model loading the checkpoint error
RuntimeError: Error(s) in loading state_dict for BertModel:
	size mismatch for embeddings.token_type_embeddings.weight: copying a param of torch.Size([16, 768]) from checkpoint, where the shape is torch.Size([2, 768]) in current model.
But  I  print the model.embeddings.token_type_embeddings  it was Embedding(16,768) .<|||||>which model are you loading?<|||||>> which model are you loading?

the pre-trained  model   chinese_L-12_H-768_A-12<|||||>mycode:
bert_config = BertConfig.from_json_file('bert_config.json')
model=BertModel(bert_config)
model.load_state_dict(torch.load('pytorch_model.bin'))

The error:
RuntimeError: Error(s) in loading state_dict for BertModel:
	size mismatch for embeddings.token_type_embeddings.weight: copying a param of torch.Size([16, 768]) from checkpoint, where the shape is torch.Size([2, 768]) in current model.
<|||||>I'm testing the chinese model.
Do you use the `config.json` of the chinese_L-12_H-768_A-12 ?
 Can you send the content of your `config_json` ?<|||||>> I'm testing the chinese model.
> Do you use the `config.json` of the chinese_L-12_H-768_A-12 ?
> Can you send the content of your `config_json` ?

In the 'config.json' of the chinese_L-12_H-768_A-12  ,the type_vocab_size=2.But I change the config.type_vocab_size=16, it still error.<|||||>> I'm testing the chinese model.
> Do you use the `config.json` of the chinese_L-12_H-768_A-12 ?
> Can you send the content of your `config_json` ?

{
  "attention_probs_dropout_prob": 0.1, 
  "directionality": "bidi", 
  "hidden_act": "gelu", 
  "hidden_dropout_prob": 0.1, 
  "hidden_size": 768, 
  "initializer_range": 0.02, 
  "intermediate_size": 3072, 
  "max_position_embeddings": 512, 
  "num_attention_heads": 12, 
  "num_hidden_layers": 12, 
  "pooler_fc_size": 768, 
  "pooler_num_attention_heads": 12, 
  "pooler_num_fc_layers": 3, 
  "pooler_size_per_head": 128, 
  "pooler_type": "first_token_transform", 
  "type_vocab_size": 2, 
  "vocab_size": 21128
}


I change my code:
bert_config = BertConfig.from_json_file('bert_config.json')
bert_config.type_vocab_size=16
model=BertModel(bert_config)
model.load_state_dict(torch.load('pytorch_model.bin'))

it still error.<|||||>> I see you have `"type_vocab_size": 2` in your config file, how is that?

Yes,but I change it  in my code.<|||||>> is your `pytorch_model.bin` the good converted model of the chinese one (and not of an English one)?

I think it's good.<|||||>Ok, I have the models. I think `type_vocab_size` should be 2 also for chinese. I am wondering why it is 16 in your `pytorch_model.bin`<|||||>I have no idea.Did my model  make the wrong convert?<|||||>I am testing that right now. I haven't played with the multi-lingual models yet.<|||||>> I am testing that right now. I haven't played with the multi-lingual models yet.

I also use it for the first  time.I am looking forward to your test results.<|||||>> I am testing that right now. I haven't played with the multi-lingual models yet.

When I was converting the model .

Traceback (most recent call last):
  File "convert_tf_checkpoint_to_pytorch.py", line 95, in <module>
    convert()
  File "convert_tf_checkpoint_to_pytorch.py", line 85, in convert
    assert pointer.shape == array.shape
AssertionError: (torch.Size([16, 768]), (2, 768)) <|||||>are you supplying a config file with `"type_vocab_size": 2` to the conversion script?<|||||>> are you supplying a config file with `"type_vocab_size": 2` to the conversion script?

I used the 'bert_config.json' of the  chinese_L-12_H-768_A-12  when  I was  converting .<|||||>Ok, I think I found the issue, your BertConfig is not build from the configuration file for some reason and thus use the default value of `type_vocab_size` in BertConfig which is 16.

This error happen on my system when I use `config = BertConfig('bert_config.json')` instead of `config = BertConfig.from_json_file('bert_config.json')`.

I will make sure these two ways of initializing the configuration file (from parameters or from json file) cannot be messed up.<|||||>> 运行时错误：加载 BertModel state_dict时出错：embeddings.token_type_embeddings 的大小不匹配.weight：
> 复制火炬参数。大小（[16， 768]） 从检查点开始，其中形状为火炬。当前模型中的大小（[2， 768]

i have the same problem as you. did you solve the problem?