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Writing logs to /home/ubuntu/buildsCodes/Adversarial_training/trained_models/Multi-delete-our_bert-base-uncased-IMDB/train_log.txt. Wrote original training args to /home/ubuntu/buildsCodes/Adversarial_training/trained_models/Multi-delete-our_bert-base-uncased-IMDB/training_args.json. ***** Running training ***** Num examples = 25000 Num epochs = 5 Num clean epochs = 1 Instantaneous batch size per device = 8 Total train batch size (w. parallel, distributed & accumulation) = 32 Gradient accumulation steps = 4 Total optimization steps = 4410 ========================================================== Epoch 1 Running clean epoch 1/1 Writing logs to /home/ubuntu/buildsCodes/Adversarial_training/trained_models/Multi-delete-our_bert-base-uncased-IMDB/train_log.txt. Wrote original training args to /home/ubuntu/buildsCodes/Adversarial_training/trained_models/Multi-delete-our_bert-base-uncased-IMDB/training_args.json. ***** Running training ***** Num examples = 25000 Num epochs = 5 Num clean epochs = 1 Instantaneous batch size per device = 8 Total train batch size (w. parallel, distributed & accumulation) = 32 Gradient accumulation steps = 4 Total optimization steps = 4410 ========================================================== Epoch 1 Running clean epoch 1/1 Writing logs to /home/ubuntu/buildsCodes/Adversarial_training/trained_models/Multi-delete-our_bert-base-uncased-IMDB/train_log.txt. Wrote original training args to /home/ubuntu/buildsCodes/Adversarial_training/trained_models/Multi-delete-our_bert-base-uncased-IMDB/training_args.json. ***** Running training ***** Num examples = 25000 Num epochs = 5 Num clean epochs = 1 Instantaneous batch size per device = 8 Total train batch size (w. parallel, distributed & accumulation) = 32 Gradient accumulation steps = 4 Total optimization steps = 4410 ========================================================== Epoch 1 Running clean epoch 1/1 Writing logs to /home/ubuntu/buildsCodes/Adversarial_training/trained_models/Multi-delete-our_bert-base-uncased-IMDB/train_log.txt. Wrote original training args to /home/ubuntu/buildsCodes/Adversarial_training/trained_models/Multi-delete-our_bert-base-uncased-IMDB/training_args.json. ***** Running training ***** Num examples = 25000 Num epochs = 5 Num clean epochs = 1 Instantaneous batch size per device = 8 Total train batch size (w. parallel, distributed & accumulation) = 32 Gradient accumulation steps = 4 Total optimization steps = 4410 ========================================================== Epoch 1 Running clean epoch 1/1 Train accuracy: 97.48% Eval accuracy: 90.31% Best score found. Saved model to /home/ubuntu/buildsCodes/Adversarial_training/trained_models/Multi-delete-our_bert-base-uncased-IMDB//best_model/ ========================================================== Epoch 2 Attacking model to generate new adversarial training set... Total number of attack results: 4403 Attack success rate: 91.43% [4000 / 4375] Train accuracy: 98.84% Eval accuracy: 93.46% Best score found. Saved model to /home/ubuntu/buildsCodes/Adversarial_training/trained_models/Multi-delete-our_bert-base-uncased-IMDB//best_model/ ========================================================== Epoch 3 Attacking model to generate new adversarial training set... Writing logs to /home/ubuntu/buildsCodes/Adversarial_training/trained_models/Multi-delete-our_bert-base-uncased-IMDB/train_log.txt. Wrote original training args to /home/ubuntu/buildsCodes/Adversarial_training/trained_models/Multi-delete-our_bert-base-uncased-IMDB/training_args.json. ***** Running training ***** Num examples = 25000 Num epochs = 5 Num clean epochs = 1 Instantaneous batch size per device = 8 Total train batch size (w. parallel, distributed & accumulation) = 32 Gradient accumulation steps = 4 Total optimization steps = 4410 ========================================================== Epoch 1 Running clean epoch 1/1 Train accuracy: 97.48% Eval accuracy: 90.31% Best score found. Saved model to /home/ubuntu/buildsCodes/Adversarial_training/trained_models/Multi-delete-our_bert-base-uncased-IMDB//best_model/ ========================================================== Epoch 2 Attacking model to generate new adversarial training set... Train accuracy: 98.89% Eval accuracy: 93.25% Best score found. Saved model to /home/ubuntu/buildsCodes/Adversarial_training/trained_models/Multi-delete-our_bert-base-uncased-IMDB//best_model/ ========================================================== Epoch 3 Attacking model to generate new adversarial training set... Total number of attack results: 6088 Attack success rate: 65.77% [4000 / 6082] Train accuracy: 70.22% Eval accuracy: 93.25% ========================================================== Epoch 4 Attacking model to generate new adversarial training set... |