2.tokenizer.add_tokens() 3.tokenizer.add_special_tokens() model.resize_token_embeddings() 实现疑问 实现代码 使用场景 在Transformer模型输入的文本中常常会额外使用一些特殊[token]来表示一些特殊含义,比如希望对LLM通过设计prompt提升下游任务效果。 最开始在Bert预训练文本中就约定俗成用[CLS]表示句子开头、[SE...
❓ Questions & Help Details When I read the code of tokenizer, I have a problem if I want to use a pretrained model in NMT task, I need to add some tag tokens, such as '2English' or '2French'. I think these tokens are special tokens, so w...
special_tokens_dict = { "additional_special_tokens": ['[ABC]', '[DEF]', '[GHI]'], } num_added_toks = tokenizer.add_special_tokens(special_tokens_dict) model.resize_token_embeddings(len(tokenizer)) unk_tok_emb = model.transformer.wte.weight.data[tokenizer.unk_token_id, :] for i i...
At the next step, we need toprepare the set of new tokensand check if they are already in the vocabulary of our tokenizer. We have access to the vocabulary mapping of the tokenizer withtokenizer.vocab. This is a dictionary with tokens as keys and indices as values. So we do it like t...
{}, 'spaces_between_special_tokens': False, 'add_special_tokens': <bound method SpecialTokensMixin.add_special_tokens of LlamaTokenizerFast(name_or_path='mistralai/Mistral-7B-v0.1', vocab_size=32000, model_max_length=1000000000000000019884624838656, is_fast=True, padding_side='left', truncation...
huggingface / tokenizers Public Notifications Fork 816 Star 9.2k New issue Jump to bottom ByteLevelBPETokenizer ignores enable_padding, add_tokens, add_special tokens. Always same vocab. #133 Closed Tenoke opened this issue Feb 8, 2020· 18 comments Closed ByteLevelBPETokenizer ignores...
Feature request Today, when you add new tokens to the vocabulary (e.g. <|im_start|> and <|im_end|>), you need to also add embed_tokens and lm_head to the modules_to_save kwarg. This, as far as I can tell, unfreezes all token embeddings. ...
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from_pretrained("huggyllama/llama-7b", add_eos_token=True, use_fast=True) print(auto_tokenizer.decode(auto_tokenizer.encode("auto_tokenizer", add_special_tokens = True))) print(llama_tokenizer.decode(llama_tokenizer.encode("llama_tokenizer", add_special_tokens = True)))...
self.fairseq_tokens_to_ids["<mask>"] = len(self.sp_model) - 1 self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()} def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[in...