So here’s how you do it: first, let’s say we have this text input: “The quick brown fox jumps over the lazy dog.” We want to figure out what each word is (like noun or verb) so that we can analyze it more easily later on. To do this with FlaxAlbert, we start by loading in our pre-trained model and then adding some extra layers for token classification:
# Import necessary libraries
from flax import linen as nn # Importing the flax library and aliasing it as nn
import jax # Importing the jax library
import jax.numpy as np # Importing the numpy library from jax and aliasing it as np
# Load the pre-trained Albert model
model = ... # Loading the pre-trained FlaxAlbert model
# Add a linear layer and softmax activation for token classification
classifier_head = nn.Dense(num_outputs=len(labels), name="token_classification") # Creating a linear layer with the specified number of outputs and name for token classification
# Define the full model with input, hidden layers, and output head
def my_model(params):
# Inputs: (batch size, sequence length)
inputs = params["input"] # Assigning the input parameter to the "input" key in the params dictionary
# Hidden layers: (sequence length, num hidden units) -> (num hidden units, num hidden units)
hidden1 = nn.relu(nn.Dense(params["hidden_size"], name="hidden1")(inputs)) # Creating a hidden layer with the specified number of hidden units and name, and applying the relu activation function to it
hidden2 = nn.relu(nn.Dense(params["hidden_size"], name="hidden2")(hidden1)) # Creating a second hidden layer with the same number of hidden units and name, and applying the relu activation function to it
# Dropout layer (0.5 probability of dropping out)
dropout = jax.lax.stop_gradient(np.random.uniform(shape=inputs.shape, minval=0., maxval=1.) < 0.5) # Creating a dropout layer with a 50% probability of dropping out, using the jax library to generate a random uniform distribution and comparing it to 0.5
hidden2 *= dropout # Applying the dropout layer to the second hidden layer
# Output head: (num hidden units, num output classes) -> (batch size, sequence length, num output classes)
logits = classifier_head(hidden2) # Applying the linear layer created earlier to the second hidden layer to get the final output
return logits # Returning the final output logits
So what’s going on here? First we load in our pre-trained FlaxAlbert model and then add a linear layer with softmax activation for token classification. Then we define the full model, which includes input layers (for our text), hidden layers (to help us process that text), dropout techniques to prevent overfitting, and an output head for our final predictions.
Now let’s say we want to train this model on some data:
# Load the training data
train_data = ... # load your training data here
# Define a loss function (e.g., cross-entropy) and optimizer (e.g., Adam)
# Define a function that takes in model parameters and training data and returns the loss and gradients
loss, grads = jax.jit(lambda params: train_step(params, train_data))
# Define a function that updates the model parameters using gradient descent with a given step size
optimize = jax.lax.partial(jax.tree_map, jax.lax.update, params=model.init(), stepsize=0.01)
# Train the model for a certain number of epochs (e.g., 5)
for i in range(num_epochs):
# Loop through each batch of data and update our parameters using gradient descent
for j, batch in enumerate(train_data):
# Calculate loss and gradients on this batch
# Use vmap to apply the loss function to each batch of data and return the loss and gradients
_, (grads, _) = vmap(loss)(params=model.init(), inputs=(batch["input"],))
# Update our parameters using gradient descent with Adam optimizer
# Use the optimize function defined earlier to update the model parameters with the calculated gradients
params = optimize(params, grads)
So what’s going on here? First we load in our training data and define a loss function (like cross-entropy) and an optimizer (like Adam). Then we loop through each batch of data and update our parameters using gradient descent. This process is repeated for a certain number of epochs, which helps us to train the model more effectively over time.