learning_rate: 5e-05
train_batch_size: 16
eval_batch_size: 16
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
lr_scheduler_warmup_steps: 500
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Democracy F1	Democracy Accuracy	Economy F1	Economy Accuracy	Race F1	Race Accuracy	Leadership F1	Leadership Accuracy	Development F1	Development Accuracy	Corruption F1	Corruption Accuracy	Stability F1	Stability Accuracy	Safety F1	Safety Accuracy	Administration F1	Administration Accuracy	Education F1	Education Accuracy	Religion F1	Religion Accuracy	Environment F1	Environment Accuracy	Overall F1	Overall Accuracy
No log	1.0	96	0.7728	0.7594	0.8346	0.8568	0.9029	0.8912	0.9265	0.6722	0.7717	0.9065	0.9370	0.8759	0.9160	0.8341	0.8871	0.8078	0.8688	0.7891	0.8556	0.9608	0.9738	0.8720	0.9134	0.9297	0.9528	0.8463	0.8950
1.3991	2.0	192	0.4467	0.7594	0.8346	0.8568	0.9029	0.8912	0.9265	0.6722	0.7717	0.9065	0.9370	0.8759	0.9160	0.8341	0.8871	0.8078	0.8688	0.7891	0.8556	0.9608	0.9738	0.8720	0.9134	0.9297	0.9528	0.8463	0.8950
0.5431	3.0	288	0.3804	0.7594	0.8346	0.8568	0.9029	0.8912	0.9265	0.6851	0.7769	0.9065	0.9370	0.8759	0.9160	0.8341	0.8871	0.8078	0.8688	0.7891	0.8556	0.9785	0.9843	0.8720	0.9134	0.9297	0.9528	0.8488	0.8963
0.4275	4.0	384	0.3278	0.7581	0.8320	0.8715	0.9055	0.8912	0.9265	0.7398	0.7874	0.9065	0.9370	0.8988	0.9239	0.8453	0.8740	0.8794	0.9029	0.7891	0.8556	0.9790	0.9843	0.8893	0.9213	0.9773	0.9816	0.8688	0.9027
0.35	5.0	480	0.3028	0.7711	0.8136	0.8820	0.9081	0.9250	0.9396	0.7487	0.7979	0.9117	0.9396	0.9167	0.9318	0.8570	0.8898	0.9019	0.9134	0.7891	0.8556	0.9693	0.9711	0.9093	0.9291	0.9709	0.9764	0.8794	0.9055
0.2846	6.0	576	0.2856	0.7767	0.8215	0.9040	0.9186	0.9183	0.9344	0.7716	0.8189	0.9240	0.9449	0.9140	0.9344	0.8518	0.8688	0.9053	0.9186	0.7891	0.8556	0.9771	0.9816	0.9317	0.9423	0.9758	0.9790	0.8866	0.9099
0.23	7.0	672	0.2759	0.7920	0.8373	0.8959	0.9134	0.9312	0.9423	0.7453	0.8058	0.9299	0.9475	0.9258	0.9423	0.8516	0.8871	0.9061	0.9213	0.7962	0.8583	0.9771	0.9816	0.9391	0.9475	0.9818	0.9843	0.8893	0.9140
0.1845	8.0	768	0.2677	0.7986	0.8425	0.8956	0.9081	0.9380	0.9449	0.7611	0.8110	0.9325	0.9475	0.9298	0.9449	0.8590	0.8898	0.9148	0.9239	0.8095	0.8556	0.9771	0.9816	0.9305	0.9423	0.9830	0.9843	0.8941	0.9147
0.1643	9.0	864	0.2664	0.8099	0.8451	0.8909	0.9081	0.9367	0.9449	0.7676	0.8163	0.9370	0.9475	0.9298	0.9449	0.8643	0.8898	0.9125	0.9213	0.8050	0.8530	0.9820	0.9843	0.9344	0.9449	0.9793	0.9816	0.8958	0.9151
0.1353	10.0	960	0.2658	0.8047	0.8399	0.8967	0.9108	0.9393	0.9475	0.7616	0.8084	0.9365	0.9475	0.9298	0.9449	0.8689	0.8924	0.9170	0.9265	0.8177	0.8635	0.9820	0.9843	0.9323	0.9423	0.9823	0.9843	0.8974	0.9160

Framework versions

Transformers 4.56.1
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.22.0

YagiASAFAS/MyPoliBERT-HITL

MyPoliBERT-HITL

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

YagiASAFAS/
MyPoliBERT-HITL