BertForTokenClassification: A Python Class for Token Classification Tasks

The 'BertForTokenClassification' class is a subclass of 'BertPretrainedModel' and is used for token classification tasks such as Named Entity Recognition (NER) or Part-of-Speech (POS) tagging. It takes as input the token IDs, token type IDs, position IDs, and attention mask produced by a tokenizer and returns logits, which represent the predicted class probabilities for each token.

The class initializes with a 'bert' model, which is a pre-trained BERT model, and a 'num_classes' parameter, which represents the number of classes to predict. It also takes an optional 'dropout' parameter to apply dropout regularization to the output of the BERT model.

During forward pass, the input is passed through the BERT model to obtain the 'sequence_output', which is then passed through a dropout layer. The output of the dropout layer is then passed through a linear layer, which produces the logits. The logits are then used to calculate the loss and update the model during training.