importsentencepieceasspm# load 训练好的模型sp=spm.SentencePieceProcessor()sp.Load('tokenizer.model')# 将text转化为token和token_idtext="你好,我的小名叫小明"tokens=sp.EncodeAsPieces(text)token_ids=sp.EncodeAsIds(text)print(tokens)# ['▁你', '好', ',', '我的', '小', '名', '叫', '...
if (len(tokenizer.all_special_tokens) > 0): if ("tokenizer_has_special_tokens" in modelInfo): special_tokens_str = ''.join(tokenizer.all_special_tokens) special_tokens_len = [len(x) for x in tokenizer.all_special_tokens] special_tokens_ids = tokenizer.all_special_ids14...
tokenizer.encode(sentence, add_special_tokens=True))#Batch size 1#tensor([ 101, 7592, 1010, 2026, 2365, 2003, 3013, 2075, 1012, 102])input_token2=tokenizer.tokenize(sentence)#['hello', ',', 'my', 'son', 'is', 'cut', '##ing', '.']input_ids_method2 =tokenizer.convert_tokens...
在transformer 不同版本里 fromtransformersimportBertTokenizer,BertModel# modelbert_tokenizer=BertTokenizer.from_pretrained('dataset/scibert_scivocab_cased/')bert_model=BertModel.from_pretrained('dataset/scibert_scivocab_cased/')ADDITIONAL_SPECIAL_TOKENS=["<O:Prot>","</O:Prot>","<S:Bind>","</S:...
(tokenizer.all_special_ids) # --> [100, 102, 0, 101, 103] num_added_toks = tokenizer.add_tokens(['[EOT]']) model.resize_token_embeddings(len(tokenizer)) # --> Embedding(30523, 768) tokenizer.convert_tokens_to_ids('[EOT]') # --> 30522 text_to_encode = '''QUERY: I want ...
return_token_type_ids: Optional[bool]=None, return_attention_mask: Optional[bool]=None, return_overflowing_tokens: bool=False, return_special_tokens_mask: bool=False, return_offsets_mapping: bool=False, return_length: bool=False, verbose: bool=True,**kwargs ...
additional_special_tokens– list of other tokens beside standard special tokens (bos, eos, pad, etc.). For example, sentinel tokens for T5 (<extra_id_0>, <extra_id_1>, etc.) use_fast– whether to use fast HuggingFace tokenizer
additional_special_tokens, self.tokenizer.additional_special_tokens_ids, ): if token.strip("<|>") in LANGUAGES: result.append(token_id) return tuple(result) @property @lru_cache() def all_language_codes(self) -> Tuple[str]: return tuple(self.decode([l]).strip("<|>") for l in ...
"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>", ...
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)def encode_plus(self,text: Union[TextInput, PreTokenizedInput, EncodedInput],text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,add_special_tokens: bool = True,padding: Union[bool, str...