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Benefits of Fine-Tuning AI Models

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Questions and Answers

What is the main purpose of transforming raw input data?

To make the data compatible with the model (correct)

To increase computational resources

To evaluate model performance

To reduce the size of the dataset

What is the role of the Trainer utility in the Hugging Face library?

To freeze model weights

To handle dynamic padding of variable-length sequences

To manage the fine-tuning process of a model (correct)

To provide hyperparameters for the training process

What is the advantage of using dynamic padding over traditional padding methods?

It reduces wasted computational resources (correct)

It is only used for fixed-length sequences

It increases the length of every sequence to the longest one in the dataset

It is faster than traditional padding methods

What is a primary benefit of fine-tuning a pre-trained model?

It allows the model to tailor its knowledge to specific tasks, leading to improved performance. Signup and view all the answers

What is the purpose of freezing the model's weights during training?

To speed up training significantly but with less accuracy. Signup and view all the answers

When freezing model weights, which part of the model is it generally better to freeze?

The lower layers near the beginning of the model Signup and view all the answers

What is the purpose of the TrainingArguments object in the Hugging Face library?

To provide hyperparameters and options for the training process Signup and view all the answers

What is the typical choice of classifier used in training a transformer-based model?

Feed Forward Neural Network (FFNN) Signup and view all the answers

What is the purpose of a Data Collator in machine learning?

To process and prepare input data for a model. Signup and view all the answers

What is the term for the collection of data used for machine learning?

Dataset Signup and view all the answers