gloss parallel corpus 2012: Aslg-pc12}, | |
| author={Othman, Achraf and Jemni, Mohamed}, | |
| booktitle={5th Workshop on the Representation and Processing of Sign Languages: Interactions between Corpus and Lexicon LREC}, | |
| year={2012} | |
| } | |
| """ | |
| _GLOSS_URL = "https://www.achrafothman.net/aslsmt/corpus/sample-corpus-asl-en.asl" | |
| _TEXT_URL = "https://www.achrafothman.net/aslsmt/corpus/sample-corpus-asl-en.en" | |
| _HOMEPAGE = "https://achrafothman.net/site/asl-smt/" | |
| class ASLGPC12(datasets.GeneratorBasedBuilder): | |
| """ASLG-PC12: Synthetic English-ASL Gloss Parallel Corpus 2012""" | |
| VERSION = datasets.Version("0.0.1") # sample corpus | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| # This defines the different columns of the dataset and their types | |
| features=datasets.Features( | |
| { | |
| "gloss": datasets.Value("string"), # American sign language gloss | |
| "text": datasets.Value("string"), # English text | |
| } | |
| ), | |
| homepage=_HOMEPAGE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """Returns SplitGenerators.""" | |
| gloss_path, text_path = dl_manager.download([_GLOSS_URL, _TEXT_URL]) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={"gloss_path": gloss_path, "text_path": text_path}, | |
| ) | |
| ] | |
| def _generate_examples(self, gloss_path, text_path): | |
| """ Yields examples. """ | |
| gloss_f = open(gloss_path, "r", encoding="utf-8") | |
| text_f = open(text_path, "r", encoding="utf-8") | |
| for i, (gloss, text) in enumerate(zip(gloss_f, text_f)): | |
| yield i, {"gloss": gloss, "text": text} | |
| gloss_f.close() | |
| text_f.close() | |