lm1b.py

# coding=utf-8
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#
# 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
#
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# distributed under the License is distributed on an "AS IS" BASIS,
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# Lint as: python3
"""The Language Model 1 Billion dataset."""


import os
from fnmatch import fnmatch

import datasets


logger = datasets.logging.get_logger(__name__)


_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 = "/".join([_TOP_LEVEL_DIR, "training-monolingual.tokenized.shuffled", "news.en-*"])
_HELDOUT_FILE_FORMAT = "/".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)


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):
        archive = dl_manager.download(_DOWNLOAD_URL)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"files": dl_manager.iter_archive(archive), "pattern": _TRAIN_FILE_FORMAT},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"files": dl_manager.iter_archive(archive), "pattern": _HELDOUT_FILE_FORMAT},
            ),
        ]

    def _generate_examples(self, files, pattern):
        for path, f in files:
            if fnmatch(path, pattern):
                for idx, line in enumerate(f):
                    yield "%s_%d" % (os.path.basename(path), idx), {
                        "text": line.decode("utf-8").strip(),
                    }