❓ 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...
num_added_toks['mask_token'] ="<mask>"num_new_tokens:int= tokenizer.add_special_tokens(num_added_toks)tokenizer.bos_token =="<bos>"tokenizer.cls_token ==tokenizer.sep_token ==""asserttokenizer.mask_token =="<mask>"msg =assertlen(tokenizer) == original_len + num...
model=AutoModel.from_pretrained(model_type)# new tokensnew_tokens=["new_token"]# check if the tokens are already in the vocabularynew_tokens=set(new_tokens)-set(tokenizer.vocab.keys())# add the tokens to the tokenizer vocabularytokenizer.add_tokens(list(new_tokens))# add new, random embed...
return_special_tokens_mask=True, return_offsets_mapping=True, return_overflowing_tokens=True,# Return multiple chunksmax_length=self.tokenizer.model_max_length, padding=True)#inputs.pop("overflow_to_sample_mapping", None)num_chunks =len(inputs["input_ids"])foriinrange(num_chunks):ifself.f...
I believe that Photoshop really needs the ability to add "variable tokens" into the filename. The tokens are not literal characters, they are - 14492831
add_tokens adds the given tokens on top of the vocabulary. So it allocates ids starting from the end, and expect all previous ids to have been allocated contiguously. add_special_tokens just lets the tokenizer know about special tokens in its vocabulary, adding these if they don't already...
Hello! Thanks for the great model! I have a question: how to add special tokens in Qwen1.5?Could you please give some examples?
(unk_token="<UNK>", byte_fallback = True)) # Special tokens special_tokens = ["<UNK>", "<BOS>", "<EOS>"] # Initial tokens digits = [str(num) for num in range(10)] tokenizer.add_special_tokens(special_tokens) tokenizer.add_tokens(digits) trainer = trainers.BpeTrainer( vocab_...
self._inv_special_tokens[self._vocab[t]] = t _add_special_token('<CLS>') self._cls_id = self._vocab['<CLS>'] self._cls_id = self._vocab.get('<CLS>') _add_special_token('<SEP>') self._sep_id = self._vocab['<SEP>'] self._sep_id = self._vocab.get('<SEP>') _ad...