I am attempting to add the special tokens like so prior to pretraining: fromtransformersimportT5TokenizerFast,T5ForConditionalGenerationMODEL_NAME='google/t5-v1_1-base'special_tokens=["<ORG>","<PERSON>"]tokenizer=T5TokenizerFast.from_pretrained('t5-base')special_tokens_dict={'additional_special_...
from_pretrained( pretrained_model, do_lower_case=True, max_length=512, truncation=True, additional_special_tokens=special_tokens, ) config = T5Config.from_pretrained(pretrained_model) model = T5ForConditionalGeneration.from_pretrained(pretrained_model, config=config) model.resize_token_embeddings(len(...
{'eos_token': '', 'unk_token': '<unk>', 'pad_token': '<pad>', 'additional_special_tokens': ['<extra_id_0>', '<extra_id_1>', '<extra_id_2>', '<extra_id_3>', '<extra_id_4>', '<extra_id_5>', '<extra_id_6>', '<extra_id_7>', '<extra_id_8>', '<extra...
, add_special_tokens=False) # forward of embedding module input_embeddings = token_embedding(model_inputs['input_ids']) # batch_size, seq_len, hidden_size print(input_embeddings.shape) # torch.Size([1, 5, 768]) mha(input_embeddings...
Additional special tokens used by the tokenizer. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["attention_mask"] slow_tokenizer_class = ...
"additional_special_tokens": [ "<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "<extra_id_4>", "<extra_id_5>", "<extra_id_6>", "<extra_id_7>", "<extra_id_8>", "<extra_id_9>", "<extra_id_10>", "<extra_id_11>", ...
vocab_size, config.hidden_size) # token_embedding sample_text = 'time flies like an arrow' model_inputs = tokenizer(sample_text, return_tensors='pt', add_special_tokens=False) # forward of embedding module input_embeddings = token_embedding(model_inputs['input_ids']) # batch_size, seq...
outputs=model.generate(**inputs)answer=tokenizer.decode(outputs[0],skip_special_tokens=True)print(answer) 通过以上介绍,我们可以看到Transformers库不仅提供了丰富的预训练模型,还提供了便捷的数据处理和微调工具,使得研究人员和开发者可以高效地进行自然语言处理任务。无论是初学者还是经验丰富的开发者,都可以通过...
additional_special_tokens=None, sep_token="[SEP]", cls_token="[CLS]", tokenize_chinese_chars=True, strip_accents=None, offset=100, pre_tokenizer=lambda x: jieba.cut(x, HMM=False), **kwargs): self.offset = offset if additional_special_tokens is not None: if not isinst...
else:# At this point pretrained_model_name_or_path is either a directory or a model identifier nameadditional_files_names={"added_tokens_file":ADDED_TOKENS_FILE,"special_tokens_map_file":SPECIAL_TOKENS_MAP_FILE,"tokenizer_config_file":TOKENIZER_CONFIG_FILE,}vocab_files_target={**cls.vocab_...