How to write a custom configuration for hugging face model for Token Classification
**Model description**

I add simple custom `pytorch-crf` layer on top of `TokenClassification model` for `NER`. It will make the model more robust.

I train the model and I get the error: 

    ***** Running training *****
      Num examples = 4
      Num Epochs = 2
      Instantaneous batch size per device = 2
      Total train batch size (w. parallel, distributed & accumulation) = 2
      Gradient Accumulation steps = 1
      Total optimization steps = 4
    TypeError: __init__() missing 3 required positional arguments: 'id2label', 'label2id', and 'num_labels'


**Code**

    from torchcrf import CRF
    
    model_checkpoint = "spanbert"
    tokenizer = BertTokenizer.from_pretrained(model_checkpoint,add_prefix_space=True)
    bert_model = BertForTokenClassification.from_pretrained(
                            model_checkpoint,id2label=id2label,label2id=label2id)
    bert_model.config.output_hidden_states=True




    class BertClassifierConfig(PretrainedConfig):
        
        
        model_type="BertForTokenClassification"
        
        def __init__(self,id2label ,label2id,num_labels,**kwargs):
            
            self.num_labels=num_labels
            self.id2label=id2label
            self.label2id=label2id
            self.output_hidden_states=True
            
            super().__init__(**kwargs)


**Model**


    class BertForTokenClassification(PreTrainedModel):
       
        config_class =BertClassifierConfig
        def __init__(self, config,bert_model, num_labels):
          super(BertForTokenClassification, self).__init__(config)
          self.bert = bert_model
          self.dropout = nn.Dropout(0.25)
          self.classifier = nn.Linear(768, num_labels)
          self.crf = CRF(num_labels, batch_first = True)
        
        def forward(self, input_ids, attention_mask,  labels=None, token_type_ids=None):
            outputs = self.bert(input_ids, attention_mask=attention_mask)
            sequence_output = torch.stack((outputs[1][-1], outputs[1][-2], outputs[1][-3], outputs[1][-4])).mean(dim=0)
            sequence_output = self.dropout(sequence_output)
            
            emission = self.classifier(sequence_output) # [32,256,17]
    
            labels=labels.reshape(attention_mask.size()[0],attention_mask.size()[1])
    
            if labels is not None:
    
                loss = -self.crf(log_soft(emission, 2), labels, mask=attention_mask.type(torch.uint8), reduction='mean')
                prediction = self.crf.decode(emission, mask=attention_mask.type(torch.uint8))
                return [loss, prediction]
                    
            else:
    
                prediction = self.crf.decode(emission, mask=attention_mask.type(torch.uint8))
                return prediction



**Saving**

    configuration = BertClassifierConfig(id2label ,label2id,num_labels=len(label2id))
    model = BertForTokenClassification(configuration,bert_model, num_labels=len(label2id))
    model.to(device)


    args = TrainingArguments(
        "test0000",
        # evaluation_strategy="epoch",
        save_strategy="epoch",
        learning_rate=2e-5,
        num_train_epochs=2,
        weight_decay=0.01,
        per_device_train_batch_size=2,
        # per_device_eval_batch_size=32
        fp16=True
        # bf16=True #Ampere GPU
    )
    
    trainer = Trainer(
        model=model,
        args=args,
        train_dataset=train_data,
        # eval_dataset=train_data,
        # data_collator=data_collator,
        # compute_metrics=compute_metrics,
        tokenizer=tokenizer)



**Saving**


    trainer.train()
    trainer.save_model("modeltest")
    
    
    AutoConfig.register("BertForTokenClassification", BertClassifierConfig)
    AutoModel.register(BertClassifierConfig, BertForTokenClassification)

**ERROR**


     ***** Running training *****
          Num examples = 4
          Num Epochs = 2
          Instantaneous batch size per device = 2
          Total train batch size (w. parallel, distributed & accumulation) = 2
          Gradient Accumulation steps = 1
          Total optimization steps = 4
        TypeError: __init__() missing 3 required positional arguments: 'id2label', 'label2id', and 'num_labels'



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