learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 8
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
lr_scheduler_warmup_ratio: 0.06
num_epochs: 4

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	Accuracy Balanced	F1 Micro	Precision Macro	Recall Macro	Precision Micro	Recall Micro
0.5053	0.5663	500	0.3745	0.8924	0.8911	0.8953	0.8924	0.8892	0.8953	0.8924	0.8924
0.3056	1.1325	1000	0.3315	0.9151	0.9139	0.9174	0.9151	0.9119	0.9174	0.9151	0.9151
0.2184	1.6988	1500	0.3414	0.9264	0.9253	0.9280	0.9264	0.9234	0.9280	0.9264	0.9264
0.2006	2.2650	2000	0.3502	0.9298	0.9279	0.9255	0.9298	0.9310	0.9255	0.9298	0.9298
0.1464	2.8313	2500	0.3143	0.9337	0.9323	0.9321	0.9337	0.9325	0.9321	0.9337	0.9337
0.1177	3.3975	3000	0.3550	0.9349	0.9332	0.9313	0.9349	0.9356	0.9313	0.9349	0.9349
0.0992	3.9638	3500	0.3534	0.9337	0.9322	0.9309	0.9337	0.9336	0.9309	0.9337	0.9337

mljn/mdeberta-v3-base-finetuned-climate-vs-environment-classification

mdeberta-v3-base-finetuned-climate-vs-environment-classification