Our article introducing sentence embeddings and transformers explained that these models can be used across a range of applications, such as semantic textual similarity (STS), semantic clustering, or information retrieval (IR) using concepts rather than words.
This article dives deeper into the training process of the first sentence transformer, sentence-BERT, or more commonly known as SBERT. We will explore the Natural Language Inference (NLI) training approach of softmax loss to fine-tune models for producing sentence embeddings.
This is a companion discussion topic for the original entry at https://www.pinecone.io/learn/train-sentence-transformers-softmax/