def test_sentence_piece_tokenizer_callable():
vocab = text.SentencePieceVocab.from_file([VOCAB_FILE], 5000, 0.9995,
SentencePieceModel.UNIGRAM, {})
tokenizer = text.SentencePieceTokenizer(
vocab, out_type=SPieceTokenizerOutType.STRING)
data = '123'
assert np.array_equal(tokenizer(data), ['▁', '12', '3'])
def test_with_zip_concat():
data = ds.TextFileDataset(VOCAB_FILE, shuffle=False)
vocab = text.SentencePieceVocab.from_dataset(data, [""], 5000, 0.9995, SentencePieceModel.UNIGRAM, {})
tokenizer = text.SentencePieceTokenizer(vocab, out_type=SPieceTokenizerOutType.STRING)
dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
dataset = dataset.map(operations=tokenizer, num_parallel_workers=2)
zip_test(dataset)
concat_test(dataset)
def test_from_vocab_to_int():
vocab = text.SentencePieceVocab.from_file([VOCAB_FILE], 5000, 0.9995, SentencePieceModel.UNIGRAM, {})
tokenizer = text.SentencePieceTokenizer(vocab, out_type=SPieceTokenizerOutType.INT)
dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
dataset = dataset.map(operations=tokenizer)
expect = [6, 329, 183, 8, 945, 23, 8, 3783, 4382, 4641, 1405, 4]
for i in dataset.create_dict_iterator():
ret = i["text"]
for key, value in enumerate(ret):
assert value == expect[key]
def test_from_vocab_to_str_WORD():
vocab = text.SentencePieceVocab.from_file([VOCAB_FILE], 5000, 0.9995, SentencePieceModel.WORD, {})
tokenizer = text.SentencePieceTokenizer(vocab, out_type=SPieceTokenizerOutType.STRING)
dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
dataset = dataset.map(operations=tokenizer)
expect = ['▁I', '▁saw', '▁a', '▁girl', '▁with', '▁a', '▁telescope.']
for i in dataset.create_dict_iterator():
ret = to_str(i["text"])
for key, value in enumerate(ret):
assert value == expect[key]
def test_from_file_to_str():
vocab = text.SentencePieceVocab.from_file([VOCAB_FILE], 5000, 0.9995, SentencePieceModel.UNIGRAM, {})
text.SentencePieceVocab.save_model(vocab, "./", "m.model")
tokenizer = text.SentencePieceTokenizer("./m.model", out_type=SPieceTokenizerOutType.STRING)
dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
dataset = dataset.map(operations=tokenizer)
expect = ['▁I', '▁sa', 'w', '▁a', '▁girl', '▁with', '▁a', '▁te', 'les', 'co', 'pe', '.']
for i in dataset.create_dict_iterator():
ret = to_str(i["text"])
for key, value in enumerate(ret):
assert value == expect[key]
def test_from_vocab_to_str_CHAR():
vocab = text.SentencePieceVocab.from_file([VOCAB_FILE], 5000, 0.9995, SentencePieceModel.CHAR, {})
tokenizer = text.SentencePieceTokenizer(vocab, out_type=SPieceTokenizerOutType.STRING)
dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
dataset = dataset.map(operations=tokenizer)
expect = ['▁', 'I', '▁', 's', 'a', 'w', '▁', 'a', '▁', 'g', 'i', 'r', 'l', '▁', 'w', 'i', 't', 'h',\
'▁', 'a', '▁', 't', 'e', 'l', 'e', 's', 'c', 'o', 'p', 'e', '.']
for i in dataset.create_dict_iterator():
ret = to_str(i["text"])
for key, value in enumerate(ret):
assert value == expect[key]
def test_build_from_dataset():
data = ds.TextFileDataset(VOCAB_FILE, shuffle=False)
vocab = text.SentencePieceVocab.from_dataset(data, ["text"], 5000, 0.9995,
SentencePieceModel.UNIGRAM,
{})
tokenizer = text.SentencePieceTokenizer(
vocab, out_type=SPieceTokenizerOutType.STRING)
dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
dataset = dataset.map(operations=tokenizer)
expect = [
'▁I', '▁sa', 'w', '▁a', '▁girl', '▁with', '▁a', '▁te', 'les', 'co',
'pe', '.'
]
for i in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
ret = to_str(i["text"])
for key, value in enumerate(ret):
assert value == expect[key]
