I have a list of sentences :
sentences = ["Missing Plate", "Plate not found"]
I am trying to find the most similar sentences in the list by using Transformers model with Huggingface embedding. I am able to find the similar sentences but the model is still not able to identify the difference between :
"Message ID exists" "Message ID doesn't exist"
[Note: I am trying to find the similarity by using the Cosine similarity from pytorch]
Can you suggest me ways to hyperparameter tune my model so that the model can weigh in more on the negative words and consider them opposite?
I found the list of parameters that can be tuned but not sure what the best parameters would be