learning_rate: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 15

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
1.0496	1.0	1850	0.9913	0.5331	0.4339	0.5863	0.4787
0.9587	2.0	3700	0.9192	0.5672	0.5225	0.5576	0.5306
0.9183	3.0	5550	0.8934	0.5730	0.5267	0.5688	0.5357
0.9016	4.0	7400	0.8801	0.5879	0.5683	0.5701	0.5705
0.891	5.0	9250	0.8707	0.5863	0.5475	0.5795	0.5521
0.8842	6.0	11100	0.8623	0.5963	0.5758	0.5812	0.5752
0.8811	7.0	12950	0.8563	0.6006	0.5776	0.5849	0.5776
0.8761	8.0	14800	0.8532	0.6016	0.5815	0.5872	0.5805
0.874	9.0	16650	0.8520	0.5977	0.5670	0.5940	0.5679
0.8733	10.0	18500	0.8492	0.6021	0.5782	0.5931	0.5770
0.8717	11.0	20350	0.8466	0.6031	0.5787	0.5934	0.5777
0.8691	12.0	22200	0.8444	0.6063	0.5829	0.5932	0.5822
0.8675	13.0	24050	0.8448	0.6060	0.5849	0.5950	0.5833
0.866	14.0	25900	0.8434	0.6051	0.5794	0.5947	0.5788
0.8672	15.0	27750	0.8429	0.6067	0.5847	0.5939	0.5836

masa-research/me5-small-preskripsi-multiclass

me5-small-preskripsi-multiclass