learning_rate: 2e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
num_epochs: 20

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
0.6009	1.0	73	0.5947	0.436	0.6134	0.1369
0.5678	2.0	146	0.5712	0.4273	0.6124	0.1456
0.5116	3.0	219	0.4825	0.6053	0.7128	0.2877
0.4375	4.0	292	0.4319	0.6749	0.7600	0.3501
0.3723	5.0	365	0.3959	0.7178	0.7919	0.4073
0.2906	6.0	438	0.3896	0.7329	0.8001	0.4385
0.2457	7.0	511	0.4272	0.7291	0.8009	0.4125
0.2035	8.0	584	0.4255	0.7516	0.8198	0.4385
0.1546	9.0	657	0.4514	0.7462	0.8126	0.4402
0.1238	10.0	730	0.4732	0.7518	0.8155	0.4419
0.0896	11.0	803	0.5441	0.7437	0.8104	0.4506
0.0659	12.0	876	0.5355	0.7594	0.8228	0.4506
0.0459	13.0	949	0.5450	0.7582	0.8200	0.4714
0.0436	14.0	1022	0.5919	0.7525	0.8164	0.4558
0.0366	15.0	1095	0.6016	0.7588	0.8226	0.4541

sercetexam9/cs221-xlnet-large-cased-eng-finetuned-20-epochs-tapt

cs221-xlnet-large-cased-eng-finetuned-20-epochs-tapt