input_ids = tokenizer.encode(input_text, return_tensors='pt')# 生成文本 output = model.generate(input_ids, max_length=50)# 解码生成的文本 decoded_output = tokenizer.decode(output[0])print(decoded_output) 在这个例子中,我们首先加载了GPT-2的分词器和模型。然后,我们将一些输入文本编码成模型可以...
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....
get_tokenizer_config 调用 cached_file, 得到 resolved_config_file 为 tokenizer_config.json 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_...
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, # 返回掩码 return_tensors="pt" # 返回PyTorch张量 ) print(encoded_input) 输出(可能会因模型而异): ...
LlamaTokenizerFast(name_or_path='mistralai/Mistral-7B-Instruct-v0.3', vocab_size=32768, model_max_length=1000000000000000019884624838656, is_fast=True, padding_side='left', truncation_side='right', special_tokens={'bos_token': '', 'eos_token': '', 'unk_token': '<unk>'}, clean_up_tok...
# 使用分词器处理输入文本 inputs = tokenizer(prompt, return_tensors="pt") # 生成文本 generated_text = model.generate(**inputs, max_length=50, num_return_sequences=1) # 解码生成的文本 print(tokenizer.decode(generated_text[0], skip_special_tokens=True)) 这段代码首先导入了必要的类,然后加...
使用django的异步调用场景时，并配置了如下配置：出现了错误：ValueError: Database is int between 0 and limit - 1, not :6379/0 tf
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,
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``. ...
max_seq_length = max_seq_length config = AutoConfig.from_pretrained(model_name_or_path, **model_args, cache_dir=cache_dir) self.auto_model = AutoModel.from_pretrained(model_name_or_path, config=config, cache_dir=cache_dir) self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_...