I am training my model on multi-class task using CrossEntropyLoss
but I’m getting the following error:
ValueError: Classification metrics can't handle a mix of multiclass and continuous-multioutput targets

here is my training loop:
    for i, batch in enumerate(iterator):
        text = batch['sequence']
        features = batch['features']
        label = batch['target']
        optimizer.zero_grad()
        output = model(text, features)

        loss = criterion(output, label)
        ...


info about output:
tensor([[ 0.0436, -0.0301, -0.3313,  0.0675, -0.0690, -0.0067,  0.1439,  0.1636,
          0.0143, -0.1089,  0.0506,  0.0580, -0.0693,  0.0765, -0.1481,  0.0110,
          0.0162, -0.0422,  0.1236, -0.0883, -0.0513, -0.0020, -0.0276, -0.1482],
        [ 0.0752, -0.0756, -0.1759, -0.0140, -0.0878,  0.1035,  0.1359, -0.0080,
         -0.0461, -0.1518, -0.0269, -0.0483, -0.0990, -0.0592, -0.1666, -0.0031,
          0.0484,  0.0079,  0.1189, -0.0485, -0.1961, -0.0044, -0.0004, -0.1201],
        [ 0.1314,  0.1292, -0.3979, -0.0088,  0.0360, -0.0198,  0.0983,  0.0571,
         -0.0215, -0.1564, -0.1584, -0.0038,  0.0310, -0.0390, -0.2015, -0.1018,
          0.1232, -0.0587,  0.1216, -0.0834, -0.1158, -0.0572, -0.2376, -0.1555],
        [ 0.0392, -0.0082, -0.0542,  0.0607,  0.0393,  0.0213,  0.0775,  0.0141,
         -0.0202, -0.0561,  0.0173,  0.0134, -0.0254,  0.0482, -0.0222,  0.0305,
          0.0254,  0.0485,  0.0633, -0.0564,  0.0158, -0.0278, -0.0495, -0.0834]],
       grad_fn=<AddmmBackward>)
shape: torch.Size([4, 24])

info about label:
tensor([18,  4, 19, 11])
shape: torch.Size([4])

and here is my model:
class RCNNwithFeatures(nn.Module):
    def __init__(self, vocab_size, feature_size, embedding_length, hidden_size, output_size, n_layers):
        super().__init__()
        self.output_size = output_size
        self.hidden_size = hidden_size
        self.vocab_size = vocab_size
        self.embedding_length = embedding_length

        self.Embedding = nn.Embedding(vocab_size, embedding_length)  # Initializing the look-up table.
        self.dropout = 0.5
        self.lstm = nn.LSTM(embedding_length, hidden_size, n_layers, dropout=self.dropout, bidirectional=True)
        self.fc = nn.Linear(2 * hidden_size + embedding_length, hidden_size*2)

        self.fc2 = nn.Linear(feature_size, hidden_size)
        self.fc3 = nn.Linear(hidden_size, hidden_size*2)
        self.label = nn.Linear(hidden_size*4, output_size)


    def forward(self, input_sentence, features):

        inputs = self.Embedding(input_sentence)
        inputs = inputs.permute(1, 0, 2)

        final_encoding = torch.cat((output, inputs), 2).permute(1, 0, 2)
        x = self.fc(final_encoding)
        x = x.permute(0, 2, 1)
        x = F.max_pool1d(x, x.size()[2])
        x = x.squeeze(2)


        x2 = F.relu(self.fc2(features))
        x2 = F.relu(self.fc3(x2))

        y = torch.cat([x, x2], dim=1)
        logits = self.label(y)
        return logits


notice that output is (batchsize, C) and that what the loss want
any advice will be I thankful