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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.
"""The Open WebText Corpus"""
import re
import datasets
from glob import glob
_CITATION = """\
Dummy text
"""
_DESCRIPTION = """\
An open-source replication of the WebText dataset from OpenAI.
"""
_N_DATA_FILES = 1
_DATA_FILES = [f for f in glob("data/*.tar")]
class Openwebtext(datasets.GeneratorBasedBuilder):
"""The Open WebText dataset."""
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="plain_text",
description="Plain text",
version=datasets.Version("1.0.0"),
)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({"text": datasets.Value("string")}),
homepage="",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
archives = dl_manager.download(_DATA_FILES)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={
"archive_iterators": [
dl_manager.iter_archive(archive) for archive in archives
],
"iter_archive": dl_manager.iter_archive
}),
]
def _generate_examples(self, archive_iterators, iter_archive):
"""Yields examples."""
for archive_iterator in archive_iterators:
for xz_filepath, xz_f in archive_iterator:
if not xz_filepath.endswith(".xz"):
continue
for txt_filepath, txt_f in iter_archive(xz_f):
if not txt_filepath.endswith(".txt"):
continue
idx = f"{xz_filepath}/{txt_filepath}"
yield idx, {"text": re.sub("\n\n\n+", "\n\n", txt_f.read().decode("utf-8")).strip()}
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