learning_rate: 2e-05
train_batch_size: 32
eval_batch_size: 8
seed: 3407
optimizer: Use OptimizerNames.ADAMW_8BIT 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: 50
num_epochs: 20

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision Macro	Recall Macro	F1 Macro	Precision Weighted	Recall Weighted	F1 Weighted
1.6421	0.4	20	1.6189	0.2796	0.0665	0.2033	0.0990	0.0876	0.2796	0.1330
1.6176	0.8	40	1.6047	0.3552	0.1424	0.2711	0.1863	0.1879	0.3552	0.2453
1.5261	1.2	60	1.5614	0.3955	0.4059	0.3849	0.3373	0.4669	0.3955	0.3549
1.5492	1.6	80	1.4296	0.4358	0.4566	0.4123	0.3854	0.5207	0.4358	0.4266
1.0851	2.0	100	1.3699	0.4685	0.4874	0.4543	0.4266	0.5473	0.4685	0.4621
1.1487	2.4	120	1.1921	0.5491	0.5222	0.5344	0.5065	0.5925	0.5491	0.5486
1.2638	2.8	140	1.2074	0.5642	0.5496	0.5483	0.5117	0.6262	0.5642	0.5538
1.1191	3.2	160	1.1625	0.5441	0.5483	0.5519	0.5229	0.6081	0.5441	0.5614
0.6616	3.6	180	1.1361	0.6045	0.5685	0.5905	0.5706	0.6323	0.6045	0.6120
1.213	4.0	200	1.1492	0.5919	0.5518	0.5598	0.5485	0.5965	0.5919	0.5882
0.7761	4.4	220	1.1632	0.5970	0.5556	0.5662	0.5545	0.6102	0.5970	0.5959
0.6592	4.8	240	1.1926	0.5844	0.5521	0.5796	0.5575	0.6060	0.5844	0.5896

IMA-StreamSolve/sentiment-analysis-roberta-base-V1.6_ima_ds

sentiment-analysis-roberta-base-V1.6_ima_ds