| --- |
| library_name: transformers |
| license: mit |
| base_model: AnonymousCS/populism_xlmr_base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: populism_classifier_bsample_201 |
| 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. --> |
|
|
| # populism_classifier_bsample_201 |
| |
| This model is a fine-tuned version of [AnonymousCS/populism_xlmr_base](https://huggingface.co/AnonymousCS/populism_xlmr_base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.6825 |
| - Accuracy: 0.9606 |
| - 1-f1: 0.0 |
| - 1-recall: 0.0 |
| - 1-precision: 0.0 |
| - Balanced Acc: 0.5 |
| |
| ## 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: 1e-06 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.06 |
| - num_epochs: 15 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:| |
| | 0.7309 | 1.0 | 11 | 0.7579 | 0.0394 | 0.0758 | 1.0 | 0.0394 | 0.5 | |
| | 0.7063 | 2.0 | 22 | 0.7470 | 0.0394 | 0.0758 | 1.0 | 0.0394 | 0.5 | |
| | 0.7109 | 3.0 | 33 | 0.7348 | 0.0394 | 0.0758 | 1.0 | 0.0394 | 0.5 | |
| | 0.7686 | 4.0 | 44 | 0.7249 | 0.0394 | 0.0758 | 1.0 | 0.0394 | 0.5 | |
| | 0.7522 | 5.0 | 55 | 0.7168 | 0.0394 | 0.0758 | 1.0 | 0.0394 | 0.5 | |
| | 0.7134 | 6.0 | 66 | 0.7091 | 0.0394 | 0.0758 | 1.0 | 0.0394 | 0.5 | |
| | 0.7229 | 7.0 | 77 | 0.7028 | 0.0394 | 0.0758 | 1.0 | 0.0394 | 0.5 | |
| | 0.6546 | 8.0 | 88 | 0.6987 | 0.0394 | 0.0758 | 1.0 | 0.0394 | 0.5 | |
| | 0.6958 | 9.0 | 99 | 0.6947 | 0.0394 | 0.0758 | 1.0 | 0.0394 | 0.5 | |
| | 0.7401 | 10.0 | 110 | 0.6906 | 0.9606 | 0.0 | 0.0 | 0.0 | 0.5 | |
| | 0.7234 | 11.0 | 121 | 0.6875 | 0.9606 | 0.0 | 0.0 | 0.0 | 0.5 | |
| | 0.681 | 12.0 | 132 | 0.6848 | 0.9606 | 0.0 | 0.0 | 0.0 | 0.5 | |
| | 0.7031 | 13.0 | 143 | 0.6834 | 0.9606 | 0.0 | 0.0 | 0.0 | 0.5 | |
| | 0.7209 | 14.0 | 154 | 0.6830 | 0.9606 | 0.0 | 0.0 | 0.0 | 0.5 | |
| | 0.6832 | 15.0 | 165 | 0.6825 | 0.9606 | 0.0 | 0.0 | 0.0 | 0.5 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.46.3 |
| - Pytorch 2.4.1+cu121 |
| - Datasets 3.1.0 |
| - Tokenizers 0.20.3 |
|
|
