MyPoliBERT-HITL

YagiASAFAS/MyPoliBERT-HITL

MyPoliBERT-HITL is a text classification model from YagiASAFAS. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Text Classification
Library: transformers
License: apache-2.0
Model type: bert
Access: Public
Created: Sep 27, 2025
Updated: Sep 27, 2025
SHA: afb4da7c

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Base models

Model card

From the Hugging Face model README

MyPoliBERT-HITL

This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.2658
Democracy F1: 0.8047
Democracy Accuracy: 0.8399
Economy F1: 0.8967
Economy Accuracy: 0.9108
Race F1: 0.9393
Race Accuracy: 0.9475
Leadership F1: 0.7616
Leadership Accuracy: 0.8084
Development F1: 0.9365
Development Accuracy: 0.9475
Corruption F1: 0.9298
Corruption Accuracy: 0.9449
Stability F1: 0.8689
Stability Accuracy: 0.8924
Safety F1: 0.9170
Safety Accuracy: 0.9265
Administration F1: 0.8177
Administration Accuracy: 0.8635
Education F1: 0.9820
Education Accuracy: 0.9843
Religion F1: 0.9323
Religion Accuracy: 0.9423
Environment F1: 0.9823
Environment Accuracy: 0.9843
Overall F1: 0.8974
Overall Accuracy: 0.9160

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

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

Task: Text Classification
Library: transformers
Type: bert
License: apache-2.0
Created: Sep 27, 2025
Updated: Sep 27, 2025