dummy-text.py

Hugging Face's logo
Hugging Face
Search models, datasets, users...
Models
Datasets
Spaces
Docs
Solutions
Pricing



Datasets:

Skylion007
/
openwebtext 

like
199
Tasks:
Text Generation
Fill-Mask
Sub-tasks:
language-modeling
masked-language-modeling
Languages:
English
Multilinguality:
monolingual
Size Categories:
1M<n<10M
Language Creators:
found
Annotations Creators:
no-annotation
Source Datasets:
original
License:
cc0-1.0
Dataset card
Files and versions
Community
9
openwebtext
/
openwebtext.py
lhoestq's picture
lhoestq
HF STAFF
Make the dataset streamable (#3)
39d1a6d
6 months ago
raw
history
blame
contribute
delete
No virus
2.73 kB
# 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()}