The pre-trained language models were adapted to the NER task by concatentating the model with a classification head, which is a fully connected neural network layer with outputs corresponding to the B-and I-tags for each of the 28 entity types and the O-tag. Thus, the classification head contained 57 output neurons. The pre-trained weights of the language models were loaded from Hugging Face 14,68-70 , and the classification head was initialized with random weights. All the model weights were trained during full fine-tuning of all the BERT-based NER models. However, in the last-layer fine-tuning of the DeBERTa model, only the classification head was trained, keeping all other layer weights fixed.