Datasets:
Tasks:
Translation
Multilinguality:
translation
Size Categories:
10K<n<100K
Language Creators:
found
Source Datasets:
original
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License:
File size: 2,888 Bytes
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# 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()
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