return_token_type_ids=False) 1. 2. 3. 4. 5. 6. 7. 对于上述代码, 如果自己提前处理好数据: A B C [PAD] [PAD] [PAD]则tokenizer返回的attention_mask为 1 1 1 1 1 1 如果数据是 A B C则tokenizer返回的attention_mask为 1 1 1 0 0 0...
let token_ids = Tensor::stack(&token_ids, 0)?; let attention_mask = Tensor::stack(&attention_mask, 0)?; let token_type_ids = token_ids.zeros_like()?; println!("running inference on batch {:?}", token_ids.shape()); let embeddings = model.forward(&token_ids, &token_type_ids)...
if self._pkv_precision == Type.bf16: # numpy does not support bf16, pretending f16, should change to bf16 past_key_values = tuple( @@ -387,8 +390,13 @@ def forward( inputs["input_ids"] = np.array(input_ids) # Add the attention_mask inputs when needed if "attention_mask" ...
脚本找出mysql中缺少主键的表
hi the texts can not be empty. you can check your data again.
脚本找出mysql中缺少主键的表
11 input_ids = tf.convert_to_tensor([[1]]) * decoded_next_token 12 ---> 13 outputs = model(input_ids, return_dict=True, use_cache=True, past_key_values=past_key_values) 14 past_key_values = outputs.past_key_values 15 /usr/local/lib/python3.10/dist-packages/keras/utils/traceback...
input_ids = example["input_ids"] attention_mask = example["attention_mask"] labels = example["labels"] if ( buffer["input_ids"] and input_ids[0] == self.tokenizer.bos_token_id ): attention_mask[0] = 0 if add_concat_token: input_ids.append(self.concat_token_id) Expand All @@...
tokenizer.bos_token_id ): attention_mask[0] = 0 if add_concat_token: input_ids.append(self.concat_token_id) Expand All @@ -148,13 +155,17 @@ def __iter__(self): input_ids, dtype=self.tokens_dtype ) attention_mask_with_concat = torch.tensor( attention_mask, dtype=self.tokens_...