Fine-tuning BERT model for Sentiment Analysis
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
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
!pip install transformers
!pip install torch
import numpy as np
import pandas as pd
import torch
from transformers import BertTokenizerFast, AutoModel
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
import matplotlib.pyplot as plt
from torch import nn
from torch.utils.data import DataLoader
from torch.nn import CrossEntropyLoss
from torch.optim import AdamW
df = pd.read_csv('/content/sentiment_train.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]
plt.hist(train_lens)
From the graph above, we take 17 as the padding length.
Tokenizing the data
Tokenize the data and encode sequences using the BERT tokenizer.
tokens_train = tokenizer.batch_encode_plus(
train_text.tolist(),
max_length = pad_len,
pad_to_max_length = True,
truncation = True
)
tokens_val = tokenizer.batch_encode_plus(
val_text.tolist(),
max_length = pad_len,
pad_to_max_length = True,
truncation = True
)
tokens_test = tokenizer.batch_encode_plus(
test_text.tolist(),
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:
model = BERT_architecture(bert)
optimizer = AdamW(model.parameters(), lr=1e-5)
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():
model.train()
total_loss, total_accuracy = 0, 0
# empty list to save model predictions
total_preds=[]
# 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 = [r.to(device) for r in batch]
sent_id, mask, labels = batch
# clear previously calculated gradients
model.zero_grad()
# 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
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
# update parameters
optimizer.step()
# model predictions are stored on GPU. So, push it to CPU
preds=preds.detach().cpu().numpy()
# append the model predictions
total_preds.append(preds)
# 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.
# function for evaluating the model
def evaluate():
print("\nEvaluating...")
# deactivate dropout layers
model.eval()
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 = [t.to(device) 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()
total_preds.append(preds)
# 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.
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Move the model to the selected device (GPU or CPU)
model.to(device)
# Get predictions for test data
with torch.no_grad():
# Move data to device and perform prediction
preds = model(test_seq.to(device), test_mask.to(device))
# Move the predictions to CPU for further processing
preds = preds.detach().cpu().numpy()
# Convert predictions to class labels (0 or 1 for binary classification)
pred = np.argmax(preds, axis=1)
# Print classification report
from sklearn.metrics import classification_report
print(classification_report(test_y, pred))
After testing, we would get the results as follows:
Link to the full code is here.
