Summary: Getting Hands-On with BERT
Let’s summarize what we have learned so far.
We'll cover the following...
Key highlights
Summarized below are the main highlights of what learned in this chapter.
We looked at different configurations of the pre-trained BERT model provided by Google.
We learned that we can use the pre-trained BERT model in two ways: as a feature extractor by extracting embeddings, and by fine-tuning the pre-trained BERT model for downstream tasks such as text classification, question-answering, and more.
We learned how to extract embeddings from the pre-trained BERT model in detail.
Access this course and 1400+ top-rated courses and projects.