Benefits of Fine-Tuning in AI Models

Fine-tuning with custom data enables the model to better understand and respond to ______ jargon or regional language nuances.

Training a classifier with transformer-based models generally follows a ______-step approach.

During training, we will “freeze” the model such that its ______ will not be updated.

A ______ Forward Neural Network is typically chosen as the classifier.

In this context, the dataset is the ______ dataset.

It transforms raw input data into a format that the model can understand, which may involve ______, padding, and batching.

Unlike traditional ______ methods which pad every sequence to the length of the longest one in the dataset, dynamic padding adjusts padding for each batch separately.

When freezing model ______, it’s generally better to freeze lower weights near the beginning of the model.

______ – A configuration object provided by the Hugging Face that holds our hyperparameters and options for the training process.

______ – A utility provided by the Hugging Face library that manages the fine-tuning process of a model.