Fine-Tuning Language Models for Question Answering

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That’s where fine-tuning comes in!

Fine-tuning is like teaching our chip-loving friend to understand specific types of questions and answers better. We do this by feeding them a bunch of training data (questions and their corresponding answers) and letting the model learn from it.

For example, let’s say you have a dataset of customer support inquiries and responses. You could fine-tune your language model to understand common issues and provide helpful solutions based on the data. This would save time and resources compared to having human agents manually respond to each query.

Here’s an example script using Python:

# Load pretrained language model (LLM)
from transformers import AutoTokenizer, TFBertForSequenceClassification

# Load the tokenizer from the transformers library
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')

# Load the pre-trained BERT model for sequence classification with 2 labels
model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)

# Load training data (questions and answers)
train_data = pd.read_csv('customer_support_inquiries.csv')

# Preprocess the data for fine-tuning
def preprocess(text):
    # Clean up text, convert to lowercase, etc.
    # Tokenize the text using the tokenizer and add padding and truncation
    return tokenizer(text, padding=True, truncation=True)['input_ids']

# Fine-tune model on training data
for epoch in range(3):
    # Iterate through the training data in batches of 64
    for batch in train_data.batch(64).iterate():
        # Preprocess input and target labels (answers)
        # Get the input questions from the batch and preprocess them
        inputs = preprocess([row['question'] for row in batch])
        # Get the target labels (answers) from the batch and convert them to binary values
        targets = np.array([1 if row['answer'] == 'Yes' else 0 for row in batch]).reshape(-1, 2)
        
        # Train model on input and target labels
        with tf.GradientTape() as tape:
            # Get the model outputs for the inputs
            outputs = model(inputs, training=True)
            # Calculate the loss using Sparse Categorical Crossentropy
            loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)(outputs[0], targets)
        
        # Calculate gradients and update weights
        # Get the gradients of the loss with respect to the trainable variables
        grads = tape.gradient(loss, model.trainable_variables)
        # Apply the gradients to the trainable variables using an optimizer
        optimizer.apply_gradients(zip(grads, model.trainable_variables))
    
# Evaluate fine-tuned model on test data (new questions)
test_data = pd.read_csv('customer_support_inquiries_test.csv')
# Create an empty list to store the predictions
predictions = []
# Iterate through the test data
for row in test_data['question']:
    # Preprocess input for prediction
    # Tokenize the input question and add padding and truncation
    inputs = preprocess([row])
    
    # Run model on input and get predictions (probabilities)
    # Get the model outputs for the input and get the probabilities for each label
    outputs = model(inputs, training=False)
    probabilities = tf.nn.softmax(outputs[0], axis=-1)[0]
    
    # Get predicted label based on highest probability
    # Get the index of the highest probability and check if it corresponds to a "Yes" answer
    prediction = np.argmax(probabilities)
    # Append the prediction to the list of predictions
    predictions.append(prediction == 1)

In this example, we’re using the BERT pretrained language model and fine-tuning it to classify whether a customer support inquiry has a positive or negative answer (Yes/No). We then evaluate the fine-tuned model on new test data to see how well it can predict answers for questions that weren’t used during training.

Hope this helps clarify things!

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