A step-by-step guide on using Sentence Transformers models with TensorFlow and Keras instead of the default PyTorch backend. Covers creating a custom TFSentenceTransformer class that replicates mean pooling and normalization, validating results against the original PyTorch model (difference < 0.00000007), and building an end-to-end model with the BERT tokenizer (FastBertTokenizer/TFBERTTokenizer) embedded in the model graph for seamless inference.
