enwik8.py

# 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.

import os

import datasets


_CITATION = ""

# You can copy an official description
_DESCRIPTION = """\
The dataset is based on the Hutter Prize (http://prize.hutter1.net) and contains the first 10^8 bytes of English Wikipedia in 2006 in XML
"""

_HOMEPAGE = "http://mattmahoney.net/dc/textdata.html"

_LICENSE = ""

# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {"source": "http://mattmahoney.net/dc/enwik8.zip"}


class Enwik8(datasets.GeneratorBasedBuilder):

    VERSION = datasets.Version("2.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="enwik8-standard",
            version=VERSION,
            description="This version of the dataset uses the standard split of 90M/5M/5M bytes, and yields a single text blob per split.",
        )
    ]

    DEFAULT_CONFIG_NAME = "enwik8-standard"

    def _info(self):

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "text": datasets.Value("string"),
                }
            ),
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):

        urls = _URLS["source"]
        data_dir = dl_manager.download_and_extract(urls)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "enwik8"),
                    "split": "train",
                    "start_index": 0,
                    "end_index": 90_000_000,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "enwik8"),
                    "split": "validation",
                    "start_index": 90_000_000,
                    "end_index": 95_000_000,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "enwik8"),
                    "split": "test",
                    "start_index": 95_000_000,
                    "end_index": 100_000_000,
                },
            )
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split, start_index, end_index):
        with open(filepath, encoding="utf-8") as f:
            yield 0, {"text": f.read()[start_index:end_index]}