I was wondering why there are a lot of false positives in the model. examples: "No that is it!", "sure, the main one is jacramer12@gmail.com", "yes please".
I was trying to train with the examples I found in my dataset. But I'm afraid this imbalance is more severe. Any suggestions?
I was wondering why there are a lot of false positives in the model. examples: "No that is it!", "sure, the main one is jacramer12@gmail.com", "yes please". I was trying to train with the examples I found in my dataset. But I'm afraid this imbalance is more severe. Any suggestions?