Some weights of the model checkpoint at ./model_hub/chinese-bert-wwm-ext/ were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.seq_relationship.weight', '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).
D:\ANACONDA\envs\py38nlp\lib\site-packages\torch\nn\modules\rnn.py:58: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.1 and num_layers=1
warnings.warn("dropout option adds dropout after all but last "
D:\ANACONDA\envs\py38nlp\lib\site-packages\transformers\optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set no_deprecation_warning=True to disable this warning
warnings.warn(
Traceback (most recent call last):
File "E:/DataProcess/ner/main.py", line 189, in
main(data_name)
File "E:/DataProcess/ner/main.py", line 183, in main
report = train.test()
File "E:/DataProcess/ner/main.py", line 66, in test
self.model.load_state_dict(torch.load(os.path.join(self.output_dir, "pytorch_model_ner.bin")))
File "D:\ANACONDA\envs\py38nlp\lib\site-packages\torch\serialization.py", line 594, in load
return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
File "D:\ANACONDA\envs\py38nlp\lib\site-packages\torch\serialization.py", line 853, in _load
result = unpickler.load()
File "D:\ANACONDA\envs\py38nlp\lib\site-packages\torch\serialization.py", line 845, in persistent_load
load_tensor(data_type, size, key, _maybe_decode_ascii(location))
File "D:\ANACONDA\envs\py38nlp\lib\site-packages\torch\serialization.py", line 834, in load_tensor
loaded_storages[key] = restore_location(storage, location)
File "D:\ANACONDA\envs\py38nlp\lib\site-packages\torch\serialization.py", line 175, in default_restore_location
result = fn(storage, location)
File "D:\ANACONDA\envs\py38nlp\lib\site-packages\torch\serialization.py", line 151, in _cuda_deserialize
device = validate_cuda_device(location)
File "D:\ANACONDA\envs\py38nlp\lib\site-packages\torch\serialization.py", line 135, in validate_cuda_device
raise RuntimeError('Attempting to deserialize object on a CUDA '
RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.
出现这种问题，大佬可以帮忙看以下吗？
JoyeIEC
commented
1 month ago
Some weights of the model checkpoint at ./model_hub/chinese-bert-wwm-ext/ were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias']
no_deprecation_warning=Trueto disable this warning warnings.warn( Traceback (most recent call last): File "E:/DataProcess/ner/main.py", line 189, in