learning_rate: 1.5e-05
train_batch_size: 256
eval_batch_size: 256
seed: 42
optimizer: Use adamw_torch 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: 0.1
num_epochs: 3

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.2897	0.2569	500	0.3390	0.8773	0.5747	0.0282	0.0537
0.2437	0.5139	1000	0.3320	0.8789	0.5347	0.1568	0.2425
0.2292	0.7708	1500	0.3317	0.8826	0.5760	0.1901	0.2859
0.1915	1.0277	2000	0.3557	0.8820	0.5583	0.2164	0.3119
0.2146	1.2847	2500	0.3390	0.8837	0.5757	0.2250	0.3236
0.2222	1.5416	3000	0.3298	0.8811	0.5358	0.2819	0.3694
0.1861	1.7986	3500	0.3338	0.8823	0.5501	0.2620	0.3549
0.1789	2.0555	4000	0.3460	0.8811	0.5353	0.2849	0.3719
0.1739	2.3124	4500	0.3614	0.8850	0.5863	0.2368	0.3373
0.1899	2.5694	5000	0.3487	0.8844	0.5716	0.2578	0.3554
0.1692	2.8263	5500	0.3484	0.8847	0.5728	0.2653	0.3626

egerber1/classifier-de

classifier-de