def test_roundtrip(self):
tokenizer = SentencePieceTokenizer.load(self.tmp_dir.name)
text = 'I saw a girl with a telescope.'
ids = tokenizer.map_text_to_id(text)
tokens = tokenizer.map_text_to_token(text)
self.assertEqual(text, tokenizer.map_id_to_text(ids))
self.assertEqual(text, tokenizer.map_token_to_text(tokens))
def setUp(self):
self.tmp_dir = tempfile.TemporaryDirectory()
self.SAMPLE_VOCAB = maybe_download(
'https://github.com/google/sentencepiece/blob/master/'
'python/test/test_model.model?raw=true', self.tmp_dir.name)
self.tokenizer = SentencePieceTokenizer.load(self.SAMPLE_VOCAB)
self.tokenizer.save(self.tmp_dir.name)
def test_save_load(self):
tokenizer = SentencePieceTokenizer.load(self.tmp_dir.name)
before_tokens = tokenizer.map_text_to_id(
u"He is very happy, UNwant\u00E9d,running")
with tempfile.TemporaryDirectory() as tmpdirname:
tokenizer.save(tmpdirname)
tokenizer = tokenizer.load(tmpdirname)
after_tokens = tokenizer.map_text_to_id(
u"He is very happy, UNwant\u00E9d,running")
self.assertListEqual(before_tokens, after_tokens)
def test_load(self):
tokenizer = SentencePieceTokenizer.load(self.tmp_dir.name)
self.assertEqual(1000, len(tokenizer))
self.assertEqual(0, tokenizer.map_token_to_id('<unk>'))
self.assertEqual(1, tokenizer.map_token_to_id('<s>'))
self.assertEqual(2, tokenizer.map_token_to_id('</s>'))
self.assertEqual('<unk>', tokenizer.map_id_to_token(0))
self.assertEqual('<s>', tokenizer.map_id_to_token(1))
self.assertEqual('</s>', tokenizer.map_id_to_token(2))
for i in range(len(tokenizer)):
token = tokenizer.map_id_to_token(i)
self.assertEqual(i, tokenizer.map_token_to_id(token))
def test_add_tokens(self):
tokenizer = SentencePieceTokenizer.load(self.tmp_dir.name)
vocab_size = tokenizer.vocab_size
all_size = len(tokenizer)
self.assertNotEqual(vocab_size, 0)
self.assertEqual(vocab_size, all_size)
new_toks = ["aaaaabbbbbb", "cccccccccdddddddd"]
added_toks = tokenizer.add_tokens(new_toks)
vocab_size_2 = tokenizer.vocab_size
all_size_2 = len(tokenizer)
self.assertNotEqual(vocab_size_2, 0)
self.assertEqual(vocab_size, vocab_size_2)
self.assertEqual(added_toks, len(new_toks))
self.assertEqual(all_size_2, all_size + len(new_toks))
tokens = tokenizer.map_text_to_id(
"aaaaabbbbbb low cccccccccdddddddd l")
self.assertGreaterEqual(len(tokens), 4)
self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
new_toks_2 = {
'eos_token': ">>>>|||<||<<|<<",
'pad_token': "<<<<<|||>|>>>>|>"
}
added_toks_2 = tokenizer.add_special_tokens(new_toks_2)
vocab_size_3 = tokenizer.vocab_size
all_size_3 = len(tokenizer)
self.assertNotEqual(vocab_size_3, 0)
self.assertEqual(vocab_size, vocab_size_3)
self.assertEqual(added_toks_2, len(new_toks_2))
self.assertEqual(all_size_3, all_size_2 + len(new_toks_2))
tokens = tokenizer.map_text_to_id(
">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd "
"<<<<<|||>|>>>>|> l")
self.assertGreaterEqual(len(tokens), 6)
self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
self.assertGreater(tokens[0], tokens[1])
self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
self.assertGreater(tokens[-2], tokens[-3])
self.assertEqual(tokens[0],
tokenizer.map_token_to_id(tokenizer.eos_token))
self.assertEqual(tokens[-2],
tokenizer.map_token_to_id(tokenizer.pad_token))
def test_train(self):
tmp_dir = tempfile.TemporaryDirectory()
TEXT_FILE = maybe_download(
'https://github.com/google/sentencepiece/blob/master/'
'data/botchan.txt?raw=true', tmp_dir.name)
hparams = {
"vocab_file": None,
"text_file": TEXT_FILE,
"vocab_size": 1000,
}
tokenizer = SentencePieceTokenizer(hparams=hparams)
with open(TEXT_FILE, 'r', encoding='utf-8') as file:
for line in file:
tokenizer.map_token_to_text(tokenizer.map_text_to_token(line))
tokenizer.map_id_to_text(tokenizer.map_text_to_id(line))
def test_pickle(self):
tokenizer = SentencePieceTokenizer.load(self.tmp_dir.name)
self.assertIsNotNone(tokenizer)
text = u"Munich and Berlin are nice cities"
subwords = tokenizer.map_text_to_token(text)
with tempfile.TemporaryDirectory() as tmpdirname:
filename = os.path.join(tmpdirname, u"tokenizer.bin")
with open(filename, "wb") as f:
pickle.dump(tokenizer, f)
with open(filename, "rb") as f:
tokenizer_new = pickle.load(f)
subwords_loaded = tokenizer_new.map_text_to_token(text)
self.assertListEqual(subwords, subwords_loaded)