def SQuAD1(root, split):
extracted_files = download_from_url(URL[split],
root=root,
hash_value=MD5[split],
hash_type='md5')
return RawTextIterableDataset('SQuAD1', NUM_LINES[split],
_create_data_from_json(extracted_files))
def WikiText103(root, split):
dataset_tar = download_from_url(URL, root=root, hash_value=MD5, hash_type='md5')
extracted_files = extract_archive(dataset_tar)
path = find_match(split, extracted_files)
logging.info('Creating {} data'.format(split))
return RawTextIterableDataset('WikiText103',
NUM_LINES[split], iter(io.open(path, encoding="utf8")))
def PennTreebank(root, split):
path = download_from_url(URL[split],
root=root,
hash_value=MD5[split],
hash_type='md5')
logging.info('Creating {} data'.format(split))
return RawTextIterableDataset('PennTreebank', NUM_LINES[split],
iter(io.open(path, encoding="utf8")))
def CoNLL2000Chunking(root, split):
# Create a dataset specific subfolder to deal with generic download filenames
root = os.path.join(root, 'conll2000chunking')
path = os.path.join(root, split + ".txt.gz")
data_filename = download_extract_validate(root, URL[split], MD5[split], path, os.path.join(root, _EXTRACTED_FILES[split]),
_EXTRACTED_FILES_MD5[split], hash_type="md5")
logging.info('Creating {} data'.format(split))
return RawTextIterableDataset("CoNLL2000Chunking", NUM_LINES[split],
_create_data_from_iob(data_filename, " "))
def UDPOS(root, split):
dataset_tar = download_from_url(URL, root=root, hash_value=MD5, hash_type='md5')
extracted_files = extract_archive(dataset_tar)
if split == 'valid':
path = find_match("dev.txt", extracted_files)
else:
path = find_match(split + ".txt", extracted_files)
return RawTextIterableDataset("UDPOS", NUM_LINES[split],
_create_data_from_iob(path))
def AG_NEWS(root, split):
def _create_data_from_csv(data_path):
with io.open(data_path, encoding="utf8") as f:
reader = unicode_csv_reader(f)
for row in reader:
yield int(row[0]), ' '.join(row[1:])
path = download_from_url(URL[split],
root=root,
path=os.path.join(root, split + ".csv"),
hash_value=MD5[split],
hash_type='md5')
return RawTextIterableDataset("AG_NEWS", NUM_LINES[split],
_create_data_from_csv(path))
def YelpReviewFull(root, split):
def _create_data_from_csv(data_path):
with io.open(data_path, encoding="utf8") as f:
reader = unicode_csv_reader(f)
for row in reader:
yield int(row[0]), ' '.join(row[1:])
dataset_tar = download_from_url(URL, root=root,
path=os.path.join(root, _PATH),
hash_value=MD5, hash_type='md5')
extracted_files = extract_archive(dataset_tar)
path = find_match(split + '.csv', extracted_files)
return RawTextIterableDataset("YelpReviewFull", NUM_LINES[split],
_create_data_from_csv(path))
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)
def AmazonReviewFull(root, split):
def _create_data_from_csv(data_path):
with io.open(data_path, encoding="utf8") as f:
reader = unicode_csv_reader(f)
for row in reader:
yield int(row[0]), ' '.join(row[1:])
path = download_extract_validate(root,
URL,
MD5,
os.path.join(root, _PATH),
os.path.join(root,
_EXTRACTED_FILES[split]),
_EXTRACTED_FILES_MD5[split],
hash_type="md5")
logging.info('Creating {} data'.format(split))
return RawTextIterableDataset("AmazonReviewFull", NUM_LINES[split],
_create_data_from_csv(path))
def WMTNewsCrawl(root, split, year=2010, language='en'):
if year not in _AVAILABLE_YEARS:
raise ValueError(
"{} not available. Please choose from years {}".format(
year, _AVAILABLE_YEARS))
if language not in _AVAILABLE_LANGUAGES:
raise ValueError(
"{} not available. Please choose from languages {}".format(
language, _AVAILABLE_LANGUAGES))
path = download_extract_validate(root,
URL,
MD5,
_PATH,
_EXTRACTED_FILES[language],
_EXTRACTED_FILES_MD5[language],
hash_type="md5")
logging.info('Creating {} data'.format(split))
return RawTextIterableDataset("WMTNewsCrawl", NUM_LINES[split],
iter(io.open(path, encoding="utf8")))
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))
def Multi30k(root,
split,
task='task1',
language_pair=('de', 'en'),
train_set="train",
valid_set="val",
test_set="test_2016_flickr"):
"""Multi30k Dataset
The available datasets include following:
**Language pairs (task1)**:
+-----+-----+-----+-----+-----+
| |'en' |'cs' |'de' |'fr' |
+-----+-----+-----+-----+-----+
|'en' | | x | x | x |
+-----+-----+-----+-----+-----+
|'cs' | x | | x | x |
+-----+-----+-----+-----+-----+
|'de' | x | x | | x |
+-----+-----+-----+-----+-----+
|'fr' | x | x | x | |
+-----+-----+-----+-----+-----+
**Language pairs (task2)**:
+-----+-----+-----+
| |'en' |'de' |
+-----+-----+-----+
|'en' | | x |
+-----+-----+-----+
|'de' | x | |
+-----+-----+-----+
For additional details refer to source: https://github.com/multi30k/dataset
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’)
task: Indicate the task
language_pair: tuple or list containing src and tgt language
train_set: A string to identify train set.
valid_set: A string to identify validation set.
test_set: A string to identify test set.
Examples:
>>> from torchtext.experimental.datasets.raw import Multi30k
>>> train_iter, valid_iter, test_iter = Multi30k()
>>> src_sentence, tgt_sentence = next(train_iter)
"""
if task not in SUPPORTED_DATASETS.keys():
raise ValueError(
'task {} is not supported. Valid options are {}'.format(
task, SUPPORTED_DATASETS.keys()))
assert (
len(language_pair) == 2
), 'language_pair must contain only 2 elements: src and tgt language respectively'
if language_pair[0] not in SUPPORTED_DATASETS[task].keys():
raise ValueError(
"Source language '{}' is not supported. Valid options for task '{}' are {}"
.format(language_pair[0], task,
list(SUPPORTED_DATASETS[task].keys())))
if language_pair[1] not in SUPPORTED_DATASETS[task].keys():
raise ValueError(
"Target language '{}' is not supported. Valid options for task '{}' are {}"
.format(language_pair[1], task,
list(SUPPORTED_DATASETS[task].keys())))
if train_set not in SUPPORTED_DATASETS[task][
language_pair[0]].keys() or 'train' not in train_set:
raise ValueError(
"'{}' is not a valid train set identifier. valid options for task '{}' and language pair {} are {}"
.format(train_set, task, language_pair, [
k for k in SUPPORTED_DATASETS[task][language_pair[0]].keys()
if 'train' in k
]))
if valid_set not in SUPPORTED_DATASETS[task][
language_pair[0]].keys() or 'val' not in valid_set:
raise ValueError(
"'{}' is not a valid valid set identifier. valid options for task '{}' and language pair {} are {}"
.format(valid_set, task, language_pair, [
k for k in SUPPORTED_DATASETS[task][language_pair[0]].keys()
if 'val' in k
]))
if test_set not in SUPPORTED_DATASETS[task][
language_pair[0]].keys() or 'test' not in test_set:
raise ValueError(
"'{}' is not a valid test set identifier. valid options for task '{}' and language pair {} are {}"
.format(test_set, task, language_pair, [
k for k in SUPPORTED_DATASETS[task][language_pair[0]].keys()
if 'test' in k
]))
train_filenames = [
"{}.{}".format(train_set, language_pair[0]),
"{}.{}".format(train_set, language_pair[1])
]
valid_filenames = [
"{}.{}".format(valid_set, language_pair[0]),
"{}.{}".format(valid_set, language_pair[1])
]
test_filenames = [
"{}.{}".format(test_set, language_pair[0]),
"{}.{}".format(test_set, language_pair[1])
]
if split == 'train':
src_file, tgt_file = train_filenames
elif split == 'valid':
src_file, tgt_file = valid_filenames
else:
src_file, tgt_file = test_filenames
extracted_files = [] # list of paths to the extracted files
current_url = []
current_md5 = []
current_filenames = [src_file, tgt_file]
for url, md5 in zip(URL[split], MD5[split]):
if any(f in url for f in current_filenames):
current_url.append(url)
current_md5.append(md5)
for url, md5 in zip(current_url, current_md5):
dataset_tar = download_from_url(url,
path=os.path.join(
root, os.path.basename(url)),
root=root,
hash_value=md5,
hash_type='md5')
extracted_files.extend(extract_archive(dataset_tar))
file_archives = extracted_files
data_filenames = {
split: _construct_filepaths(file_archives, src_file, tgt_file),
}
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))
assert data_filenames[split][
0] is not None, "Internal Error: File not found for reading"
assert data_filenames[split][
1] is not None, "Internal Error: File not found for reading"
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
set_identifier = {
'train': train_set,
'valid': valid_set,
'test': test_set,
}
return RawTextIterableDataset(
"Multi30k", SUPPORTED_DATASETS[task][language_pair[0]][
set_identifier[split]]['NUM_LINES'],
_iter(src_data_iter, tgt_data_iter))
def WMT14(root,
split,
language_pair=('de', 'en'),
train_set='train.tok.clean.bpe.32000',
valid_set='newstest2013.tok.bpe.32000',
test_set='newstest2014.tok.bpe.32000'):
"""WMT14 Dataset
The available datasets include following:
**Language pairs**:
+-----+-----+-----+
| |'en' |'de' |
+-----+-----+-----+
|'en' | | x |
+-----+-----+-----+
|'de' | x | |
+-----+-----+-----+
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
train_set: A string to identify train set.
valid_set: A string to identify validation set.
test_set: A string to identify test set.
Examples:
>>> from torchtext.datasets import WMT14
>>> train_iter, valid_iter, test_iter = WMT14()
>>> src_sentence, tgt_sentence = next(train_iter)
"""
supported_language = ['en', 'de']
supported_train_set = [s for s in NUM_LINES if 'train' in s]
supported_valid_set = [s for s in NUM_LINES if 'test' in s]
supported_test_set = [s for s in NUM_LINES if 'test' in s]
assert (
len(language_pair) == 2
), 'language_pair must contain only 2 elements: src and tgt language respectively'
if language_pair[0] not in supported_language:
raise ValueError(
"Source language '{}' is not supported. Valid options are {}".
format(language_pair[0], supported_language))
if language_pair[1] not in supported_language:
raise ValueError(
"Target language '{}' is not supported. Valid options are {}".
format(language_pair[1], supported_language))
if train_set not in supported_train_set:
raise ValueError(
"'{}' is not a valid train set identifier. valid options are {}".
format(train_set, supported_train_set))
if valid_set not in supported_valid_set:
raise ValueError(
"'{}' is not a valid valid set identifier. valid options are {}".
format(valid_set, supported_valid_set))
if test_set not in supported_test_set:
raise ValueError(
"'{}' is not a valid valid set identifier. valid options are {}".
format(test_set, supported_test_set))
train_filenames = '{}.{}'.format(train_set,
language_pair[0]), '{}.{}'.format(
train_set, language_pair[1])
valid_filenames = '{}.{}'.format(valid_set,
language_pair[0]), '{}.{}'.format(
valid_set, language_pair[1])
test_filenames = '{}.{}'.format(test_set,
language_pair[0]), '{}.{}'.format(
test_set, language_pair[1])
if split == 'train':
src_file, tgt_file = train_filenames
elif split == 'valid':
src_file, tgt_file = valid_filenames
else:
src_file, tgt_file = test_filenames
root = os.path.join(root, 'wmt14')
dataset_tar = download_from_url(URL,
root=root,
hash_value=MD5,
path=os.path.join(root, _PATH),
hash_type='md5')
extracted_files = extract_archive(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 = {
split: _construct_filepaths(file_archives, src_file, tgt_file),
}
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))
assert data_filenames[split][
0] is not None, "Internal Error: File not found for reading"
assert data_filenames[split][
1] is not None, "Internal Error: File not found for reading"
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("WMT14",
NUM_LINES[os.path.splitext(src_file)[0]],
_iter(src_data_iter, tgt_data_iter))