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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 os
import json
import datasets
_CITATION = """\
@article{2019t5,
author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
journal = {arXiv e-prints},
year = {2019},
archivePrefix = {arXiv},
eprint = {1910.10683},
}
"""
_DESCRIPTION = """\
This is a small subset representing the first 10K records of the original C4 dataset, "en" subset - created for testing. The records were extracted after having been shuffled.
The full 1TB+ dataset is at https://huggingface.co/datasets/c4.
"""
_URL = "https://cdn-datasets.huggingface.co/nlp/datasets/c4/c4-en-10k.tar.xz"
class C4En10k(datasets.GeneratorBasedBuilder):
"""The C4 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="https://huggingface.co/datasets/allenai/c4/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
dl_dir = dl_manager.download_and_extract(_URL)
jsonl_file = os.path.join(dl_dir, "c4-en-10k", "c4-en-10k.jsonl")
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"jsonl_file": jsonl_file}),
]
def _generate_examples(self, jsonl_file):
"""Yields examples."""
with open(jsonl_file, encoding="utf-8") as f:
idx = 0
for line in f:
rec = json.loads(line)
yield idx, {"text": rec["text"]}
idx += 1
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