results

ssh://mrwu@222.22.66.226:22/home2/mrwu/anaconda3/bin/python -u /tmp/pycharm_project_521/main.py
Some weights of the model checkpoint at bert-base-chinese were not used when initializing BertModel: ['cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.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).
Some weights of the model checkpoint at bert-base-chinese were not used when initializing BertModel: ['cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.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).
Epoch 1/5: Train Loss=0.544, Train Acc=0.658, Val Loss=0.566, Val Acc=0.816
Test Loss: 0.566, Test Acc: 0.816
Epoch 2/5: Train Loss=0.562, Train Acc=0.782, Val Loss=0.469, Val Acc=0.816
Test Loss: 0.469, Test Acc: 0.816
Epoch 3/5: Train Loss=0.453, Train Acc=0.808, Val Loss=0.351, Val Acc=0.898
Test Loss: 0.351, Test Acc: 0.898
Epoch 4/5: Train Loss=0.392, Train Acc=0.876, Val Loss=0.347, Val Acc=0.918
Test Loss: 0.347, Test Acc: 0.918
Epoch 5/5: Train Loss=0.351, Train Acc=0.855, Val Loss=0.274, Val Acc=0.898
Test Loss: 0.274, Test Acc: 0.898
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Sentiment: Positive, Probability: 0.910

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