learning_rate: 2e-05
train_batch_size: 32
eval_batch_size: 32
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: cosine
lr_scheduler_warmup_steps: 0.1
num_epochs: 6
label_smoothing_factor: 0.05

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro
3.6869	0.6369	100	3.4232	0.1955	0.0818
1.4881	1.2739	200	1.5163	0.1966	0.0822
1.4256	1.9108	300	1.3756	0.1966	0.0822
1.3842	2.5478	400	1.3779	0.5085	0.1685
1.3608	3.1847	500	1.3413	0.5085	0.1685
1.3480	3.8217	600	1.3528	0.5085	0.1685
1.3431	4.4586	700	1.3453	0.5085	0.1685
1.3597	5.0955	800	1.3448	0.5085	0.1685
1.3451	5.7325	900	1.3430	0.5085	0.1685
1.3451	6.0	942	1.3429	0.5085	0.1685

MMQuan/deberta-v3-ielts-node-classifier-v3

deberta-v3-ielts-node-classifier-v3