Source code for torchtext.datasets.iwslt2017
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/u/0/uc?id=12ycYSzLIG253AFN35Y6qoyf9wtkOjakp',
'_PATH': '2017-01-trnmted.tgz',
'MD5': 'aca701032b1c4411afc4d9fa367796ba',
'valid_test': ['dev2010', 'tst2010'],
'language_pair': {
'en': ['nl', 'de', 'it', 'ro'],
'ro': ['de', 'en', 'nl', 'it'],
'de': ['ro', 'en', 'nl', 'it'],
'it': ['en', 'nl', 'de', 'ro'],
'nl': ['de', 'en', 'it', 'ro'],
},
'year': 17,
}
URL = SUPPORTED_DATASETS['URL']
MD5 = SUPPORTED_DATASETS['MD5']
NUM_LINES = {
'train': {
'train': {
('en', 'nl'): 237240,
('de', 'en'): 206112,
('en', 'it'): 231619,
('en', 'ro'): 220538,
('de', 'ro'): 201455,
('nl', 'ro'): 206920,
('it', 'ro'): 217551,
('de', 'nl'): 213628,
('de', 'it'): 205465,
('it', 'nl'): 233415
}
},
'valid': {
'dev2010': {
('en', 'nl'): 1003,
('de', 'en'): 888,
('en', 'it'): 929,
('en', 'ro'): 914,
('de', 'ro'): 912,
('nl', 'ro'): 913,
('it', 'ro'): 914,
('de', 'nl'): 1001,
('de', 'it'): 923,
('it', 'nl'): 1001
},
'tst2010': {
('en', 'nl'): 1777,
('de', 'en'): 1568,
('en', 'it'): 1566,
('en', 'ro'): 1678,
('de', 'ro'): 1677,
('nl', 'ro'): 1680,
('it', 'ro'): 1643,
('de', 'nl'): 1779,
('de', 'it'): 1567,
('it', 'nl'): 1669
}
},
'test': {
'dev2010': {
('en', 'nl'): 1003,
('de', 'en'): 888,
('en', 'it'): 929,
('en', 'ro'): 914,
('de', 'ro'): 912,
('nl', 'ro'): 913,
('it', 'ro'): 914,
('de', 'nl'): 1001,
('de', 'it'): 923,
('it', 'nl'): 1001
},
'tst2010': {
('en', 'nl'): 1777,
('de', 'en'): 1568,
('en', 'it'): 1566,
('en', 'ro'): 1678,
('de', 'ro'): 1677,
('nl', 'ro'): 1680,
('it', 'ro'): 1643,
('de', 'nl'): 1779,
('de', 'it'): 1567,
('it', 'nl'): 1669
}
}
}
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 IWSLT2017(root='.data', split=('train', 'valid', 'test'), language_pair=('de', 'en')):
"""IWSLT2017 dataset
The available datasets include following:
**Language pairs**:
+-----+-----+-----+-----+-----+-----+
| |'en' |'nl' |'de' |'it' |'ro' |
+-----+-----+-----+-----+-----+-----+
|'en' | | x | x | x | x |
+-----+-----+-----+-----+-----+-----+
|'nl' | x | | x | x | x |
+-----+-----+-----+-----+-----+-----+
|'de' | x | x | | x | x |
+-----+-----+-----+-----+-----+-----+
|'it' | x | x | x | | x |
+-----+-----+-----+-----+-----+-----+
|'ro' | x | x | x | x | |
+-----+-----+-----+-----+-----+-----+
For additional details refer to source website: https://wit3.fbk.eu/2017-01
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
Examples:
>>> from torchtext.datasets import IWSLT2017
>>> train_iter, valid_iter, test_iter = IWSLT2017()
>>> src_sentence, tgt_sentence = next(train_iter)
"""
valid_set = 'dev2010'
test_set = 'tst2010'
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]))
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]
iwslt_tar = os.path.join(root, SUPPORTED_DATASETS['_PATH'].split(".")[0], 'texts/DeEnItNlRo/DeEnItNlRo', 'DeEnItNlRo-DeEnItNlRo.tgz')
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:
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("IWSLT2017", NUM_LINES[split][num_lines_set_identifier[split]][tuple(sorted(language_pair))], _iter(src_data_iter, tgt_data_iter))
