Python: BERT Error - Some weights of the model checkpoint at were not used when initializing BertModel
I am creating an entity extraction model in PyTorch using bert-base-uncased but when I try to run the model I get this error:
Error:
Some weights of the model checkpoint at D:\Transformers\bert-entity-extraction\input\bert-base-uncased_L-12_H-768_A-12 were not used when initializing BertModel:    
['cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight',   'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias',  
 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight',  
 'cls.predictions.bias']  
    - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
    - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).

I have downloaded the bert model from here and the additional files from here
Code
Following is the code for my model:
import config
import torch
import transformers
import torch.nn as nn

def loss_fn(output, target, mask, num_labels):

    lfn = nn.CrossEntropyLoss()
    active_loss = mask.view(-1) == 1
    active_logits = output.view(-1, num_labels)
    active_labels = torch.where(
        active_loss,
        target.view(-1),
        torch.tensor(lfn.ignore_index).type_as(target)
    )
    loss = lfn(active_logits, active_labels)
    return loss

class EntityModel(nn.Module):
    def __init__(self, num_tag, num_pos):
        super(EntityModel, self).__init__()

        self.num_tag = num_tag
        self.num_pos = num_pos
        self.bert = transformers.BertModel.from_pretrained(config.BASE_MODEL_PATH)
        self.bert_drop_1 = nn.Dropout(p = 0.3)
        self.bert_drop_2 = nn.Dropout(p = 0.3)
        self.out_tag = nn.Linear(768, self.num_tag)
        self.out_pos = nn.Linear(768, self.num_pos)

    def forward(self, ids, mask, token_type_ids, target_pos, target_tag):
        o1, _ = self.bert(ids, 
                          attention_mask = mask,
                          token_type_ids = token_type_ids)

        bo_tag = self.bert_drop_1(o1)
        bo_pos = self.bert_drop_2(o1)

        tag = self.out_tag(bo_tag)
        pos = self.out_pos(bo_pos)

        loss_tag = loss_fn(tag, target_tag, mask, self.num_tag)
        loss_pos = loss_fn(pos, target_pos, mask, self.num_pos)

        loss = (loss_tag + loss_pos) / 2

        return tag, pos, loss 

print(&quot;model.py run success!&quot;)


As R. Marolahy suggests, if you don't want to see this every time, I know I don't, add the following:
from transformers import logging
logging.set_verbosity_error()

