想得到一段文本每个token的输出概率,本人原代码如下: output = model(input_ids=torch.tensor(input_ids), attention_mask=torch.tensor(input_mask), output_attentions=True) logits = output.logits attentions = output.attentions prob = torch.softmax(logits, dim=-1) token_prob = torch.tensor([prob[i...
candidate_input_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, use_cache=True, ) 在这里,把原始的token和辅助模型(在huggingface的代码里面,叫辅助模型,但是和上面的小模型是一回事,叫法不一样)生成的token绑定在一起,然后放入原始模型做推理。 在下面的代码块中,把大模型...
output_attentions (`bool`, *optional*, defaults to `False`): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more details. output_hidden_states (`bool`, *optional*, defaults to `False`): Whether or not to return the ...
output_attentions:bool=False, output_hidden_states:bool=False,) ->Tuple[torch.Tensor, torch.Tensor]:# 使用注意力机制模块处理隐藏状态attention_output = self.attention(hidden_states, attention_mask, head_mask, output_attentions)# 应用分块机制到前馈网络ffn_output = apply_chunking_to_forward(self.ff...
hidden_states(tuple(torch.FloatTensor),可选,当传递output_hidden_states=True或config.output_hidden_states=True时返回)- 形状为(batch_size, sequence_length, hidden_size)的torch.FloatTensor元组(一个用于嵌入的输出 + 一个用于每一层的输出)。 模型在每一层输出的隐藏状态加上初始嵌入输出。 attentions(tup...
transformers.modeling_outputs.CausalLMOutputWithCrossAttentions 或tuple(torch.FloatTensor) 一个transformers.modeling_outputs.CausalLMOutputWithCrossAttentions 或一个torch.FloatTensor元组(如果传递了return_dict=False或config.return_dict=False,或者当config.return_dict=False时)包含根据配置(Pix2StructConfig)和输入的...
Optional = None return_loss: Optional = None token_type_ids: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = None ) → export const metadata = 'undefined';transformers.models.clip.modeling_clip.CLIPOutput or tuple(torch.FloatTenso...
output_hidden_states=True, output_attentions=True) input_ids = torch.tensor([tokenizer.encode("Let's see all hidden-states and attentions on this text")]) all_hidden_states, all_attentions = model(input_ids)[-2:] #模型与Torchscript兼容 ...
output = self.model(**inputs) hidden_states = ... loss = loss_fn(outputs, labels) return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ...
output_hidden_states=True, output_attentions=True) input_ids = torch.tensor([tokenizer.encode("Let's see all hidden-states and attentions on this text")]) all_hidden_states, all_attentions = model(input_ids)[-2:] #Models are compatible with Torchscript ...