Source code for gluonnlp.data.corpora.google_billion_word

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# pylint: disable=
"""Google billion words dataset."""

__all__ = ['GBWStream']

import glob
import hashlib
import io
import os
import tarfile
import zipfile

from mxnet.gluon.utils import _get_repo_file_url, check_sha1, download

from ..._constants import EOS_TOKEN
from ...base import get_home_dir
from ...vocab import Vocab
from ..dataset import CorpusDataset
from ..stream import SimpleDatasetStream


[docs]class GBWStream(SimpleDatasetStream): """1-Billion-Word word-level dataset for language modeling, from Google. The GBWSream iterates over CorpusDatasets(flatten=False). Source http://www.statmt.org/lm-benchmark License: Apache Parameters ---------- segment : {'train', 'test'}, default 'train' Dataset segment. skip_empty : bool, default True Whether to skip the empty samples produced from sample_splitters. If False, `bos` and `eos` will be added in empty samples. bos : str or None, default None The token to add at the begining of each sentence. If None, nothing is added. eos : str or None, default '<eos>' The token to add at the end of each sentence. If None, nothing is added. root : str, default '$MXNET_HOME/datasets/gbw' Path to temp folder for storing data. MXNET_HOME defaults to '~/.mxnet'. """ _archive_data = ('1-billion-word-language-modeling-benchmark-r13output.tar.gz', '4df859766482e12264a5a9d9fb7f0e276020447d') _archive_vocab = ('gbw-ebb1a287.zip', '63b335dcc27b6804d0a14acb88332d2602fe0f59') _data_file = {'train': ('training-monolingual.tokenized.shuffled', 'news.en-00*-of-00100', '5e0d7050b37a99fd50ce7e07dc52468b2a9cd9e8'), 'test': ('heldout-monolingual.tokenized.shuffled', 'news.en.heldout-00000-of-00050', '0a8e2b7496ba0b5c05158f282b9b351356875445')} _vocab_file = ('gbw-ebb1a287.vocab', 'ebb1a287ca14d8fa6f167c3a779e5e7ed63ac69f') # Directory layout: # - root ($MXNET_HOME/datasets/gbw) # - archive_file (1-billion-word-language-modeling-benchmark-r13output.tar.gz) # - dir (1-billion-word-language-modeling-benchmark-r13output) # - subdir (training-monolingual.tokenized.shuffled) # - subdir (heldout-monolingual.tokenized.shuffled) def __init__(self, segment='train', skip_empty=True, bos=None, eos=EOS_TOKEN, root=os.path.join(get_home_dir(), 'datasets', 'gbw')): root = os.path.expanduser(root) if not os.path.isdir(root): os.makedirs(root) self._root = root self._dir = os.path.join(root, '1-billion-word-language-modeling-benchmark-r13output') self._namespace = 'gluon/dataset/gbw' subdir_name, pattern, data_hash = self._data_file[segment] self._subdir = os.path.join(self._dir, subdir_name) self._file_pattern = os.path.join(self._subdir, pattern) self._data_hash = data_hash self._get_data() sampler = 'sequential' if segment != 'train' else 'random' super().__init__(dataset=CorpusDataset, file_pattern=self._file_pattern, skip_empty=skip_empty, bos=bos, eos=eos, file_sampler=sampler) def _get_data(self): archive_file_name, archive_hash = self._archive_data archive_file_path = os.path.join(self._root, archive_file_name) exists = False if os.path.exists(self._dir) and os.path.exists(self._subdir): # verify sha1 for all files in the subdir sha1 = hashlib.sha1() filenames = sorted(glob.glob(self._file_pattern)) for filename in filenames: with open(filename, 'rb') as f: while True: data = f.read(1048576) if not data: break sha1.update(data) if sha1.hexdigest() == self._data_hash: exists = True if not exists: # download archive if not os.path.exists(archive_file_path) or \ not check_sha1(archive_file_path, archive_hash): download(_get_repo_file_url(self._namespace, archive_file_name), path=self._root, sha1_hash=archive_hash) # extract archive with tarfile.open(archive_file_path, 'r:gz') as tf: tf.extractall(path=self._root) def _get_vocab(self): archive_file_name, archive_hash = self._archive_vocab vocab_file_name, vocab_hash = self._vocab_file namespace = 'gluon/dataset/vocab' root = self._root path = os.path.join(root, vocab_file_name) if not os.path.exists(path) or not check_sha1(path, vocab_hash): downloaded_path = download(_get_repo_file_url(namespace, archive_file_name), path=root, sha1_hash=archive_hash) with zipfile.ZipFile(downloaded_path, 'r') as zf: zf.extractall(path=root) return path @property def vocab(self): path = self._get_vocab() with io.open(path, 'r', encoding='utf-8') as in_file: return Vocab.from_json(in_file.read())