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...
If set to a number, will limit the total sequence returned so that it has a maximum length. If there are overflowing tokens,those overflowing tokens will be added to the returned dictionary whenreturn_overflowing_tokensisTrue. Defaults toNone. stride(int, optional): Only available for batch in...
(3)利用return_overflowing_tokens和return_special_tokens_mask等参数,获取被截断部分的token信息。 优化分词效果 为了提高分词效果,我们可以根据实际情况调整BertTokenizer的参数设置。例如,通过调整do_lower_case参数来控制是否对输入文本进行小写转换;通过设置strip_accents参数来决定是否去除文本中的重音符号等。 此外,...
return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length...
3.3 return_overflowing_tokens 该参数指定是否返回溢出的tokens(超过max_length的部分)。默认值为False。 3.4 return_special_tokens_mask 该参数指定是否返回特殊token mask([CLS]、[SEP]、[MASK]等)。默认值为False。 四、总结 BERTTokenizer是一个非常强大和灵活的自然语言处理工具,在处理文本序列时,我们可以根据...
( question, long_context, # 128 是模型fine-tuning时设置的长度 stride=128, # 384 是模型支持的最大长度 max_length=384, padding="longest", truncation="only_second", return_overflowing_tokens=True, return_offsets_mapping=True, ) # overflow_to_sample_mapping 和 offset_mapping 模型都用不到,...
return_overflowing_tokens: bool=False, return_special_tokens_mask: bool=False, return_offsets_mapping: bool=False, return_length: bool=False, verbose: bool=True,**kwargs )->BatchEncoding:"""Tokenize and prepare for the model a sequence or a pair of sequences. ...
[bool, NoneType] = 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) -> transformers.tokenization_utils_base.BatchEncoding method of transformers....
overflowing_tokens.extend(pair_ids[-window_len:]) pair_ids = pair_ids[:-1] Here it loops num_tokens_to_remove times to decide how many tokens needs to be truncated for each sequence, which can be calculated without looping. And in case stride is not 0, it seems to return up to...
Several weeks ago, I summit a issue 23001 related to return_overflowing_tokens behavior, which is considered as a specific feature of fast tokenizer, so it's a feature not a bug. Generally, I want to know the differences between slow and fast tokenizer, should be viewed as features, or ...