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Update files from the datasets library (from 1.1.0)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.1.0

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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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dataset_infos.json ADDED
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+ {"plain_text": {"description": "An open-source replication of the WebText dataset from OpenAI.\n", "citation": "@misc{Gokaslan2019OpenWeb, \n\ttitle={OpenWebText Corpus},\n\tauthor={Aaron Gokaslan*, Vanya Cohen*, Ellie Pavlick, Stefanie Tellex},\n\thowpublished{\\url{http://Skylion007.github.io/OpenWebTextCorpus}}, \n\tyear={2019}\n}\n", "homepage": "https://skylion007.github.io/OpenWebTextCorpus/", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "openwebtext", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 39769494896, "num_examples": 8013769, "dataset_name": "openwebtext"}}, "download_checksums": {"https://zenodo.org/record/3834942/files/openwebtext.tar.xz": {"num_bytes": 12880027468, "checksum": "9fe39d154c5bc67da8c359415372b79510eb1e2edb0d035fe4f7fc3a732b9336"}}, "download_size": 12880027468, "post_processing_size": null, "dataset_size": 39769494896, "size_in_bytes": 52649522364}}
dummy/plain_text/1.0.0/dummy_data.zip ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:a86b562b3452a6bf2588ba6cd4184c3f636d473db15024a00b1e5fa7c6f69bfe
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+ size 9786
openwebtext.py ADDED
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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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+
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+ from __future__ import absolute_import, division, print_function
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+
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+ import os
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+ import re
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+ from itertools import chain
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+
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+ import datasets
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+
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+
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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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+
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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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+
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+ _URL = "https://zenodo.org/record/3834942/files/openwebtext.tar.xz"
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+
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+
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+ class Openwebtext(datasets.GeneratorBasedBuilder):
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+ """The Open WebText dataset."""
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+
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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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+
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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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+
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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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+ return [
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+ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"txt_files": txt_files}),
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+ ]
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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()}