Source code for torchtext.datasets.iwslt2016
import os
import io
from torchtext.utils import (download_from_url, extract_archive)
from torchtext.data.datasets_utils import (
_RawTextIterableDataset,
_wrap_split_argument,
_clean_xml_file,
_clean_tags_file,
)
SUPPORTED_DATASETS = {
'URL': 'https://drive.google.com/uc?id=1l5y6Giag9aRPwGtuZHswh3w5v3qEz8D8',
'_PATH': '2016-01.tgz',
'MD5': 'c393ed3fc2a1b0f004b3331043f615ae',
'valid_test': ['dev2010', 'tst2010', 'tst2011', 'tst2012', 'tst2013', 'tst2014'],
'language_pair': {
'en': ['ar', 'de', 'fr', 'cs'],
'ar': ['en'],
'fr': ['en'],
'de': ['en'],
'cs': ['en'],
},
'year': 16,
}
URL = SUPPORTED_DATASETS['URL']
MD5 = SUPPORTED_DATASETS['MD5']
NUM_LINES = {
'train': {
'train': {
('ar', 'en'): 224126,
('de', 'en'): 196884,
('en', 'fr'): 220400,
('cs', 'en'): 114390
}
},
'valid': {
'dev2010': {
('ar', 'en'): 887,
('de', 'en'): 887,
('en', 'fr'): 887,
('cs', 'en'): 480
},
'tst2010': {
('ar', 'en'): 1569,
('de', 'en'): 1565,
('en', 'fr'): 1664,
('cs', 'en'): 1511
},
'tst2011': {
('ar', 'en'): 1199,
('de', 'en'): 1433,
('en', 'fr'): 818,
('cs', 'en'): 1013
},
'tst2012': {
('ar', 'en'): 1702,
('de', 'en'): 1700,
('en', 'fr'): 1124,
('cs', 'en'): 1385
},
'tst2013': {
('ar', 'en'): 1169,
('de', 'en'): 993,
('en', 'fr'): 1026,
('cs', 'en'): 1327
},
'tst2014': {
('ar', 'en'): 1107,
('de', 'en'): 1305,
('en', 'fr'): 1305
}
},
'test': {
'dev2010': {
('ar', 'en'): 887,
('de', 'en'): 887,
('en', 'fr'): 887,
('cs', 'en'): 480
},
'tst2010': {
('ar', 'en'): 1569,
('de', 'en'): 1565,
('en', 'fr'): 1664,
('cs', 'en'): 1511
},
'tst2011': {
('ar', 'en'): 1199,
('de', 'en'): 1433,
('en', 'fr'): 818,
('cs', 'en'): 1013
},
'tst2012': {
('ar', 'en'): 1702,
('de', 'en'): 1700,
('en', 'fr'): 1124,
('cs', 'en'): 1385
},
'tst2013': {
('ar', 'en'): 1169,
('de', 'en'): 993,
('en', 'fr'): 1026,
('cs', 'en'): 1327
},
'tst2014': {
('ar', 'en'): 1107,
('de', 'en'): 1305,
('en', 'fr'): 1305
}
}
}
SET_NOT_EXISTS = {
('en', 'ar'): [],
('en', 'de'): [],
('en', 'fr'): [],
('en', 'cs'): ['tst2014'],
('ar', 'en'): [],
('fr', 'en'): [],
('de', 'en'): [],
('cs', 'en'): ['tst2014']
}
def _read_text_iterator(path):
with io.open(path, encoding="utf8") as f:
for row in f:
yield row
def _construct_filenames(filename, languages):
filenames = []
for lang in languages:
filenames.append(filename + "." + lang)
return filenames
def _construct_filepaths(paths, src_filename, tgt_filename):
src_path = None
tgt_path = None
for p in paths:
src_path = p if src_filename in p else src_path
tgt_path = p if tgt_filename in p else tgt_path
return (src_path, tgt_path)
[docs]@_wrap_split_argument(('train', 'valid', 'test'))
def IWSLT2016(root='.data', split=('train', 'valid', 'test'), language_pair=('de', 'en'), valid_set='tst2013', test_set='tst2014'):
"""IWSLT2016 dataset
The available datasets include following:
**Language pairs**:
+-----+-----+-----+-----+-----+-----+
| |'en' |'fr' |'de' |'cs' |'ar' |
+-----+-----+-----+-----+-----+-----+
|'en' | | x | x | x | x |
+-----+-----+-----+-----+-----+-----+
|'fr' | x | | | | |
+-----+-----+-----+-----+-----+-----+
|'de' | x | | | | |
+-----+-----+-----+-----+-----+-----+
|'cs' | x | | | | |
+-----+-----+-----+-----+-----+-----+
|'ar' | x | | | | |
+-----+-----+-----+-----+-----+-----+
**valid/test sets**: ['dev2010', 'tst2010', 'tst2011', 'tst2012', 'tst2013', 'tst2014']
For additional details refer to source website: https://wit3.fbk.eu/2016-01
Args:
root: Directory where the datasets are saved. Default: ".data"
split: split or splits to be returned. Can be a string or tuple of strings. Default: (‘train’, ‘valid’, ‘test’)
language_pair: tuple or list containing src and tgt language
valid_set: a string to identify validation set.
test_set: a string to identify test set.
Examples:
>>> from torchtext.datasets import IWSLT2016
>>> train_iter, valid_iter, test_iter = IWSLT2016()
>>> src_sentence, tgt_sentence = next(train_iter)
"""
num_lines_set_identifier = {
'train': 'train',
'valid': valid_set,
'test': test_set
}
if not isinstance(language_pair, list) and not isinstance(language_pair, tuple):
raise ValueError("language_pair must be list or tuple but got {} instead".format(type(language_pair)))
assert (len(language_pair) == 2), 'language_pair must contain only 2 elements: src and tgt language respectively'
src_language, tgt_language = language_pair[0], language_pair[1]
if src_language not in SUPPORTED_DATASETS['language_pair']:
raise ValueError("src_language '{}' is not valid. Supported source languages are {}".
format(src_language, list(SUPPORTED_DATASETS['language_pair'])))
if tgt_language not in SUPPORTED_DATASETS['language_pair'][src_language]:
raise ValueError("tgt_language '{}' is not valid for give src_language '{}'. Supported target language are {}".
format(tgt_language, src_language, SUPPORTED_DATASETS['language_pair'][src_language]))
if valid_set not in SUPPORTED_DATASETS['valid_test'] or valid_set in SET_NOT_EXISTS[language_pair]:
raise ValueError("valid_set '{}' is not valid for given language pair {}. Supported validation sets are {}".
format(valid_set, language_pair, [s for s in SUPPORTED_DATASETS['valid_test'] if s not in SET_NOT_EXISTS[language_pair]]))
if test_set not in SUPPORTED_DATASETS['valid_test'] or test_set in SET_NOT_EXISTS[language_pair]:
raise ValueError("test_set '{}' is not valid for give language pair {}. Supported test sets are {}".
format(valid_set, language_pair, [s for s in SUPPORTED_DATASETS['valid_test'] if s not in SET_NOT_EXISTS[language_pair]]))
train_filenames = ('train.{}-{}.{}'.format(src_language, tgt_language, src_language),
'train.{}-{}.{}'.format(src_language, tgt_language, tgt_language))
valid_filenames = ('IWSLT{}.TED.{}.{}-{}.{}'.format(SUPPORTED_DATASETS['year'], valid_set, src_language, tgt_language, src_language),
'IWSLT{}.TED.{}.{}-{}.{}'.format(SUPPORTED_DATASETS['year'], valid_set, src_language, tgt_language, tgt_language))
test_filenames = ('IWSLT{}.TED.{}.{}-{}.{}'.format(SUPPORTED_DATASETS['year'], test_set, src_language, tgt_language, src_language),
'IWSLT{}.TED.{}.{}-{}.{}'.format(SUPPORTED_DATASETS['year'], test_set, src_language, tgt_language, tgt_language))
src_train, tgt_train = train_filenames
src_eval, tgt_eval = valid_filenames
src_test, tgt_test = test_filenames
extracted_files = [] # list of paths to the extracted files
dataset_tar = download_from_url(SUPPORTED_DATASETS['URL'], root=root, hash_value=SUPPORTED_DATASETS['MD5'],
path=os.path.join(root, SUPPORTED_DATASETS['_PATH']), hash_type='md5')
extracted_dataset_tar = extract_archive(dataset_tar)
# IWSLT dataset's url downloads a multilingual tgz.
# We need to take an extra step to pick out the specific language pair from it.
src_language = train_filenames[0].split(".")[-1]
tgt_language = train_filenames[1].split(".")[-1]
languages = "-".join([src_language, tgt_language])
iwslt_tar = '{}/{}/texts/{}/{}/{}.tgz'
iwslt_tar = iwslt_tar.format(
root, SUPPORTED_DATASETS['_PATH'].split(".")[0], src_language, tgt_language, languages)
extracted_dataset_tar = extract_archive(iwslt_tar)
extracted_files.extend(extracted_dataset_tar)
# Clean the xml and tag file in the archives
file_archives = []
for fname in extracted_files:
if 'xml' in fname:
_clean_xml_file(fname)
file_archives.append(os.path.splitext(fname)[0])
elif "tags" in fname:
_clean_tags_file(fname)
file_archives.append(fname.replace('.tags', ''))
else:
file_archives.append(fname)
data_filenames = {
"train": _construct_filepaths(file_archives, src_train, tgt_train),
"valid": _construct_filepaths(file_archives, src_eval, tgt_eval),
"test": _construct_filepaths(file_archives, src_test, tgt_test)
}
for key in data_filenames.keys():
if len(data_filenames[key]) == 0 or data_filenames[key] is None:
raise FileNotFoundError(
"Files are not found for data type {}".format(key))
src_data_iter = _read_text_iterator(data_filenames[split][0])
tgt_data_iter = _read_text_iterator(data_filenames[split][1])
def _iter(src_data_iter, tgt_data_iter):
for item in zip(src_data_iter, tgt_data_iter):
yield item
return _RawTextIterableDataset("IWSLT2016", NUM_LINES[split][num_lines_set_identifier[split]][tuple(sorted(language_pair))], _iter(src_data_iter, tgt_data_iter))
