| --- |
| library_name: transformers |
| license: mit |
| base_model: AnonymousCS/populism_xlmr_large |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: populism_classifier_bsample_210 |
| 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_210 |
| |
| This model is a fine-tuned version of [AnonymousCS/populism_xlmr_large](https://huggingface.co/AnonymousCS/populism_xlmr_large) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.6223 |
| - Accuracy: 0.9705 |
| - 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:| |
| | 0.7017 | 1.0 | 8 | 0.7740 | 0.0295 | 0.0573 | 1.0 | 0.0295 | 0.5 | |
| | 0.6096 | 2.0 | 16 | 0.7458 | 0.0295 | 0.0573 | 1.0 | 0.0295 | 0.5 | |
| | 0.6461 | 3.0 | 24 | 0.7163 | 0.0295 | 0.0573 | 1.0 | 0.0295 | 0.5 | |
| | 0.7868 | 4.0 | 32 | 0.6938 | 0.0295 | 0.0573 | 1.0 | 0.0295 | 0.5 | |
| | 0.634 | 5.0 | 40 | 0.6722 | 0.9705 | 0.0 | 0.0 | 0.0 | 0.5 | |
| | 0.7419 | 6.0 | 48 | 0.6617 | 0.9705 | 0.0 | 0.0 | 0.0 | 0.5 | |
| | 0.8392 | 7.0 | 56 | 0.6498 | 0.9705 | 0.0 | 0.0 | 0.0 | 0.5 | |
| | 0.6242 | 8.0 | 64 | 0.6411 | 0.9705 | 0.0 | 0.0 | 0.0 | 0.5 | |
| | 0.7454 | 9.0 | 72 | 0.6365 | 0.9705 | 0.0 | 0.0 | 0.0 | 0.5 | |
| | 0.6543 | 10.0 | 80 | 0.6324 | 0.9705 | 0.0 | 0.0 | 0.0 | 0.5 | |
| | 0.6166 | 11.0 | 88 | 0.6287 | 0.9705 | 0.0 | 0.0 | 0.0 | 0.5 | |
| | 0.6474 | 12.0 | 96 | 0.6255 | 0.9705 | 0.0 | 0.0 | 0.0 | 0.5 | |
| | 0.742 | 13.0 | 104 | 0.6232 | 0.9705 | 0.0 | 0.0 | 0.0 | 0.5 | |
| | 0.5817 | 14.0 | 112 | 0.6223 | 0.9705 | 0.0 | 0.0 | 0.0 | 0.5 | |
| | 0.8097 | 15.0 | 120 | 0.6223 | 0.9705 | 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 |
|
|
