Model weights don't seem to load correctly while running the example

[kyleliang919]

The results I got also seem to be different from expectation. I am wondering if it's something to do with the below warning. Here is the logit, which is completely different from the example shown in README.

{'logits': tensor([[0.3603, 0.4735, 0.1625, 0.2380, 0.3830]], device='cuda:0',
       grad_fn=<StackBackward0>), 'class_names': ['Atelectasis', 'Cardiomegaly', 'Consolidation', 'Edema', 'Pleural Effusion']}

Some weights of the model checkpoint at microsoft/swin-tiny-patch4-window7-224 were not used when initializing SwinModel: ['classifier.weight', 'classifier.bias']
- This IS expected if you are initializing SwinModel 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 SwinModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of the model checkpoint at emilyalsentzer/Bio_ClinicalBERT were not used when initializing BertModel: ['cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight']
- 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).