I am working on Sequence to Sequence encoder-decoder model with bidirectional GRU for the task of grammar error detection and correction for Arabic language. I want to calculate the F0.5 score for my model.

This is how my data divided:

train_data, valid_data, test_data = torchtext.legacy.data.TabularDataset.splits(
                            path = '',
                            train = 'train.csv',
                            test = 'test.csv',
                            validation = 'val.csv',
                            format = 'csv',
                            fields = fields)

and this is my Seq2Seq code:

class Seq2Seq(nn.Module):
    def __init__(self, encoder, decoder, src_pad_idx, device):
        super().__init__()
        
        self.encoder = encoder
        self.decoder = decoder
        self.src_pad_idx = src_pad_idx
        self.device = device
        
    def create_mask(self, src):
        mask = (src != self.src_pad_idx).permute(1, 0)
        return mask
        
    def forward(self, src, src_len, trg, teacher_forcing_ratio = 0.5):
        
        #src = [src len, batch size]
        #src_len = [batch size]
        #trg = [trg len, batch size]
        #teacher_forcing_ratio is probability to use teacher forcing
        #e.g. if teacher_forcing_ratio is 0.75 we use teacher forcing 75% of the time
                    
        batch_size = src.shape[1]
        #print(src.type())
        #print(trg.type())
        #print(src)
        trg_len = trg.shape[0]
        trg_vocab_size = self.decoder.output_dim
        
        #tensor to store decoder outputs
        outputs = torch.zeros(trg_len, batch_size, trg_vocab_size).to(self.device)
        
        #encoder_outputs is all hidden states of the input sequence, back and forwards
        #hidden is the final forward and backward hidden states, passed through a linear layer
        encoder_outputs, hidden = self.encoder(src, src_len)
                
        #first input to the decoder is the <sos> tokens
        input = trg[0,:]
        
        mask = self.create_mask(src)

        #mask = [batch size, src len]
                
        for t in range(1, trg_len):
            
            #insert input token embedding, previous hidden state, all encoder hidden states 
            #  and mask
            #receive output tensor (predictions) and new hidden state
            output, hidden, _ = self.decoder(input, hidden, encoder_outputs, mask)
            
            #place predictions in a tensor holding predictions for each token
            outputs[t] = output
            
            #decide if we are going to use teacher forcing or not
            teacher_force = random.random() < teacher_forcing_ratio
            
            #get the highest predicted token from our predictions
            top1 = output.argmax(1) 
            
            #if teacher forcing, use actual next token as next input
            #if not, use predicted token
            input = trg[t] if teacher_force else top1
            
        return outputs

I tried to use sklearn.metrics but I think my output is not suitable for this function