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# coding=utf-8
# Copyright 2020 HuggingFace Datasets Authors.
#
# 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.
import datasets
_DESCRIPTION = """\
mono corpus from http://www.opensubtitles.org/. Please check http://www.opensubtitles.org/ for the available corpora and licenses.
"""
_HOMEPAGE_URL = "http://opus.nlpl.eu"
_CITATION = """\
P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016)
"""
_BASE_URL = "https://object.pouta.csc.fi/OPUS-{}/{}/mono/{}.txt.gz"
# Please note that only few pairs are shown here. You can use config to generate data for all language pairs
_LANGUAGES = [
("OpenSubtitles", "v2018", "en"),
]
class OpenSubtitlesConfig(datasets.BuilderConfig):
def __init__(self, *args, corpus=None, lang=None, **kwargs):
corpus.strip()
splits = corpus.split()
corpus = splits[0]
corpus_version = splits[1]
super().__init__(
*args,
name=f"{corpus}-{corpus_version}-{lang}",
**kwargs,
)
self.corpus = corpus
self.corpus_version = corpus_version
self.lang = lang
class OpenSubtitles(datasets.GeneratorBasedBuilder):
BUILDER_CONFIG_CLASS = OpenSubtitlesConfig
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"text": datasets.Value("string"),
},
),
supervised_keys=None,
homepage=_HOMEPAGE_URL,
)
def _split_generators(self, dl_manager):
def _base_url(corpus, corpus_version, lang):
return _BASE_URL.format(corpus, corpus_version, lang)
download_url = _base_url(self.config.corpus, self.config.corpus_version, self.config.lang)
path = dl_manager.download_and_extract(download_url)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"datapath": path},
)
]
def _generate_examples(self, datapath):
with open(datapath, encoding="utf-8") as f:
for text_counter, line in enumerate(f):
line = line.strip()
result = (
text_counter,
{
"id": str(text_counter),
"text": line
},
)
text_counter += 1
yield result
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