Hello there,
I am struggling with two simple questions about transformers models. I hope somebody in the huggingface communitity can shed some light on these points.

transformers models create contextual embeddings. That is, the embedding of a word depend on the words around it in the sentence. Does this means a word has a different embedding for every possible sentence?
At inference time, I am feeding a sentence to a transformer model for classification. How can the attention mechanism work (this word is related to this word, etc) if the model has never seen the sentence? I get the intuition at training time (we minimize the loss) but what happens at inference time?

Any insights would be greatly appreciated!
Thanks!