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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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.
# Lint as: python3
"""The Language Model 1 Billion dataset."""
from __future__ import absolute_import, division, print_function
import glob
import logging
import os
import datasets
_CITATION = """\
@article{DBLP:journals/corr/ChelbaMSGBK13,
author = {Ciprian Chelba and
Tomas Mikolov and
Mike Schuster and
Qi Ge and
Thorsten Brants and
Phillipp Koehn},
title = {One Billion Word Benchmark for Measuring Progress in Statistical Language
Modeling},
journal = {CoRR},
volume = {abs/1312.3005},
year = {2013},
url = {http://arxiv.org/abs/1312.3005},
archivePrefix = {arXiv},
eprint = {1312.3005},
timestamp = {Mon, 13 Aug 2018 16:46:16 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/ChelbaMSGBK13},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""
_DESCRIPTION = """\
A benchmark corpus to be used for measuring progress in statistical language \
modeling. This has almost one billion words in the training data.
"""
_DOWNLOAD_URL = "http://www.statmt.org/lm-benchmark/" "1-billion-word-language-modeling-benchmark-r13output.tar.gz"
_TOP_LEVEL_DIR = "1-billion-word-language-modeling-benchmark-r13output"
_TRAIN_FILE_FORMAT = os.path.join(_TOP_LEVEL_DIR, "training-monolingual.tokenized.shuffled", "news.en-*")
_HELDOUT_FILE_FORMAT = os.path.join(_TOP_LEVEL_DIR, "heldout-monolingual.tokenized.shuffled", "news.en.heldout-*")
class Lm1bConfig(datasets.BuilderConfig):
"""BuilderConfig for Lm1b."""
def __init__(self, **kwargs):
"""BuilderConfig for Lm1b.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(Lm1bConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
def _train_data_filenames(tmp_dir):
return sorted(glob.glob(os.path.join(tmp_dir, _TRAIN_FILE_FORMAT)))
def _test_data_filenames(tmp_dir):
return sorted(glob.glob(os.path.join(tmp_dir, _HELDOUT_FILE_FORMAT)))
class Lm1b(datasets.GeneratorBasedBuilder):
"""1 Billion Word Language Model Benchmark dataset."""
BUILDER_CONFIGS = [
Lm1bConfig(
name="plain_text",
description="Plain text",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({"text": datasets.Value("string")}),
supervised_keys=("text", "text"),
homepage="http://www.statmt.org/lm-benchmark/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
lm1b_path = dl_manager.download_and_extract(_DOWNLOAD_URL)
train_files = _train_data_filenames(lm1b_path)
test_files = _test_data_filenames(lm1b_path)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"files": train_files}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"files": test_files}),
]
def _generate_examples(self, files):
for filepath in files:
logging.info("generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
for idx, line in enumerate(f):
yield "%s_%d" % (os.path.basename(filepath), idx), {
"text": line.strip(),
}
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