How would you solve token misclassification when training a transformer model for named entity recognition

Question

Can you tell me How would you solve token misclassification when training a transformer model for named entity recognition?

Answer 1

oken misclassification in NER can be addressed by using proper token alignment, weighted loss for class imbalance, and fine-tuning pre-trained transformer models with labeled data.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Aligns tokens and labels properly to avoid misclassification.
• Uses label padding and mapping to maintain input-label consistency.
• Implements weighted loss and proper optimizer settings for better training stability.

Hence, by aligning tokens, encoding labels correctly, and carefully managing training parameters, we mitigate token misclassification and improve NER model performance.