This blogpost explores the concept of sentence embeddings and the role of pooling in creating compressed representations of sequences. The blog post delves into the different pooling methods, such as CLS pooling and mean pooling, and their implications for generating sentence embeddings. It highlights the advantages of using sentence embeddings for tasks that require an understanding of the entire sequence's meaning. Additionally, the post touches on embedding similarity measures, emphasizing the use of cosine similarity. Overall, the blog provides insights into the complexity and significance of sentence embeddings and their applications in natural language processing.
The blogpost can be found on our Medium channel by clicking this link.