Then there’s Albert, which is an open-source pretrained language representation model that can be fine-tuned on specific tasks like multiple choice questions (MCQs). And finally, we have the “ForMultipleChoiceModule” part which just means this particular implementation of Flax and Albert is specifically designed for MCQs.
So how does it work? Well, let’s say you have a dataset of MCQs with answers that are either A, B, C or D (or sometimes E). The idea behind “FlaxAlbertForMultipleChoiceModule” is to take this data and train the model on it so that when given a new question, it can predict which answer is correct.
Here’s an example of how you might use it:
# Import necessary libraries
import flax_albert as fal
from flax import linen_util
# Load pretrained Albert checkpoint and create model
model = fal.AlbertForSequenceClassification(num_labels=4) # Create an instance of the model with 4 possible labels (A, B, C, or D)
params = linen_util.load_checkpoint('albert-base')['params'] # Load the pretrained parameters from the 'albert-base' checkpoint
model.init(context=linen_util.numpy_to_jaxprimitives(params)) # Initialize the model with the pretrained parameters
# Define input data and labels for training
input_data = [question1, question2, ...] # List of questions (strings)
labels = [answer1, answer2, ...] # List of answers (integers representing A, B, C, or D)
# Train the model on this data using gradient descent and other fancy algorithms
optimizer = fal.create_optimizer(model.apply_fn, params=params) # Create an optimizer to update the model parameters using gradient descent and other fancy algorithms
losses = [] # Create an empty list to store the loss values
for i in range(num_epochs): # Loop through the specified number of epochs
for j in range(len(input_data)): # Loop through the length of the input data
loss, _ = model.apply({'params': params}, input_data[j], labels[j]) # Calculate the loss for the current input data and labels
losses.append(loss) # Add the loss value to the list of losses
optimizer.step() # Update the model parameters based on the current loss and other fancy algorithms
And that’s it! Once you’ve trained your “FlaxAlbertForMultipleChoiceModule” model, you can use it to predict which answer is correct for new questions:
# Load pretrained Albert checkpoint and create model
model = fal.AlbertForSequenceClassification(num_labels=4) # Create a model for multiple choice questions with 4 possible choices (A, B, C or D)
params = linen_util.load_checkpoint('albert-base')['params'] # Load the pretrained Albert checkpoint parameters
model.init(context=linen_util.numpy_to_jaxprimitives(params)) # Initialize the model with the loaded parameters
# Define input data for prediction
input_data = [new_question] # List of new questions (strings) to be predicted
labels = None # We don't need labels for prediction, since the model will output probabilities for each answer instead
predictions = model.apply({'params': params}, input_data[0], labels=None)['logits'] # Get the logits (scores/probabilities) for each answer from the model
softmaxed_predictions = jax.nn.softmax(predictions) # Apply the softmax activation function to get probabilities for each answer (A, B, C or D)
most_likely_answer = np.argmax(softmaxed_predictions[0]) # Find the index of the answer with the highest probability using argmax function
And that’s it! You now have a model that can predict which answer is most likely to be correct for new MCQs based on your training data. Pretty cool, huh?