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# coding=utf-8
# 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.
# Lint as: python3
"""The Stories CC dataset."""
import datasets
_DESCRIPTION = """\
CC-Stories (or STORIES) is a dataset for common sense reasoning and language modeling. It was constructed by aggregating documents from the CommonCrawl dataset that has the most overlapping n-grams with the questions in commonsense reasoning tasks. The top 1.0% of highest ranked documents is chosen as the new training corpus.
"""
_CITATION = """\
@article{Trinh2018ASM,
title={A Simple Method for Commonsense Reasoning},
author={Trieu H. Trinh and Quoc V. Le},
journal={ArXiv},
year={2018},
volume={abs/1806.02847}
}
"""
URL = "https://huggingface.co/datasets/spacemanidol/cc-stories/resolve/main/cc-stories.txt.gz"
_DATASET_URLS = {
'train': "https://huggingface.co/datasets/spacemanidol/cc-stories/resolve/main/cc-stories.txt",
}
class CCStoriesConfig(datasets.BuilderConfig):
"""BuilderConfig for CC Stories."""
def __init__(self, **kwargs):
"""BuilderConfig for CC Stories
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(CCStoriesConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
class Bookcorpus(datasets.GeneratorBasedBuilder):
"""CC Stories dataset."""
BUILDER_CONFIGS = [
CCStoriesConfig(
name="plain_text",
description="Plain text",
)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
}
),
supervised_keys=None,
homepage="",
citation=_CITATION,
)
def _vocab_text_gen(self, archive):
for _, ex in self._generate_examples(archive):
yield ex["text"]
def _split_generators(self, dl_manager):
downloaded_files = dl_manager.download_and_extract(_DATASET_URLS)
splits = [
datasets.SplitGenerator(
name=split,
gen_kwargs={
"files": [downloaded_files[split]] if isinstance(downloaded_files[split], str) else downloaded_files[split],
},
) for split in downloaded_files
]
return splits
def _generate_examples(self, files):
_id = 0
for path, file in files:
for line in file:
yield _id, {"text": line.decode("utf-8").strip()}
_id += 1 |