Class weights for bertForSequenceClassification

Beginners
8

@lucasresck as per your suggestion, I did the following

train_losses = []
num_mb_train = len(train_dataloader)

import torch.nn as nn
import numpy as np




total_preds=[]

for epoch in range(num_epochs):
  train_loss = 0


  
  for step, batch in enumerate(train_dataloader):
    
    model.train()
    
    batch = tuple(t.to(device) for t in batch)
    b_input_ids, b_input_mask,b_token_type, b_labels = batch

    outputs = model(b_input_ids, token_type_ids=b_token_type, attention_mask=b_input_mask, labels=b_labels)
    
    criterion = torch.nn.CrossEntropyLoss(weight=weights,reduction='mean')
    loss = criterion(outputs[1], b_labels)
    
    loss.backward()
    torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
    optimizer.step()
    scheduler.step()
        
    train_loss = loss.item()
    optimizer.zero_grad()
    
    train_losses.append(train_loss)

    if (step) % 50 == 0:
      print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' 
             .format(epoch+1, num_epochs, step+1, total_steps, loss.item()))

Am I doing the right way or not?

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