Datasets:

Multilinguality: translation
Size Categories: 100K<n<1M
Language Creators: found
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Source Datasets: original
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tep_en_fa_para / tep_en_fa_para.py
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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.
"""TEP: Tehran English-Persian parallel corpus."""
import os
import datasets
_CITATION = """\
@InProceedings{“TEP: Tehran English-Persian Parallel Corpus”,
title = {TEP: Tehran English-Persian Parallel Corpus”, in proceedings of 12th International Conference \
on Intelligent Text Processing and Computational Linguistics (CICLing-2011)},
authors={M. T. Pilevar, H. Faili, and A. H. Pilevar, },
year={2011}
}
"""
_DESCRIPTION = """\
TEP: Tehran English-Persian parallel corpus. The first free Eng-Per corpus, provided by the Natural Language and Text Processing Laboratory, University of Tehran.
"""
_HOMEPAGE = "http://opus.nlpl.eu/TEP.php"
_LICENSE = ""
_URLs = {"train": "https://object.pouta.csc.fi/OPUS-TEP/v1/moses/en-fa.txt.zip"}
class TepEnFaPara(datasets.GeneratorBasedBuilder):
"""TEP: Tehran English-Persian parallel corpus."""
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="en-fa", version=VERSION),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{"translation": datasets.features.Translation(languages=tuple(self.config.name.split("-")))}
),
supervised_keys=None,
homepage="http://opus.nlpl.eu/TEP.php",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_dir = dl_manager.download_and_extract(_URLs)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"source_file": os.path.join(data_dir["train"], "TEP.en-fa.en"),
"target_file": os.path.join(data_dir["train"], "TEP.en-fa.fa"),
"split": "train",
},
),
]
def _generate_examples(self, source_file, target_file, split):
"""This function returns the examples in the raw (text) form."""
with open(source_file, encoding="utf-8") as f:
source_sentences = f.read().split("\n")
with open(target_file, encoding="utf-8") as f:
target_sentences = f.read().split("\n")
assert len(target_sentences) == len(source_sentences), "Sizes do not match: %d vs %d for %s vs %s." % (
len(source_sentences),
len(target_sentences),
source_file,
target_file,
)
source, target = tuple(self.config.name.split("-"))
for idx, (l1, l2) in enumerate(zip(source_sentences, target_sentences)):
result = {"translation": {source: l1, target: l2}}
yield idx, result