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我正在尝试运行代码以从 fair 和 bert 获得堆叠嵌入，但出现以下错误。建议之一是减少批量大小，但是如何批量传递数据？这是代码和错误。

from tqdm import tqdm ## tracks progress of loop ##
import torch
from flair.data import Sentence
from flair.embeddings import TransformerDocumentEmbeddings

from flair.embeddings import DocumentPoolEmbeddings
bert_embeddings = TransformerDocumentEmbeddings('bert-base-uncased')

### initialize the document embeddings, mode = mean ###
document_embeddings = DocumentPoolEmbeddings([
                                             flair_forward, 
                                             flair_backward,
                                              bert_embeddings 
                                             ])
# Storing Size of embedding #
z = sentence.embedding.size()[0]
print(z)
### Vectorising text ###
# creating a tensor for storing sentence embeddings
sen = torch.zeros(0,z)
print(sen)

# iterating Sentences #
for tweet in tqdm(txt):   
  sentence = Sentence(tweet)
  document_embeddings.embed(sentence)# *****this line is giving error*****
  # Adding Document embeddings to list #
  if(torch.cuda.is_available()):
    sen = sen.cuda()
  sen = torch.cat((sen, sentence.embedding.view(-1,z)),0)

这是我得到的错误。

RuntimeError                              Traceback (most recent call last)
<ipython-input-24-1eee00445350> in <module>()
     24 for tweet in tqdm(txt):
     25   sentence = Sentence(tweet)
---> 26   document_embeddings.embed(sentence)
     27   # Adding Document embeddings to list #
     28   if(torch.cuda.is_available()):

7 frames
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/rnn.py in forward(self, input, hx)
    580         if batch_sizes is None:
    581             result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,
--> 582                               self.dropout, self.training, self.bidirectional, self.batch_first)
    583         else:
    584             result = _VF.lstm(input, batch_sizes, hx, self._flat_weights, self.bias,

RuntimeError: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 7.43 GiB total capacity; 6.54 GiB already allocated; 10.94 MiB free; 6.70 GiB reserved in total by PyTorch)
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