Thank you for the awesome solution. however, I am facing an error while predicting output as below :
I have trained model for name and address key and I am getting validation accuracy for this as below :
+--------------------+----------+----------+----------+----------+
| name | mEP | mER | mEF | mEA |
+====================+==========+==========+==========+==========+
| address | 0.920846 | 0.943102 | 0.931841 | 0.943102 |
+--------------------+----------+----------+----------+----------+
| name | 0.965278 | 0.923588 | 0.943973 | 0.923588 |
+--------------------+----------+----------+----------+----------+
but when I predict I am getting following output
Rajesh Gangaram -- name
Thank you for the awesome solution. however, I am facing an error while predicting output as below : I have trained model for name and address key and I am getting validation accuracy for this as below :
+--------------------+----------+----------+----------+----------+ | name | mEP | mER | mEF | mEA | +====================+==========+==========+==========+==========+ | address | 0.920846 | 0.943102 | 0.931841 | 0.943102 | +--------------------+----------+----------+----------+----------+ | name | 0.965278 | 0.923588 | 0.943973 | 0.923588 | +--------------------+----------+----------+----------+----------+
but when I predict I am getting following output Rajesh Gangaram -- name
Yadav 374,shri ram chawk, omr road, chennai, tamil nadu, 600097 -- address
however Yadav is part of name.
Can you please guide me how to reduce there token misclassifications ?