self._maxlength=maxlength defcall(self,inputs):print(type(inputs))print(inputs)tokenized=tokenizer.batch_encode_plus(inputs,add_special_tokens=True,return_tensors='tf',max_length=self._maxlength,padding='max_length',truncation=True)returntokenized defbuild_classifier_model():text_input=tf.keras....
读取为 json 格式 (tokenizer_config) {"eos_token":"","model_max_length":512,"name_or_path":"xxx","pad_token":"<pad>","separate_vocabs":false,"source_lang":null,"sp_model_kwargs":{},"special_tokens_map_file":null,"target_lang":null,"tokenizer_class":"MarianTokenizer","unk_token...
并进行其他必要的处理text="Hello, I am a transformer model."encoded_input=tokenizer.encode_plus(text,add_special_tokens=True,# 返回特殊tokens(这个下面会讲到)max_length=20,# 生成语句的最大长度padding="max_length",return_attention_mask=True...
model_max_length = model_max_length, padding_side = padding_side, token = token, use_fast = False, ) return check_tokenizer( model = model, tokenizer = tokenizer, model_name = model_name, model_max_length = model_max_length,
近日，PyTorch 社区又添入了「新」工具，包括了更新后的 PyTorch 1.2，torchvision 0.4，torchaudio 0.3 和 torchtext 0.4。每项工具都进行了新的优化与改进，兼容性更强，使用起来也更加便捷。PyTorch 发布了相关文章介绍了每个工具的更新细节，AI 开发者将其整理与编译如下。最近...
max_context_len_to_capture: Maximum context len covered by CUDA graphs. When a sequence has context length larger than this, we fall back to eager mode. skip_tokenizer_init: If true, skip initialization of tokenizer and detokenizer. """ def __init__( @@ -85,6 +87,7 @@ def __ini...
max_len_truncate: int, default = 500 Truncates the length of the tokenized sequence. Because several pretrained models crash when this is > 500, it defaults to 500 add_special_tokens: bool, optional Add the special tokens to the inputs. Default ``True``. ...
def __init__(self, model_name_or_path: str, max_seq_length: int = 128, model_args: Dict = {}, cache_dir: Optional[str] = None ): super(Transformer, self).__init__() self.config_keys = ['max_seq_length'] self.max_seq_length = max_seq_length config = AutoConfig.from_pret...
defcall(self,inputs):print(type(inputs))print(inputs)tokenized=tokenizer.batch_encode_plus(inputs,add_special_tokens=True,return_tensors='tf',max_length=self._maxlength,padding='max_length',truncation=True)returntokenized defbuild_classifier_model():text_input=tf.keras.layers.Input(shape=(),...
defcall(self,inputs):print(type(inputs))print(inputs)tokenized=tokenizer.batch_encode_plus(inputs,add_special_tokens=True,return_tensors='tf',max_length=self._maxlength,padding='max_length',truncation=True)returntokenized defbuild_classifier_model():text_input=tf.keras.layers.Input(shape=(),...