我正在尝试运行代码以从flair和bert进行堆叠式嵌入，并且出现以下错误。建议之一是减小批量大小，但是如何分批传递数据？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)