From my experience trying to train embeddings from transformers, using cosine similarity is less restrictive for the model than euclidean distance. Both works b

From my experience trying to train embeddings from transformers, using cosine similarity is less restrictive for the model than euclidean distance. Both works but cosine similarity seems to have slightly better performance. Another thing you have to keep in mind is that these embeddings are in n dimensional space. Intuitions about the real world does not apply there