hidden_states:这是输出的一个可选项,如果输出,需要指定config.output_hidden_states=True,它也是一个元组,它的第一个元素是embedding,其余元素是各层的输出,每个元素的形状是(batch_size, sequence_length, hidden_size) attentions:这也是输出的一个可选项,如果输出,需要指定config.output_attentions=True,它也是一...
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 ...
attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3]) # (batch_size, seq_len_q, all_head_size) attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size)) outputs = (attention_output, attention_probs) if output_attentions else...
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 Torchscriptmodel = model_class.from_pretrained(pretrained_weights, torchscript...
cross_attentions (tuple(torch.FloatTensor), optional, 当传递output_attentions=True或config.output_attentions=True时返回) — torch.FloatTensor元组(每层一个)的形状为(batch_size, num_heads, sequence_length, sequence_length)。解码器的交叉注意力层的注意力权重,在注意力 softmax 之后,用于计算交叉注意力头...
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 ...
decoder_attentions (tuple(torch.FloatTensor), optional, 当传递output_attentions=True或config.output_attentions=True时返回) — 一个元组,包含形状为(batch_size, num_heads, sequence_length, sequence_length)的torch.FloatTensor(每层一个)。 解码器的注意力权重,在注意力 softmax 之后,用于计算自注意力头中...
decoder_attentions(tuple(torch.FloatTensor),可选,当传递output_attentions=True或config.output_attentions=True时返回)— 形状为(batch_size, num_heads, sequence_length, sequence_length)的每个层的torch.FloatTensor元组。 解码器的注意力权重,在注意力 softmax 之后,用于计算自注意力头中的加权平均值。 cross...
hidden_states(可选,当output_hidden_states=True或者config.output_hidden_states=True):每一层和初始embedding层输出的隐含状态 attentions(可选,当output_attentions=True或者config.output_attentions=True):attention softmax后的attention权重,用于在自注意力头中计算权重平均值。
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 ...