- It seems like these embeddings are learned during the training phase. Presumably by backpropagating to the embedding during each epoch?
- Given that they are learned, doesn't that mean that they are completely context specific to a given trained model? Ie, they can't readily be shared on their own?
- What does "similar" mean here? Is there some emerging practice on how to determine how close is close "enough" between multiple vectors for the purpose of similarity searches? Is this too determined by the model weights somehow?
I hope I'm not missing something fundamental with these questions. Or maybe I hope I am missing something and someone points out my errors. That's good too. :)
- It seems like these embeddings are learned during the training phase. Presumably by backpropagating to the embedding during each epoch?
- Given that they are learned, doesn't that mean that they are completely context specific to a given trained model? Ie, they can't readily be shared on their own?
- What does "similar" mean here? Is there some emerging practice on how to determine how close is close "enough" between multiple vectors for the purpose of similarity searches? Is this too determined by the model weights somehow?
I hope I'm not missing something fundamental with these questions. Or maybe I hope I am missing something and someone points out my errors. That's good too. :)