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...
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 ...
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 (:obj:`str`, :obj:`List[str]` or :obj:`List[int]`, `optional`): Optional second sequence to be encoded. This can be a string, a list of strings (tokenized string using the ``tokenize`` method) or a list of integers (tokenized string ids using the ``convert_tokens_to...
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...
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[int]=None, ...
encode_dict = tokenizer.encode_plus(text=tokens_a, text_pair=tokens_b, max_length=20, pad_to_max_length=True, truncation_strategy='only_second', is_pretokenized=True, return_token_type_ids=True, return_attention_mask=True) tokens = " ".join(['[CLS]'] + tokens_a + ['[SEP]'] +...
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_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[int] = None,stride: int = 0,is_split_into_words:...
2.2 Byte-Pair Encoding (BPE) BPE是一种基于频率统计的分词算法,常用于GPT系列模型。它从字符级别开始,通过合并频率最高的字符对,逐步构建子词单元。 fromtokenizersimportTokenizer, models, pre_tokenizers, decoders, trainers tokenizer = Tokenizer(models.BPE()) ...