我正在做一个NLP项目。最初，我逐行将句子输入模型。
我正尝试使用DataLoader一次将多个句子输入模型。我已经在DataSet中实现了此代码：
def __getitem__(self, i, cuda=use_cuda):
# encode the sentence at index i
sentence = self.corpus_original[i]
sentence_lowered = self.corpus_lowered[i]
sentence_tags = self.tags[i]
max_sent_len = self.max_sent_len
# Idea behind this is that we pad our arrays with zeros and create a new tag and word for those padded indices.
words = torch.zeros(max_sent_len, dtype=torch.long)
labels = torch.zeros(max_sent_len, dtype=torch.long)
chars = torch.zeros(max_sent_len, self.max_word_len,
dtype=torch.long)
word_lengths = np.zeros(max_sent_len, dtype=int)
for j in range(len(sentence)):
labels[j] = self.tag2idx[sentence_tags[j]]
words[j] = self.word2idx[sentence_lowered[j]]
word_lengths[j] = len(sentence[j])
for k, c in enumerate(sentence[j]): # for each character
chars[j,k] = self.char2idx[c]
# pad here
# we use -1 to denote padded values
for j in range(len(sentence), max_sent_len):
labels[j] = 45 # idx for <pad_tag>
words[j] = 1
word_lengths[j] = 5 # 5 because length of "<pad>" is 5
chars[j, :] = 94 # 94 proper characters, 0 idx so 94 is our idx for <pad_char>
if cuda:
words = words.cuda()
labels = labels.cuda()
chars = chars.cuda()
return words, labels, chars, word_lengths
然后我通过以下方式创建train_loader：
train_loader = DataLoader(train, batch_size=32, shuffle=False, num_workers=4)
我生成每个批次并尝试致电
preds = model(words, chars, word_lengths)
毫不奇怪，它告诉我
RuntimeError: Expected 3-dimensional input for 3-dimensional weight [10, 16, 5], but got input of size [16, 54, 141, 16] instead
我的问题是我应该如何更改模型代码，以使其可以接受一批16个句子而不是一个句子？
谢谢。