Hi from what i noticed the weights your using are already fine tuned for token classification (the classifier has been trained for said task), i recommend you fine tune on the bert base case as such:
from transformers import AutoModelForTokenClassification
model = AutoModelForTokenClassification('bert-base-uncased', num_labels=2)
# Start your own training

or if you want to write your own as requested with a custom classifier head
import torch.nn as nn
from transformers import AutoModel
class PosModel(nn.Module):
    def __init__(self):
        super(PosModel, self).__init__()
        
        self.base_model = AutoModel.from_pretrained('bert-base-uncased')
        self.dropout = nn.Dropout(0.5)
        self.linear = nn.Linear(768, 2) # output features from bert is 768 and 2 is ur number of labels
        
    def forward(self, input_ids, attn_mask):
        outputs = self.base_model(input_ids, attention_mask=attn_mask)
        # You write you new head here
        outputs = self.dropout(outputs[0])
        outputs = self.linear(outputs)
        
        return outputs

model = PosModel()
model.to('cuda')