How to load BertforSequenceClassification models weights into BertforTokenClassification model?
Initially, I have a fine-tuned BERT base cased model using a text classification dataset and I have used BertforSequenceClassification class for this. 

from transformers import BertForSequenceClassification, AdamW, BertConfig

# Load BertForSequenceClassification, the pretrained BERT model with a single 
# linear classification layer on top. 
model = BertForSequenceClassification.from_pretrained(
    "bert-base-uncased", # Use the 12-layer BERT model, with an uncased vocab.
    num_labels = 2, # The number of output labels--2 for binary classification.
                    # You can increase this for multi-class tasks.   
    output_attentions = False, # Whether the model returns attentions weights.
    output_hidden_states = False, # Whether the model returns all hidden-states.
)


Now I want to use this fine-tuned BERT model weights for Named Entity Recognition and I have to use BertforTokenClassification class for this. I'm unable to figure out how to load the fine-tuned BERT model weights into the new model created using BertforTokenClassification.

Thanks in advance.......................

You can get weights from the bert inside the first model and load into the bert inside the second:

new_model = BertForTokenClassification(config=config)
new_model.bert.load_state_dict(model.bert.state_dict())

