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@ -309,7 +309,7 @@ def _compute_pytorch(model_names, dictionary, average_over, device, torchscript)
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model = AutoModel.from_pretrained(model_name, config=config)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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tokenized_sequence = tokenizer.encode(input_text)
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tokenized_sequence = tokenizer.encode(input_text, add_special_tokens=False)
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max_input_size = tokenizer.max_model_input_sizes[model_name]
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batch_sizes = [1, 2, 4, 8]
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@ -353,7 +353,7 @@ def _compute_tensorflow(model_names, dictionary, average_over):
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model = TFAutoModel.from_pretrained(model_name, config=config)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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tokenized_sequence = tokenizer.encode(input_text)
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tokenized_sequence = tokenizer.encode(input_text, add_special_tokens=False)
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max_input_size = tokenizer.max_model_input_sizes[model_name]
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batch_sizes = [1, 2, 4, 8]
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@ -68,7 +68,7 @@ def main():
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start = time.time()
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for text in data:
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text = f'{bos} {text.strip()} {sep}'
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token_ids = tokenizer.encode(text)
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token_ids = tokenizer.encode(text, add_special_tokens=False)
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rslt.append(token_ids)
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iter += 1
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@ -223,7 +223,7 @@ def main():
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if args.model_type in ["transfo-xl", "xlnet"]:
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# Models with memory likes to have a long prompt for short inputs.
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raw_text = (args.padding_text if args.padding_text else PADDING_TEXT) + raw_text
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context_tokens = tokenizer.encode(raw_text)
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context_tokens = tokenizer.encode(raw_text, add_special_tokens=False)
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out = sample_sequence(
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model=model,
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context=context_tokens,
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@ -128,8 +128,8 @@ class BertTokenizationTest(CommonTestCases.CommonTokenizerTester):
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def test_sequence_builders(self):
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tokenizer = self.tokenizer_class.from_pretrained("bert-base-uncased")
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text = tokenizer.encode("sequence builders")
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text_2 = tokenizer.encode("multi-sequence build")
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text = tokenizer.encode("sequence builders", add_special_tokens=False)
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text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False)
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encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
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encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
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@ -33,8 +33,8 @@ class DistilBertTokenizationTest(BertTokenizationTest):
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def test_sequence_builders(self):
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tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
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text = tokenizer.encode("sequence builders")
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text_2 = tokenizer.encode("multi-sequence build")
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text = tokenizer.encode("sequence builders", add_special_tokens=False)
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text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False)
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encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
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encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
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@ -70,19 +70,19 @@ class RobertaTokenizationTest(CommonTestCases.CommonTokenizerTester):
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tokenizer = self.get_tokenizer()
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self.assertListEqual(
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tokenizer.encode('Hello world!'),
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tokenizer.encode('Hello world!', add_special_tokens=False),
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[0, 31414, 232, 328, 2]
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)
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self.assertListEqual(
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tokenizer.encode('Hello world! cécé herlolip 418'),
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tokenizer.encode('Hello world! cécé herlolip 418', add_special_tokens=False),
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[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]
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)
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def test_sequence_builders(self):
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tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
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text = tokenizer.encode("sequence builders")
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text_2 = tokenizer.encode("multi-sequence build")
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text = tokenizer.encode("sequence builders", add_special_tokens=False)
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text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False)
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encoded_text_from_decode = tokenizer.encode("sequence builders", add_special_tokens=True)
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encoded_pair_from_decode = tokenizer.encode("sequence builders", "multi-sequence build", add_special_tokens=True)
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@ -79,13 +79,13 @@ class CommonTestCases:
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# Now let's start the test
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tokenizer = self.get_tokenizer(max_len=42)
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before_tokens = tokenizer.encode(u"He is very happy, UNwant\u00E9d,running")
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before_tokens = tokenizer.encode(u"He is very happy, UNwant\u00E9d,running", add_special_tokens=False)
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with TemporaryDirectory() as tmpdirname:
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tokenizer.save_pretrained(tmpdirname)
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tokenizer = self.tokenizer_class.from_pretrained(tmpdirname)
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after_tokens = tokenizer.encode(u"He is very happy, UNwant\u00E9d,running")
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after_tokens = tokenizer.encode(u"He is very happy, UNwant\u00E9d,running", add_special_tokens=False)
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self.assertListEqual(before_tokens, after_tokens)
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self.assertEqual(tokenizer.max_len, 42)
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@ -130,7 +130,7 @@ class CommonTestCases:
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self.assertEqual(added_toks, len(new_toks))
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self.assertEqual(all_size_2, all_size + len(new_toks))
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tokens = tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l")
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tokens = tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l", add_special_tokens=False)
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out_string = tokenizer.decode(tokens)
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self.assertGreaterEqual(len(tokens), 4)
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@ -148,7 +148,8 @@ class CommonTestCases:
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self.assertEqual(added_toks_2, len(new_toks_2))
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self.assertEqual(all_size_3, all_size_2 + len(new_toks_2))
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tokens = tokenizer.encode(">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l")
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tokens = tokenizer.encode(">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l",
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add_special_tokens=False)
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out_string = tokenizer.decode(tokens)
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self.assertGreaterEqual(len(tokens), 6)
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@ -166,7 +167,7 @@ class CommonTestCases:
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tokens = tokenizer.tokenize(input_text)
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ids = tokenizer.convert_tokens_to_ids(tokens)
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ids_2 = tokenizer.encode(input_text)
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ids_2 = tokenizer.encode(input_text, add_special_tokens=False)
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self.assertListEqual(ids, ids_2)
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tokens_2 = tokenizer.convert_ids_to_tokens(ids)
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@ -206,7 +207,7 @@ class CommonTestCases:
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seq_0 = "Test this method."
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seq_1 = "With these inputs."
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sequences = tokenizer.encode(seq_0, seq_1)
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sequences = tokenizer.encode(seq_0, seq_1, add_special_tokens=False)
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attached_sequences = tokenizer.encode(seq_0, seq_1, add_special_tokens=True)
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# Method is implemented (e.g. not GPT-2)
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@ -219,7 +220,7 @@ class CommonTestCases:
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seq_0 = "This is a sentence to be encoded."
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stride = 2
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sequence = tokenizer.encode(seq_0)
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sequence = tokenizer.encode(seq_0, add_special_tokens=False)
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num_added_tokens = tokenizer.num_added_tokens()
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total_length = len(sequence) + num_added_tokens
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information = tokenizer.encode_plus(seq_0, max_length=total_length - 2, add_special_tokens=True, stride=stride)
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@ -239,13 +240,13 @@ class CommonTestCases:
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seq_1 = "This is another sentence to be encoded."
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stride = 2
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sequence_0_no_special_tokens = tokenizer.encode(seq_0)
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sequence_1_no_special_tokens = tokenizer.encode(seq_1)
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sequence_0_no_special_tokens = tokenizer.encode(seq_0, add_special_tokens=False)
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sequence_1_no_special_tokens = tokenizer.encode(seq_1, add_special_tokens=False)
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sequence = tokenizer.encode(seq_0, seq_1, add_special_tokens=True)
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truncated_second_sequence = tokenizer.build_inputs_with_special_tokens(
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tokenizer.encode(seq_0),
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tokenizer.encode(seq_1)[:-2]
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tokenizer.encode(seq_0, add_special_tokens=False),
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tokenizer.encode(seq_1, add_special_tokens=False)[:-2]
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)
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information = tokenizer.encode_plus(seq_0, seq_1, max_length=len(sequence) - 2, add_special_tokens=True,
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@ -283,7 +284,7 @@ class CommonTestCases:
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sequence_1 = "This one too please."
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# Testing single inputs
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encoded_sequence = tokenizer.encode(sequence_0)
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encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False)
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encoded_sequence_dict = tokenizer.encode_plus(sequence_0, add_special_tokens=True)
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encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
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special_tokens_mask = encoded_sequence_dict["special_tokens_mask"]
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@ -294,7 +295,8 @@ class CommonTestCases:
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self.assertEqual(encoded_sequence, filtered_sequence)
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# Testing inputs pairs
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encoded_sequence = tokenizer.encode(sequence_0) + tokenizer.encode(sequence_1)
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encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False) + tokenizer.encode(sequence_1,
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add_special_tokens=False)
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encoded_sequence_dict = tokenizer.encode_plus(sequence_0, sequence_1, add_special_tokens=True)
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encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
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special_tokens_mask = encoded_sequence_dict["special_tokens_mask"]
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@ -69,8 +69,8 @@ class XLMTokenizationTest(CommonTestCases.CommonTokenizerTester):
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def test_sequence_builders(self):
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tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-en-2048")
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text = tokenizer.encode("sequence builders")
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text_2 = tokenizer.encode("multi-sequence build")
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text = tokenizer.encode("sequence builders", add_special_tokens=False)
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text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False)
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encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
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encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
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@ -92,8 +92,8 @@ class XLNetTokenizationTest(CommonTestCases.CommonTokenizerTester):
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def test_sequence_builders(self):
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tokenizer = XLNetTokenizer.from_pretrained("xlnet-base-cased")
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text = tokenizer.encode("sequence builders")
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text_2 = tokenizer.encode("multi-sequence build")
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text = tokenizer.encode("sequence builders", add_special_tokens=False)
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text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False)
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encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
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encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
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@ -689,14 +689,14 @@ class PreTrainedTokenizer(object):
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raise NotImplementedError
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def encode(self,
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text,
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text_pair=None,
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add_special_tokens=False,
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max_length=None,
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stride=0,
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truncation_strategy='longest_first',
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return_tensors=None,
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**kwargs):
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text,
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text_pair=None,
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add_special_tokens=True,
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max_length=None,
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stride=0,
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truncation_strategy='longest_first',
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return_tensors=None,
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**kwargs):
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"""
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Converts a string in a sequence of ids (integer), using the tokenizer and vocabulary.
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@ -739,7 +739,7 @@ class PreTrainedTokenizer(object):
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def encode_plus(self,
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text,
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text_pair=None,
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add_special_tokens=False,
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add_special_tokens=True,
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max_length=None,
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stride=0,
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truncation_strategy='longest_first',
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@ -794,7 +794,7 @@ class PreTrainedTokenizer(object):
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truncation_strategy=truncation_strategy,
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return_tensors=return_tensors)
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def prepare_for_model(self, ids, pair_ids=None, max_length=None, add_special_tokens=False, stride=0,
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def prepare_for_model(self, ids, pair_ids=None, max_length=None, add_special_tokens=True, stride=0,
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truncation_strategy='longest_first', return_tensors=None):
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"""
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Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model.
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