我正在尝试遵循此处的huggingface文档中的示例https://huggingface.co/transformers/model_doc/longformer.html :
import torch
from transformers import LongformerModel, LongformerTokenizer
model = LongformerModel.from_pretrained('allenai/longformer-base-4096')
tokenizer = LongformerTokenizer.from_pretrained('allenai/longformer-base-4096')
SAMPLE_TEXT = ' '.join(['Hello world! '] * 1000) # long input document
input_ids = torch.tensor(tokenizer.encode(SAMPLE_TEXT)).unsqueeze(0) # batch of size 1
# Attention mask values -- 0: no attention, 1: local attention, 2: global attention
attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=input_ids.device) # initialize to local attention
global_attention_mask = torch.zeros(input_ids.shape, dtype=torch.long, device=input_ids.device) # initialize to global attention to be deactivated for all tokens
global_attention_mask[:, [1, 4, 21,]] = 1 # Set global attention to random tokens for the sake of this example
# Usually, set global attention based on the task. For example,
# classification: the <s> token
# QA: question tokens
# LM: potentially on the beginning of sentences and paragraphs
outputs = model(input_ids, attention_mask=attention_mask, global_attention_mask=global_attention_mask, output_hidden_states= True)
sequence_output = outputs[0].last_hidden_state
pooled_output = outputs.pooler_output
我认为这将返回示例文本的文档嵌入。 但是,我遇到了以下错误:
AttributeError: 'Tensor' object has no attribute 'last_hidden_state'
为什么不能调用last_hidden_state?
最佳答案
不通过索引选择:
sequence_output = outputs.last_hidden_state
outputs
是一个具有以下属性的 LongformerBaseModelOutputWithPooling 对象:
print(outputs.keys())
输出:
odict_keys(['last_hidden_state', 'pooler_output', 'hidden_states'])
调用outputs[0]
或outputs.last_hidden_state
都会给你相同的张量,但这个张量没有名为last_hidden_state
的属性>.
关于python - Longformer 获取last_hidden_state,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/66655023/