Source code for torchtext.datasets.imdb
from torchtext.utils import download_from_url, extract_archive
from torchtext.data.datasets_utils import _RawTextIterableDataset
from torchtext.data.datasets_utils import _wrap_split_argument
from torchtext.data.datasets_utils import _add_docstring_header
import io
URL = 'http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz'
MD5 = '7c2ac02c03563afcf9b574c7e56c153a'
NUM_LINES = {
'train': 25000,
'test': 25000,
}
_PATH = 'aclImdb_v1.tar.gz'
[docs]@_add_docstring_header(num_lines=NUM_LINES, num_classes=2)
@_wrap_split_argument(('train', 'test'))
def IMDB(root, split):
def generate_imdb_data(key, extracted_files):
for fname in extracted_files:
if 'urls' in fname:
continue
elif key in fname and ('pos' in fname or 'neg' in fname):
with io.open(fname, encoding="utf8") as f:
label = 'pos' if 'pos' in fname else 'neg'
yield label, f.read()
dataset_tar = download_from_url(URL, root=root,
hash_value=MD5, hash_type='md5')
extracted_files = extract_archive(dataset_tar)
iterator = generate_imdb_data(split, extracted_files)
return _RawTextIterableDataset("IMDB", NUM_LINES[split], iterator)