So say you have some text that looks like this: “The quick brown fox jumps over the lazy dog.” And let’s say we want to predict what comes next in the sentence based on context clues (because sometimes writers forget how to finish their thoughts). We can feed our input into the RobertaModel Forward Method and it will spit out a prediction for us.
Here’s an example of what that might look like:
# Import necessary libraries
from transformers import TFBertForSequenceClassification, BertTokenizerFast
import tensorflow as tf
# Load pretrained Roberta model
model = TFBertForSequenceClassification.from_pretrained('roberta-base') # Load the pretrained Roberta model for sequence classification
tokenizer = BertTokenizerFast.from_pretrained('roberta-base') # Load the tokenizer for the Roberta model
# Define input text and label (in this case, we're predicting the next word)
input_text = "The quick brown fox jumps over the lazy dog."
label = 'over' # This is what we want to predict as the next word in our sentence.
# Preprocess input text by tokenizing and converting to a list of integers (indices).
encoded_input = tokenizer(input_text, return_tensors='tf')['input_ids'] # Tokenize the input text and convert it to a list of integers (indices) using the tokenizer
# Define placeholder for output label (in this case, we're predicting the next word)
output_label = tf.convert_to_tensor([[1 if l == 'over' else 0 for l in labels] for _, labels in encoded_input['labels'].numpy()]) # Convert the list of labels (which is a nested list containing one label per input text) into a tensor that the model can use as an output.
# Run prediction through RobertaModel Forward Method and get results.
predictions = model(encoded_input['input_ids'], training=False, return_dict=True).logits # Run the preprocessed input text through the RobertaModel Forward Method to generate predictions for each label in our list of labels (which is a nested list containing one label per input text).
# Get predicted probability for 'over' as next word.
predicted_probability = tf.nn.softmax(predictions['logits'][:, 1])[0, output_label] # Get the predicted probability for our chosen label (in this case, 'over') based on the input text and pretrained Roberta model.
The “RobertaModel Forward Method” in a nutshell. It’s not as scary as it sounds once you break it down into simpler terms.