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library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: MyPoliBERT-HITL
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MyPoliBERT-HITL
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/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
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