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

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

Transfer learning involves using pre-trained models on new tasks, while traditional learning trains models from scratch.

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

Sure! A common example is using BERT, which is pre-trained on a large corpus, and fine-tuning it for specific tasks like sentiment analysis.

What are the advantages of using transfer learning?

Transfer learning can save time and resources, improve performance on small datasets, and leverage knowledge from related tasks.

Are there any downsides to transfer learning?

Yes, potential downsides include overfitting to the pre-trained model and the risk of negative transfer if the tasks are too different.