What are the differences between traditional and transfer learning in NLP?

What are the differences between traditional and transfer learning in NLP?

Traditional learning in NLP involves training models from scratch on a specific dataset, while transfer learning uses pre-trained models and fine-tunes them on a new task, making it more efficient.

Can you give me an example of transfer learning in NLP?

Sure! A common example is using BERT, a pre-trained model, and fine-tuning it for sentiment analysis on a specific dataset.

What are the advantages of using transfer learning?

Transfer learning significantly reduces training time, requires less data, and often leads to better performance on specific tasks.

Are there any downsides to transfer learning?

Yes, transfer learning can sometimes lead to overfitting if the new dataset is too small or not representative of the task.

Which approach is better for small datasets?

For small datasets, transfer learning is generally better as it leverages knowledge from larger datasets, improving performance.