How do you address data sparsity when fine-tuning a text generation model for medical text generation

Question

With the help of code can you tell me How do you address data sparsity when fine-tuning a text generation model for medical text generation?

Answer 1

Use techniques like data augmentation, transfer learning, and regularization to mitigate data sparsity while fine-tuning a text generation model.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Uses pre-trained GPT-2 for transfer learning.
• Tokenizes medical text dataset with padding and truncation.
• Applies regularization via weight decay and warmup steps.
• Uses small batch size to address data sparsity and avoid overfitting.
• Sets up and fine-tunes the model with the Trainer API.

Hence, this approach effectively mitigates data sparsity through transfer learning, careful preprocessing, and regularization techniques.