learning_rate: 3e-05
train_batch_size: 64
eval_batch_size: 64
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
num_epochs: 5

It achieves the following results on the evaluation set:

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	Low Precision	Low Recall	Low F1	Medium Precision	Medium Recall	Medium F1	High Precision	High Recall	High F1	Critical Precision	Critical Recall	Critical F1
3.0373	1.0	1167	3.0503	0.6895	0.5626	0.7959	0.1099	0.1931	0.7233	0.7958	0.7578	0.6083	0.5152	0.5579	0.6947	0.7954	0.7416
2.9084	2.0	2334	2.8601	0.7142	0.6048	0.8	0.1803	0.2943	0.7523	0.8001	0.7754	0.6923	0.5156	0.5910	0.6660	0.8807	0.7584
2.5937	3.0	3501	2.6529	0.7335	0.6349	0.6967	0.2394	0.3564	0.7565	0.8379	0.7952	0.7126	0.5411	0.6152	0.7092	0.8488	0.7727
2.5230	4.0	4668	2.6348	0.7365	0.6549	0.6170	0.3268	0.4273	0.7403	0.8568	0.7943	0.7208	0.5451	0.6207	0.7526	0.8038	0.7773
2.0599	5.0	5835	2.6495	0.7417	0.6650	0.6154	0.3380	0.4364	0.7619	0.8312	0.7951	0.6869	0.6080	0.6450	0.7678	0.7996	0.7834

CIRCL/vulnerability-severity-classification-russian-ruRoberta-large

VLAI: Automated Vulnerability Severity Classification (Russian Text)