Summary: Exploring Sentence and Domain-Specific BERT

Key highlights

Summarized below are the main highlights of what we've learned in this chapter.

• We started off by understanding how Sentence-BERT works. We learned that in Sentence-BERT, we use mean or max pooling for computing the sentence representation. We also learned that Sentence-BERT is basically a pre-trained BERT model that is fine-tuned for computing sentence representation. For fine-tuning the pre-trained BERT model, Sentence-BERT uses a Siamese and triplet network architecture, which makes the fine-tuning faster and helps in obtaining accurate sentence embeddings.

• We learned how to use the sentence-transformers library. We learned how to compute sentence representation and also how to compute the semantic similarity between a sentence pair using sentence-transformers.