Chapter 7: Text Representation
So far, we have addressed classification and generation problems with the transformers library. Text representation is another crucial task in modern Natural Language Processing (NLP), especially for unsupervised tasks such as clustering, semantic search, and topic modeling. Representing sentences by using various models such as Universal Sentence Encoder (USE) and Siamese BERT (Sentence-BERT) with additional libraries such as sentence transformers will be explained here. Zero-shot learning using BART will also be explained, and you will learn how to utilize it. Few-shot learning methodologies and unsupervised use cases such as semantic text clustering and topic modeling will also be described. Finally, one-shot learning use cases such as semantic search will be covered.
The following topics will be covered in this chapter:
- Introduction to sentence embeddings
- Benchmarking sentence similarity models
- Using BART for zero-shot learning...
