learning_rate: 7e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
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_steps: 500
num_epochs: 16
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Regionalism F1	Regionalism Accuracy	Clientelism F1	Clientelism Accuracy	Economic Policy F1	Economic Policy Accuracy	Security F1	Security Accuracy	Discipline Among Poor F1	Discipline Among Poor Accuracy	Populism F1	Populism Accuracy	Marcos Duterte Alliance F1	Marcos Duterte Alliance Accuracy	Uniteam Positive Campaign F1	Uniteam Positive Campaign Accuracy	Overall F1	Overall Accuracy
0.6703	1.0	250	0.4869	0.9487	0.9635	0.8803	0.883	0.8468	0.856	0.8181	0.8405	0.9647	0.9695	0.5545	0.6345	0.8339	0.839	0.6386	0.701	0.8107	0.8359
0.2993	2.0	500	0.2892	0.9746	0.977	0.9420	0.9465	0.9424	0.9435	0.9245	0.9335	0.9713	0.975	0.7850	0.792	0.8923	0.905	0.8420	0.854	0.9092	0.9158
0.2011	3.0	750	0.2276	0.9692	0.9705	0.9513	0.9535	0.9488	0.949	0.9504	0.9535	0.9743	0.976	0.8702	0.8705	0.9290	0.9355	0.8991	0.9	0.9366	0.9386
0.143	4.0	1000	0.2217	0.9803	0.9805	0.9568	0.9575	0.9496	0.9495	0.9555	0.9575	0.9722	0.9725	0.8834	0.884	0.9308	0.9365	0.8906	0.8905	0.9399	0.9411
0.1029	5.0	1250	0.2258	0.9781	0.9785	0.9579	0.9595	0.9505	0.9515	0.9483	0.952	0.9769	0.978	0.8945	0.894	0.9355	0.9415	0.8867	0.8885	0.9410	0.9429
0.0865	6.0	1500	0.2201	0.9795	0.98	0.9477	0.9475	0.9468	0.9455	0.9559	0.958	0.9776	0.978	0.9078	0.9075	0.9255	0.9305	0.8936	0.892	0.9418	0.9424
0.0796	7.0	1750	0.2157	0.9771	0.976	0.9605	0.961	0.9579	0.958	0.9559	0.9575	0.9715	0.9745	0.9116	0.9115	0.9422	0.9465	0.8929	0.8935	0.9462	0.9473
0.0702	8.0	2000	0.2149	0.9797	0.9795	0.9559	0.9565	0.9429	0.9405	0.9545	0.9565	0.9717	0.9735	0.8992	0.8985	0.9403	0.9445	0.9023	0.903	0.9433	0.9441
0.0685	9.0	2250	0.2115	0.9786	0.9775	0.9601	0.961	0.9521	0.952	0.9602	0.962	0.9767	0.9775	0.9020	0.9015	0.9447	0.9485	0.8936	0.894	0.9460	0.9467

Framework versions

Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 2.14.4
Tokenizers 0.21.1

YagiASAFAS/PhilippinesPoliBERT

PhilippinesPoliBERT

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

YagiASAFAS/
PhilippinesPoliBERT