Here’s an example: let’s say you give this model the following prompt: “In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced.” And then we have two choices for it to choose from: “It is eaten with a fork and a knife” or “It is eaten while held in the hand”.
The model will read through that prompt and try to figure out which choice is most likely based on what it has learned from other texts. It’s like having your own personal food critic who can tell you how Italians eat their pizza!
Now, if we want to use this model in our code, here’s an example of how we might do that:
# Import necessary libraries
from transformers import AutoTokenizer, FlaxAlbertForMultipleChoice
import tensorflow as tf
# Load the pre-trained tokenizer and model from Hugging Face Hub
tokenizer = AutoTokenizer.from_pretrained("albert/albert-base") # Load the tokenizer for the ALBERT model
model = FlaxAlbertForMultipleChoice.from_pretrained("albert/albert-base") # Load the pre-trained ALBERT model
# Define our prompt and choices for the model to choose between
prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
choice0 = "It is eaten with a fork and a knife."
choice1 = "It is eaten while held in the hand."
# Prepare our input data for the model to process
inputs = tokenizer(prompt + "\n" + choice0 + "\n" + choice1, return_tensors="tf") # Tokenize the prompt and choices and convert them into tensors for the model to process
outputs = model(**inputs) # Pass the inputs to the model and get the outputs
predictions = tf.argmax(outputs[0], axis=1).numpy() # Get the index of the highest probability output from the model
print("The model thinks that:", choice0 if predictions == 0 else choice1, "is more likely to be true.") # Print the choice with the highest probability as the predicted answer
A simple example of how we can use the Transformers FlaxAlbertForMultipleChoice Model in our code.