Each task is unique, and having sentence / text embeddings tuned for that specific task greatly improves the performance.
SentenceTransformers was designed in such way that fine-tuning your own sentence / text embeddings models is easy. It provides most of the building blocks that you can stick together to tune embeddings for your specific task.
Sadly there is no single training strategy that works for all use-cases. Instead, which training strategy  to use greatly depends on your available data and on your target task.
In the Training section, I will discuss the fundamentals of training your own embedding models with SentenceTransformers. In the Training Examples section, I will provide examples how to tune embedding models for common real-world applications.
Network Architecture
For sentence / text embeddings, we want to map a variable length input text to a fixed sized dense vector. The most basic network architecture we can use is the following:
