Continual learning evaluation
Continual learning is the ability of a model to learn new tasks without forgetting previously learned ones. Here’s an example of how you might evaluate continual learning in LLMs:
- Set up our continual learning framework by initializing the model, the tokenizer, and the main function structure:
def evaluate_continual_learning( model_name, tasks=['sst2', 'qnli', 'qqp'], num_epochs=3 ): model = \ AutoModelForSequenceClassification.from_ pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) results = {} - Define the preprocessing function that handles different input formats for various GLUE tasks:
def preprocess_function(examples, task): # Different tasks have different input formats...
