def test_full_tokenizer_no_lower(self):
tokenizer = TransfoXLTokenizer(lower_case=False)
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo ! how \n Are yoU ? "),
["HeLLo", "!", "how", "Are", "yoU", "?"],
)
def test_full_tokenizer_lower(self):
tokenizer = TransfoXLTokenizer(lower_case=True)
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo ! how \n Are yoU ? "),
["hello", "!", "how", "are", "you", "?"],
)
def test_full_tokenizer(self):
tokenizer = TransfoXLTokenizer(vocab_file=self.vocab_file,
lower_case=True)
tokens = tokenizer.tokenize(u"<unk> UNwanted , running")
self.assertListEqual(tokens, ["<unk>", "unwanted", ",", "running"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens),
[0, 4, 8, 7])
def test_full_tokenizer(self):
vocab_tokens = [
"<unk>", "[CLS]", "[SEP]", "want", "unwanted", "wa", "un",
"running", ",", "low", "l",
]
with TemporaryDirectory() as tmpdirname:
vocab_file = os.path.join(tmpdirname, VOCAB_FILES_NAMES['vocab_file'])
with open(vocab_file, "w", encoding='utf-8') as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
input_text = u"<unk> UNwanted , running"
output_text = u"<unk> unwanted, running"
create_and_check_tokenizer_commons(self, input_text, output_text, TransfoXLTokenizer, tmpdirname, lower_case=True)
tokenizer = TransfoXLTokenizer(vocab_file=vocab_file, lower_case=True)
tokens = tokenizer.tokenize(u"<unk> UNwanted , running")
self.assertListEqual(tokens, ["<unk>", "unwanted", ",", "running"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(tokens), [0, 4, 8, 7])
def transformerXLTokenizer(*args, **kwargs):
"""
Instantiate a Transformer-XL tokenizer adapted from Vocab class in https://github.com/kimiyoung/transformer-xl
Args:
pretrained_model_name_or_path: Path to pretrained model archive
or one of pre-trained vocab configs below.
* transfo-xl-wt103
Example:
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'transformerXLTokenizer', 'transfo-xl-wt103')
>>> text = "Who was Jim Henson ?"
>>> tokenized_text = tokenizer.tokenize(tokenized_text)
>>> indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
"""
tokenizer = TransfoXLTokenizer.from_pretrained(*args, **kwargs)
return tokenizer
def get_tokenizer(self, **kwargs):
kwargs['lower_case'] = True
return TransfoXLTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_tokenizer(self):
return TransfoXLTokenizer.from_pretrained(self.tmpdirname,
lower_case=True)