Upload openwebtext_split.py
Browse files- openwebtext_split.py +90 -0
openwebtext_split.py
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# coding=utf-8
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""The Open WebText Corpus"""
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import os
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import re
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from itertools import chain
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import datasets
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_CITATION = """\
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@misc{Gokaslan2019OpenWeb,
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title={OpenWebText Corpus},
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author={Aaron Gokaslan*, Vanya Cohen*, Ellie Pavlick, Stefanie Tellex},
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howpublished{\\url{http://Skylion007.github.io/OpenWebTextCorpus}},
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year={2019}
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}
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"""
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_DESCRIPTION = """\
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An open-source replication of the WebText dataset from OpenAI.
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"""
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_URL = "https://zenodo.org/record/3834942/files/openwebtext.tar.xz"
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class Openwebtext(datasets.GeneratorBasedBuilder):
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"""The Open WebText dataset."""
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(
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name="plain_text",
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description="Plain text",
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version=datasets.Version("1.0.0"),
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)
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]
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features({"text": datasets.Value("string")}),
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homepage="https://skylion007.github.io/OpenWebTextCorpus/",
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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dl_dir = dl_manager.download_and_extract(_URL)
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owt_dir = os.path.join(dl_dir, "openwebtext")
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subset_xzs = [
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os.path.join(owt_dir, file_name)
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for file_name in sorted(os.listdir(owt_dir))
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if file_name.endswith("xz") # filter out ...xz.lock
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]
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ex_dirs = dl_manager.extract(subset_xzs, num_proc=round(os.cpu_count() * 0.75))
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nested_txt_files = [
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[
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os.path.join(ex_dir, txt_file_name)
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for txt_file_name in sorted(os.listdir(ex_dir))
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if txt_file_name.endswith("txt")
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]
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for ex_dir in ex_dirs
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]
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txt_files = chain(*nested_txt_files)
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train_end_idx = int(0.9 * len(txt_files))
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val_end_idx = train_end_idx + int(0.05 * len(txt_files))
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return [
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"txt_files": txt_files[:train_end_idx], "split": "train"}),
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"txt_files": txt_files[train_end_idx:val_end_idx], "split": "validation"}),
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"txt_files": txt_files[val_end_idx:], "split": "test"})
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]
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def _generate_examples(self, txt_files):
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"""Yields examples."""
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for idx, filepath in enumerate(txt_files):
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with open(filepath, encoding="utf-8") as f:
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yield idx, {"text": re.sub("\n\n\n+", "\n\n", f.read()).strip()}
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