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---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: MSPoliBERT-12
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. -->
# MSPoliBERT-12
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: 0.2936
- Democracy F1: 0.9392
- Democracy Accuracy: 0.9426
- Economy F1: 0.9141
- Economy Accuracy: 0.9156
- Race F1: 0.9303
- Race Accuracy: 0.9331
- Leadership F1: 0.7696
- Leadership Accuracy: 0.7688
- Development F1: 0.8747
- Development Accuracy: 0.8790
- Corruption F1: 0.9411
- Corruption Accuracy: 0.9441
- Instability F1: 0.9093
- Instability Accuracy: 0.9141
- Safety F1: 0.9291
- Safety Accuracy: 0.9305
- Administration F1: 0.8768
- Administration Accuracy: 0.8853
- Education F1: 0.9538
- Education Accuracy: 0.9557
- Religion F1: 0.9338
- Religion Accuracy: 0.9349
- Environment F1: 0.9807
- Environment Accuracy: 0.9819
- Overall F1: 0.9127
- Overall Accuracy: 0.9155
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- 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 | Instability F1 | Instability 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------------:|:----------:|:----------------:|:-------:|:-------------:|:-------------:|:-------------------:|:--------------:|:--------------------:|:-------------:|:-------------------:|:--------------:|:--------------------:|:---------:|:---------------:|:-----------------:|:-----------------------:|:------------:|:------------------:|:-----------:|:-----------------:|:--------------:|:--------------------:|:----------:|:----------------:|
| 0.4282 | 1.0 | 841 | 0.2914 | 0.9080 | 0.9293 | 0.8960 | 0.9088 | 0.9066 | 0.9221 | 0.7142 | 0.7328 | 0.8409 | 0.8585 | 0.9253 | 0.9287 | 0.9013 | 0.9076 | 0.9076 | 0.9097 | 0.8349 | 0.8651 | 0.9376 | 0.9483 | 0.9147 | 0.9233 | 0.9671 | 0.9744 | 0.8878 | 0.9007 |
| 0.2346 | 2.0 | 1682 | 0.2568 | 0.9172 | 0.9364 | 0.9016 | 0.9105 | 0.9172 | 0.9254 | 0.7547 | 0.7652 | 0.8586 | 0.8648 | 0.9265 | 0.9346 | 0.8974 | 0.9111 | 0.9272 | 0.9296 | 0.8539 | 0.8802 | 0.9451 | 0.9519 | 0.9264 | 0.9308 | 0.9767 | 0.9786 | 0.9002 | 0.9099 |
| 0.1601 | 3.0 | 2523 | 0.2519 | 0.9260 | 0.9355 | 0.9108 | 0.9186 | 0.9228 | 0.9278 | 0.7575 | 0.7620 | 0.8748 | 0.8808 | 0.9360 | 0.9415 | 0.9067 | 0.9135 | 0.9285 | 0.9316 | 0.8609 | 0.8799 | 0.9518 | 0.9560 | 0.9301 | 0.9337 | 0.9801 | 0.9810 | 0.9072 | 0.9135 |
| 0.1169 | 4.0 | 3364 | 0.2627 | 0.9315 | 0.9412 | 0.9120 | 0.9192 | 0.9214 | 0.9284 | 0.7637 | 0.7646 | 0.8757 | 0.8799 | 0.9411 | 0.9459 | 0.9071 | 0.9123 | 0.9296 | 0.9328 | 0.8685 | 0.8820 | 0.9512 | 0.9542 | 0.9335 | 0.9364 | 0.9802 | 0.9810 | 0.9096 | 0.9148 |
| 0.0798 | 5.0 | 4205 | 0.2729 | 0.9368 | 0.9412 | 0.9129 | 0.9159 | 0.9284 | 0.9328 | 0.7642 | 0.7652 | 0.8760 | 0.8799 | 0.9414 | 0.9435 | 0.9078 | 0.9126 | 0.9277 | 0.9290 | 0.8703 | 0.8743 | 0.9565 | 0.9581 | 0.9323 | 0.9349 | 0.9799 | 0.9801 | 0.9112 | 0.9140 |
| 0.0565 | 6.0 | 5046 | 0.2821 | 0.9357 | 0.9403 | 0.9144 | 0.9159 | 0.9266 | 0.9284 | 0.7687 | 0.7685 | 0.8748 | 0.8785 | 0.9384 | 0.9403 | 0.9115 | 0.9153 | 0.9266 | 0.9299 | 0.8693 | 0.8814 | 0.9557 | 0.9578 | 0.9321 | 0.9325 | 0.9790 | 0.9813 | 0.9111 | 0.9142 |
| 0.0443 | 7.0 | 5887 | 0.2914 | 0.9375 | 0.9406 | 0.9150 | 0.9156 | 0.9293 | 0.9322 | 0.7719 | 0.7715 | 0.8727 | 0.8767 | 0.9412 | 0.9447 | 0.9103 | 0.9144 | 0.9292 | 0.9316 | 0.8761 | 0.8832 | 0.9558 | 0.9569 | 0.9322 | 0.9334 | 0.9797 | 0.9813 | 0.9126 | 0.9152 |
| 0.0361 | 8.0 | 6728 | 0.2936 | 0.9392 | 0.9426 | 0.9141 | 0.9156 | 0.9303 | 0.9331 | 0.7696 | 0.7688 | 0.8747 | 0.8790 | 0.9411 | 0.9441 | 0.9093 | 0.9141 | 0.9291 | 0.9305 | 0.8768 | 0.8853 | 0.9538 | 0.9557 | 0.9338 | 0.9349 | 0.9807 | 0.9819 | 0.9127 | 0.9155 |
### Framework versions
- Transformers 4.18.0
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.12.1
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