Fine-tune BERT with Classification

[calusbr]

Hello I'm running the code in my dataset. With frozen weights the values are promising.

However, to fully defrost the BERT the model hits only one label.

Warning: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use zero_division parameter to control this behavior. warn_prf(average, modifier, msg_start, len(result))

          precision    recall  f1-score   support

       0       0.00      0.00      0.00        42
       1       0.40      1.00      0.57        28

accuracy                           0.40        70

macro avg 0.20 0.50 0.29 70
weighted avg 0.16 0.40 0.23 70

Do you need any more procedures for the model to learn labels correctly? I'm using 470 examples.