learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 8
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_ratio: 0.06
num_epochs: 4

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	Accuracy Balanced	F1 Micro	Precision Macro	Recall Macro	Precision Micro	Recall Micro
0.4492	0.3053	500	0.5885	0.8289	0.7932	0.7853	0.8289	0.8034	0.7853	0.8289	0.8289
0.2917	0.6105	1000	0.4281	0.8523	0.8369	0.8615	0.8523	0.8252	0.8615	0.8523	0.8523
0.248	0.9158	1500	0.4235	0.8795	0.8584	0.8580	0.8795	0.8588	0.8580	0.8795	0.8795
0.181	1.2210	2000	0.6807	0.8487	0.8034	0.7794	0.8487	0.8558	0.7794	0.8487	0.8487
0.1562	1.5263	2500	0.5073	0.8823	0.8631	0.8664	0.8823	0.8601	0.8664	0.8823	0.8823
0.1696	1.8315	3000	0.8349	0.8257	0.7588	0.7313	0.8257	0.8563	0.7313	0.8257	0.8257
0.14	2.1368	3500	0.5772	0.8827	0.8567	0.8435	0.8827	0.8748	0.8435	0.8827	0.8827
0.0941	2.4420	4000	0.6805	0.8823	0.8603	0.8563	0.8823	0.8647	0.8563	0.8823	0.8823
0.0923	2.7473	4500	0.5840	0.8889	0.8679	0.8634	0.8889	0.8730	0.8634	0.8889	0.8889
0.0905	3.0525	5000	0.5933	0.8925	0.8721	0.8671	0.8925	0.8777	0.8671	0.8925	0.8925
0.0555	3.3578	5500	0.6134	0.8919	0.8702	0.8623	0.8919	0.8798	0.8623	0.8919	0.8919
0.0639	3.6630	6000	0.6264	0.8903	0.8681	0.8595	0.8903	0.8785	0.8595	0.8903	0.8903
0.0631	3.9683	6500	0.6027	0.8927	0.8743	0.8750	0.8927	0.8736	0.8750	0.8927	0.8927

mljn/mdeberta-v3-base-finetuned-green-classification-new

mdeberta-v3-base-finetuned-green-classification-new