encode升级版,但是一样只能最多对text pair进行分token和转换token ids的操作,在encode的功能的基础上增加了新功能,例如返回attention mask和token type ids以及返回torch或tf的张量等等 encode_plus(text: Union[str, List[str], List[int]], text_pair: Union[str, List[str], List[int], NoneType] = None...
tokens += [self.text_tokenizer["[gMASK]"], self.text_tokenizer["<sop>"]] prefix_mask += [1, 0] if text_pair is not None: text_pair = self.preprocess(text_pair, linebreak, whitespaces) pair_tokens = self.text_tokenizer.encode(text_pair) tokens += pair_tokens prefix_mask += [0...
text_pair (str, List[str], List[List[str]])– The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set is_split_into_words=True (to ...
1__call__(2text,text_pair,add_special_tokens,padding,truncation,3max_length,stride,is_split_into_words,pad_to_multiple_of,4return_tensors,return_token_type_ids,5return_attention_mask,6return_overflowing_tokens,7return_special_tokens_mask,8return_offsets_mapping,9return_length,10verbose,11**k...
text: Union[TextInput, PreTokenizedInput, EncodedInput], text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]]=None, add_special_tokens: bool=True, padding: Union[bool, str, PaddingStrategy]=False, truncation: Union[bool, str, TruncationStrategy]=False, ...
process(encoding, pair=None, add_special_tokens=True):对指定的 encoding 执行后处理。 参数: encoding:单个句子的 encoding,类型为 tokenizer.Encoding。 pair:一对句子的 encoding,类型为 tokenizer.Encoding。 add_special_tokens:一个布尔值,指定是否添加 special token。 BertProcessing 会把[SEP] token 和[CL...
pair="[CLS] $A [SEP] $B:1 [SEP]:1", special_tokens=[ ("[CLS]",1), ("[SEP]",2), ], ) fromtokenizers.trainersimportWordPieceTrainer trainer = WordPieceTrainer( vocab_size=30522, special_tokens=["[UNK]","[CLS]","[SEP]","[PAD]","[MASK]"] ...
text: Union[TextInput, PreTokenizedInput, EncodedInput],text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,add_special_tokens: bool = True,padding: Union[bool, str, PaddingStrategy] = False,truncation: Union[bool, str, TruncationStrategy] = False,max_length: Optional[...
完整报错: File "transformers/tokenization_utils_base.py", line 2520, in __call__ encodings = self._call_one(text=text, text_pair=text_pair, **all_kwargs) File "transformers/tokenization_utils_base.py", line 2606, in _call_one
Byte-Pair Encoding(BPE)是最广泛采用的subword分词器。 训练方法:从字符级的小词表出发,训练产生合并规则以及一个词表 编码方法:将文本切分成字符,再应用训练阶段获得的合并规则 经典模型:GPT, GPT-2, RoBERTa, BART, LLaMA, ChatGLM等 3.1. 训练阶段 在训练环节,目标是给定语料,通过训练算法,生成合并规则和词...