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policy-politics
luissattelmayer/policy-politics
policy-politics is a text classification model from luissattelmayer. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Oct 10, 2025
- Updated
- Oct 10, 2025
- SHA
- 15c474e3
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Base models
Model card
From the Hugging Face model README
<!-- 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. -->
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This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2418
- F1: 0.9725
- Precision: 0.9690
- Recall: 0.9760
| lang | f1 | precision | recall | prevalence | n |
|---|---|---|---|---|---|
| cy | 1.000000 | 1.000000 | 1.000000 | 0.812500 | 16 |
| hu | 1.000000 | 1.000000 | 1.000000 | 0.529412 | 17 |
| sv | 0.987552 | 0.975410 | 1.000000 | 0.868613 | 137 |
| no | 0.980392 | 1.000000 | 0.961538 | 0.666667 | 39 |
| de | 0.977853 | 0.972881 | 0.982877 | 0.630670 | 463 |
| da | 0.974619 | 0.989691 | 0.960000 | 0.800000 | 125 |
| en | 0.974138 | 0.974138 | 0.974138 | 0.758170 | 153 |
| it | 0.971429 | 0.980769 | 0.962264 | 0.670886 | 79 |
| fr | 0.971429 | 0.944444 | 1.000000 | 0.566667 | 30 |
| es | 0.967742 | 0.937500 | 1.000000 | 0.714286 | 21 |
| nl | 0.966507 | 0.961905 | 0.971154 | 0.675325 | 154 |
| fi | 0.960000 | 0.923077 | 1.000000 | 0.750000 | 16 |
| pl | 0.936170 | 0.956522 | 0.916667 | 0.592593 | 81 |
| cs | 0.933333 | 0.875000 | 1.000000 | 0.411765 | 17 |
| pt | 0.833333 | 0.714286 | 1.000000 | 0.454545 | 22 |
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 1234
- 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall |
|---|---|---|---|---|---|---|
| 0.2087 | 0.9989 | 437 | 0.1219 | 0.9751 | 0.9806 | 0.9696 |
| 0.1227 | 2.0 | 875 | 0.1591 | 0.9724 | 0.9868 | 0.9584 |
| 0.0943 | 2.9989 | 1312 | 0.1554 | 0.9744 | 0.9759 | 0.9728 |
| 0.0465 | 4.0 | 1750 | 0.2055 | 0.9732 | 0.9868 | 0.96 |
| 0.0339 | 4.9943 | 2185 | 0.1788 | 0.9759 | 0.9791 | 0.9728 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.20.3
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Oct 10, 2025
- Updated
- Oct 10, 2025