Hi all, I picked up the ‘NLP with Transformers’ book released recently and have begun reading it. This is my first foray into NLP and Transformers so I have a few questions:
When I have used PyTorch previously, I have been able to use GridSearch for parameters by wrapping it in a Skorch wrapper. Are we able to GridSearch the parameters for a model when we train transformer models?
I used the follow code to train my first text classification model, but in order to actually run this on custom sentences I had to first push the model to my hub and then load it as a classifier. Is there a way to begin testing the model directly after training, instead of first pushing it to the hub as a classifier?
#Now we set the trainer up trainer = Trainer(model = model, args = training_args, compute_metrics = compute_metrics, train_dataset = emotions_encoded['train'], eval_dataset = emotions_encoded['validation'], tokenizer = tokenizer) trainer.train(); #Load the model and test it out model_id = 'Jimchoo91/distilbert-base-uncased-finetuned-emotion' classifier = pipeline('text-classification', model = model_id) #Load custom sentence custom_tweet = 'I really love you to pieces' preds = classifier(custom_tweet, return_all_scores = True)