runtime error

['Negative', 'Source', 'Target', 'Positive'] Some weights of the model checkpoint at models/encoder were not used when initializing BertModel: ['cls.seq_relationship.weight', 'bert.pooler.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'bert.pooler.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.bias', 'cls.predictions.decoder.bias', '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). Traceback (most recent call last): File "app.py", line 6, in <module> model = model_wrapper.PredictionModel() File "/home/user/app/model_wrapper.py", line 38, in __init__ self.model.load_state_dict(self.checkpoint["model"]) File "/home/user/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 2041, in load_state_dict raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for Model: Unexpected key(s) in state_dict: "encoder.bert.embeddings.position_ids".

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