MyPoliBERT-HITL-ver02

YagiASAFAS/MyPoliBERT-HITL-ver02

MyPoliBERT-HITL-ver02 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: Oct 11, 2025
Updated: Oct 11, 2025
SHA: 08718b11

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Base models

YagiASAFAS/MyPoliBERT-HITL

Model card

From the Hugging Face model README

MyPoliBERT-HITL-ver02

This model is a fine-tuned version of YagiASAFAS/MyPoliBERT-HITL on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.2656
Democracy F1: 0.9337
Democracy Accuracy: 0.9344
Economy F1: 0.9183
Economy Accuracy: 0.9190
Race F1: 0.9491
Race Accuracy: 0.9499
Leadership F1: 0.8503
Leadership Accuracy: 0.8490
Development F1: 0.8721
Development Accuracy: 0.8739
Corruption F1: 0.9487
Corruption Accuracy: 0.9499
Stability F1: 1.0
Stability Accuracy: 1.0
Safety F1: 0.9052
Safety Accuracy: 0.9047
Administration F1: 0.8831
Administration Accuracy: 0.8854
Education F1: 0.9592
Education Accuracy: 0.9599
Religion F1: 0.9436
Religion Accuracy: 0.9439
Environment F1: 0.9736
Environment Accuracy: 0.9740
Overall F1: 0.9281
Overall Accuracy: 0.9287

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
0.2312	1.0	1346	0.2204	0.9072	0.9261	0.9032	0.9110	0.9386	0.9393	0.7710	0.7987	0.8389	0.8564	0.9334	0.9396	1.0	1.0	0.8753	0.8860	0.8627	0.8845	0.9531	0.9554	0.9346	0.9361	0.9718	0.9721	0.9075	0.9171
0.1721	2.0	2692	0.1994	0.9287	0.9357	0.9116	0.9134	0.9456	0.9461	0.8359	0.8377	0.8622	0.8715	0.9412	0.9467	1.0	1.0	0.8985	0.9012	0.8829	0.8889	0.9575	0.9595	0.9394	0.9415	0.9705	0.9725	0.9228	0.9262
0.1109	3.0	4038	0.2045	0.9306	0.9315	0.9120	0.9108	0.9487	0.9499	0.8409	0.8416	0.8664	0.8683	0.9477	0.9493	1.0	1.0	0.9006	0.9006	0.8814	0.8813	0.9557	0.9554	0.9464	0.9476	0.9727	0.9731	0.9253	0.9258
0.0844	4.0	5384	0.2123	0.9345	0.9361	0.9147	0.9160	0.9491	0.9506	0.8378	0.8375	0.8716	0.8759	0.9475	0.9499	1.0	1.0	0.9028	0.9043	0.8820	0.8845	0.9567	0.9575	0.9420	0.9428	0.9726	0.9729	0.9259	0.9273
0.0605	5.0	6730	0.2309	0.9330	0.9342	0.9188	0.9212	0.9455	0.9447	0.8457	0.8447	0.8709	0.875	0.9480	0.9499	1.0	1.0	0.8997	0.8990	0.8787	0.8783	0.9587	0.9591	0.9406	0.9400	0.9725	0.9725	0.9260	0.9266
0.0413	6.0	8076	0.2465	0.9337	0.9337	0.9166	0.9168	0.9468	0.9482	0.8389	0.8436	0.8703	0.8700	0.9477	0.9502	1.0	1.0	0.9018	0.9014	0.8810	0.8900	0.9603	0.9610	0.9423	0.9426	0.9719	0.9723	0.9259	0.9275
0.0286	7.0	9422	0.2531	0.9331	0.9318	0.9177	0.9188	0.9485	0.9491	0.8458	0.8444	0.8740	0.8759	0.9463	0.9471	1.0	1.0	0.9022	0.9003	0.8850	0.8858	0.9564	0.9565	0.9434	0.9437	0.9737	0.9740	0.9272	0.9273
0.0209	8.0	10768	0.2618	0.9335	0.9322	0.9178	0.9168	0.9495	0.9499	0.8452	0.8432	0.8733	0.8744	0.9478	0.9497	1.0	1.0	0.9049	0.9042	0.8809	0.8806	0.9581	0.9586	0.9439	0.9439	0.9724	0.9727	0.9273	0.9272
0.0157	9.0	12114	0.2646	0.9342	0.9346	0.9174	0.9179	0.9497	0.9506	0.8528	0.8522	0.8728	0.8744	0.9465	0.9474	1.0	1.0	0.9048	0.9042	0.8819	0.8832	0.9594	0.9601	0.9448	0.9454	0.9739	0.9742	0.9282	0.9287
0.0147	10.0	13460	0.2656	0.9337	0.9344	0.9183	0.9190	0.9491	0.9499	0.8503	0.8490	0.8721	0.8739	0.9487	0.9499	1.0	1.0	0.9052	0.9047	0.8831	0.8854	0.9592	0.9599	0.9436	0.9439	0.9736	0.9740	0.9281	0.9287

Framework versions

Transformers 4.57.0
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.22.1

Task: Text Classification
Library: transformers
Type: bert
License: apache-2.0
Created: Oct 11, 2025
Updated: Oct 11, 2025