learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 5
mixed_precision_training: Native AMP

Training Loss	Epoch	Step	Accuracy	F1	Validation Loss	Precision	Recall
0.1381	0.128	4000	0.9586	0.9586	0.1627	0.9599	0.9586
0.0821	0.256	8000	0.9761	0.9761	0.1081	0.9761	0.9761
0.0667	0.384	12000	0.9786	0.9786	0.1008	0.9787	0.9786
0.0754	0.512	16000	0.9820	0.9820	0.0779	0.9821	0.9820
0.0776	0.64	20000	0.9846	0.9846	0.0617	0.9847	0.9846
0.0643	0.768	24000	0.9831	0.9831	0.0761	0.9832	0.9831
0.064	0.896	28000	0.9878	0.9878	0.0495	0.9878	0.9878
0.0477	1.024	32000	0.9879	0.9879	0.0480	0.9880	0.9879
0.0427	1.152	36000	0.9894	0.9894	0.0424	0.9894	0.9894
0.0381	1.28	40000	0.9880	0.9880	0.0484	0.9880	0.9880
0.0423	1.408	44000	0.9901	0.9901	0.0399	0.9901	0.9901
0.0389	1.536	48000	0.9888	0.9888	0.0513	0.9889	0.9888
0.0416	1.6640	52000	0.9908	0.9908	0.0358	0.9908	0.9908
0.0374	1.792	56000	0.0370	0.9905	0.9905	0.9905	0.9905
0.0441	1.92	60000	0.0355	0.9905	0.9905	0.9905	0.9905
0.0358	2.048	64000	0.0384	0.9907	0.9907	0.9907	0.9907

azherali/CodeGenDetect-CodeBert_Lora

CodeGenDetect-CodeBert_Lora