Upload training results

.gitattributes

@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Training[[:space:]]Results/attack-train-2.csv filter=lfs diff=lfs merge=lfs -text
+Training[[:space:]]Results/attack-train-2.txt filter=lfs diff=lfs merge=lfs -text
+Training[[:space:]]Results/attack-train-3.csv filter=lfs diff=lfs merge=lfs -text
+Training[[:space:]]Results/attack-train-3.txt filter=lfs diff=lfs merge=lfs -text

Training Results/attack-train-2.csv

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Training Results/train_log.txt

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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...

Training Results/training_args.json

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+{"num_epochs": 5, "num_clean_epochs": 1, "attack_epoch_interval": 1, "early_stopping_epochs": null, "learning_rate": 5e-05, "num_warmup_steps": 500, "weight_decay": 0.01, "per_device_train_batch_size": 8, "per_device_eval_batch_size": 32, "gradient_accumulation_steps": 4, "random_seed": 786, "parallel": false, "load_best_model_at_end": false, "alpha": 1.0, "num_train_adv_examples": 4000, "query_budget_train": null, "attack_num_workers_per_device": 1, "output_dir": "/home/ubuntu/buildsCodes/Adversarial_training/trained_models/Multi-delete-our_bert-base-uncased-IMDB/", "checkpoint_interval_steps": null, "checkpoint_interval_epochs": 1, "save_last": true, "log_to_tb": true, "tb_log_dir": null, "log_to_wandb": false, "wandb_project": "textattack", "logging_interval_step": 1}