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Some weights of the model checkpoint at Rostlab/prot_bert were not used when initializing BertModel: ['cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.decoder.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.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).
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Model: ProtBERT
Using GPU
Traceback (most recent call last):
File "save_representations_frozen_full.py", line 73, in <module>
outputs = model(**inputs)
File "/home/h_ghazik/python_venv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/home/h_ghazik/python_venv/lib/python3.7/site-packages/transformers/models/bert/modeling_bert.py", line 1017, in forward
past_key_values_length=past_key_values_length,
File "/home/h_ghazik/python_venv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/home/h_ghazik/python_venv/lib/python3.7/site-packages/transformers/models/bert/modeling_bert.py", line 230, in forward
inputs_embeds = self.word_embeddings(input_ids)
File "/home/h_ghazik/python_venv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/home/h_ghazik/python_venv/lib/python3.7/site-packages/torch/nn/modules/sparse.py", line 162, in forward
self.norm_type, self.scale_grad_by_freq, self.sparse)
File "/home/h_ghazik/python_venv/lib/python3.7/site-packages/torch/nn/functional.py", line 2210, in embedding
return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument index in method wrapper__index_select)