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Fine-tuning BERT model for Sentiment Analysis

Last Updated : 02 Mar, 2022
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Google created a transformer-based machine learning approach for natural language processing pre-training called Bidirectional Encoder Representations from Transformers. It has a huge number of parameters, hence training it on a small dataset would lead to overfitting. This is why we use a pre-trained BERT model that has been trained on a huge dataset. Using the pre-trained model and try to “tune” it for the current dataset, i.e. transferring the learning, from that huge dataset to our dataset, so that we can “tune” BERT from that point onwards.

In this article, we will fine-tune the BERT by adding a few neural network layers on our own and freezing the actual layers of BERT architecture. The problem statement that we are taking here would be of classifying sentences into POSITIVE and NEGATIVE by using fine-tuned BERT model. 

Preparing the dataset

Link for the dataset. 

The sentence column has text and the label column has the sentiment of the text – 0 for negative and 1 for positive. We first load the dataset followed by, some preprocessing before tuning the model.

Loading dataset


import pandas as pd
import numpy as np
df = pd.read_csv('/content/data.csv')

Split dataset: 

After loading the data, split the data into train, validation ad test data. We are taking the 70:15:15 ratio for this division. The inbuilt function of sklearn is being used below to split the data. We use stratified attributes to ensure that the proportion of the categories remains the same after splitting the data. 


from sklearn.model_selection import train_test_split
train_text, temp_text, train_labels, temp_labels = train_test_split(df['sentence'], df['label'], 
                                                                    random_state = 2021
                                                                    test_size = 0.3
                                                                    stratify = df['label'])
val_text, test_text, val_labels, test_labels = train_test_split(temp_text, temp_labels, 
                                                                random_state = 2021
                                                                test_size = 0.5
                                                                stratify = temp_labels)

Load pre-trained BERT model and tokenizer

Next, we proceed with loading the pre-trained BERT model and tokenizer. We would use the tokenizer to convert the text into a format(which has input ids, attention masks) that can be sent to the model.


#load model and tokenizer
bert = AutoModel.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')

Deciding the padding length

If we take the padding length as the maximum length of text found in the training texts, it might leave the training data sparse. Taking the least length would in turn lead to loss of information. Hence, we would plot the graph and see the “average” length and set it as the padding length to trade-off between the two extremes.


train_lens = [len(i.split()) for i in train_text]

From the graph above, we take 17 as the padding length.

Tokenizing the data

Tokenize the data and encode sequences using the BERT tokenizer. 


# tokenize and encode sequences 
tokens_train = tokenizer.batch_encode_plus(
    max_length = pad_len,
    pad_to_max_length = True,
    truncation = True
tokens_val = tokenizer.batch_encode_plus(
    max_length = pad_len,
    pad_to_max_length = True,
    truncation = True
tokens_test = tokenizer.batch_encode_plus(
    max_length = pad_len,
    pad_to_max_length = True,
    truncation = True
train_seq = torch.tensor(tokens_train['input_ids'])
train_mask = torch.tensor(tokens_train['attention_mask'])
train_y = torch.tensor(train_labels.tolist())
val_seq = torch.tensor(tokens_val['input_ids'])
val_mask = torch.tensor(tokens_val['attention_mask'])
val_y = torch.tensor(val_labels.tolist())
test_seq = torch.tensor(tokens_test['input_ids'])
test_mask = torch.tensor(tokens_test['attention_mask'])
test_y = torch.tensor(test_labels.tolist())

Defining the model

We first freeze the BERT pre-trained model, and then add layers as shown in the following code snippets:


#freeze the pretrained layers
for param in bert.parameters():
    param.requires_grad = False
#defining new layers
class BERT_architecture(nn.Module):
    def __init__(self, bert):
      super(BERT_architecture, self).__init__()
      self.bert = bert 
      # dropout layer
      self.dropout = nn.Dropout(0.2)
      # relu activation function
      self.relu =  nn.ReLU()
      # dense layer 1
      self.fc1 = nn.Linear(768,512)
      # dense layer 2 (Output layer)
      self.fc2 = nn.Linear(512,2)
      #softmax activation function
      self.softmax = nn.LogSoftmax(dim=1)
    #define the forward pass
    def forward(self, sent_id, mask):
      #pass the inputs to the model  
      _, cls_hs = self.bert(sent_id, attention_mask=mask, return_dict=False)
      x = self.fc1(cls_hs)
      x = self.relu(x)
      x = self.dropout(x)
      # output layer
      x = self.fc2(x)
      # apply softmax activation
      x = self.softmax(x)
      return x

Also, add an optimizer to enhance the performance:


optimizer = AdamW(model.parameters(),lr = 1e-5# learning rate

Then compute class weights, and send them as parameters while defining loss function to ensure imbalance in the dataset is handled well while computing the loss.

Training the model

After defining the model, define a function to train the model (fine-tune, in this case):


# function to train the model
def train():
  total_loss, total_accuracy = 0, 0
  # empty list to save model predictions
  # iterate over batches
  for step,batch in enumerate(train_dataloader):
    # progress update after every 50 batches.
    if step % 50 == 0 and not step == 0:
      print('  Batch {:>5,}  of  {:>5,}.'.format(step, len(train_dataloader)))
    # push the batch to gpu
    batch = [ for r in batch]
    sent_id, mask, labels = batch
    # clear previously calculated gradients 
    # get model predictions for the current batch
    preds = model(sent_id, mask)
    # compute the loss between actual and predicted values
    loss = cross_entropy(preds, labels)
    # add on to the total loss
    total_loss = total_loss + loss.item()
    # backward pass to calculate the gradients
    torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
    # update parameters
    # model predictions are stored on GPU. So, push it to CPU
    # append the model predictions
  # compute the training loss of the epoch
  avg_loss = total_loss / len(train_dataloader)
  # predictions are in the form of (no. of batches, size of batch, no. of classes).
  total_preds  = np.concatenate(total_preds, axis=0)
  #returns the loss and predictions
  return avg_loss, total_preds

Now, define another function that would evaluate the model on validation data.


# code
print "GFG"
# function for evaluating the model
def evaluate():
  # deactivate dropout layers
  total_loss, total_accuracy = 0, 0
  # empty list to save the model predictions
  total_preds = []
  # iterate over batches
  for step,batch in enumerate(val_dataloader):
    # Progress update every 50 batches.
    if step % 50 == 0 and not step == 0:
      # # Calculate elapsed time in minutes.
      # elapsed = format_time(time.time() - t0)
      # Report progress.
      print('  Batch {:>5,}  of  {:>5,}.'.format(step, len(val_dataloader)))
    # push the batch to gpu
    batch = [ for t in batch]
    sent_id, mask, labels = batch
    # deactivate autograd
    with torch.no_grad():
      # model predictions
      preds = model(sent_id, mask)
      # compute the validation loss between actual and predicted values
      loss = cross_entropy(preds,labels)
      total_loss = total_loss + loss.item()
      preds = preds.detach().cpu().numpy()
  # compute the validation loss of the epoch
  avg_loss = total_loss / len(val_dataloader) 
  # reshape the predictions in form of (number of samples, no. of classes)
  total_preds  = np.concatenate(total_preds, axis=0)
  return avg_loss, total_preds

Test the data

After fine-tuning the model, test it on the test dataset. Print a classification report to get a better picture of the model’s performance.


# get predictions for test data
with torch.no_grad():
  preds = model(,
  preds = preds.detach().cpu().numpy()
from sklearn.metrics import classification_report
pred = np.argmax(preds, axis = 1)
print(classification_report(test_y, pred))

After testing, we would get the results as follows:

Classification report

Link to the full code.



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