title: Fine-Tuning DistilBertForSequenceClassification: Is not learning, why is loss not changing? Weights not updated?
body: <p>I am relatively new to PyTorch and Huggingface-transformers and experimented with DistillBertForSequenceClassification on this <a href="https://www.kaggle.com/c/nlp-getting-started" rel="nofollow noreferrer">Kaggle-Dataset</a>.</p>
  <pre><code>from transformers import DistilBertForSequenceClassification
  import torch.optim as optim
  import torch.nn as nn
  from transformers import get_linear_schedule_with_warmup
  
  n_epochs = 5 # or whatever
  batch_size = 32 # or whatever
  
  bert_distil = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
  #bert_distil.classifier = nn.Sequential(nn.Linear(in_features=768, out_features=1), nn.Sigmoid())
  tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
  criterion = nn.CrossEntropyLoss()
  optimizer = optim.Adam(bert_distil.parameters(), lr=0.1)
  
  X_train = []
  Y_train = []
  
  for row in train_df.iterrows():
      seq = tokenizer.encode(preprocess_text(row[1]['text']),  add_special_tokens=True, pad_to_max_length=True)
      X_train.append(torch.tensor(seq).unsqueeze(0))
      Y_train.append(torch.tensor([row[1]['target']]).unsqueeze(0))
  X_train = torch.cat(X_train)
  Y_train = torch.cat(Y_train)
  
  running_loss = 0.0
  bert_distil.cuda()
  bert_distil.train(True)
  for epoch in range(n_epochs):
      permutation = torch.randperm(len(X_train))
      j = 0
      for i in range(0,len(X_train), batch_size):
          optimizer.zero_grad()
          indices = permutation[i:i+batch_size]
          batch_x, batch_y = X_train[indices], Y_train[indices]
          batch_x.cuda()
          batch_y.cuda()
          outputs = bert_distil.forward(batch_x.cuda())
          loss = criterion(outputs[0],batch_y.squeeze().cuda())
          loss.requires_grad = True
     
          loss.backward()
          optimizer.step()
     
          running_loss += loss.item()  
          j+=1
          if j == 20:   
              #print(outputs[0])
              print('[%d, %5d] running loss: %.3f loss: %.3f ' %
                (epoch + 1, i*1, running_loss / 20, loss.item()))
              running_loss = 0.0
              j = 0
  </code></pre>
  <blockquote>
  <p>[1,   608] running loss: 0.689 loss: 0.687
  [1,  1248] running loss: 0.693 loss: 0.694
  [1,  1888] running loss: 0.693 loss: 0.683
  [1,  2528] running loss: 0.689 loss: 0.701
  [1,  3168] running loss: 0.690 loss: 0.684
  [1,  3808] running loss: 0.689 loss: 0.688
  [1,  4448] running loss: 0.689 loss: 0.692  etc...</p>
  </blockquote>
  <p>Regardless on what I tried, loss did never decrease, or even increase, nor did the prediction get better. It seems to me that I forgot something so that weights are actually not updated. Someone has an idea?
  O</p>
  <p><strong>what I tried</strong></p>
  <ul>
  <li>Different loss functions
  <ul>
  <li>BCE</li>
  <li>CrossEntropy</li>
  <li>even MSE-loss</li>
  </ul>
  </li>
  <li>One-Hot Encoding vs A single neuron output</li>
  <li>Different learning rates, and optimizers</li>
  <li>I even changed all the targets to only one single label, but even then, the network did'nt converge.</li>
  </ul>