| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google-bert/bert-base-multilingual-cased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| - recall |
| model-index: |
| - name: populism_model3 |
| 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_model3 |
| |
| This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.6057 |
| - Accuracy: 0.9272 |
| - F1: 0.4842 |
| - Recall: 0.5565 |
| |
| ## 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-05 |
| - train_batch_size: 64 |
| - eval_batch_size: 64 |
| - 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 |
| - num_epochs: 5 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:| |
| | No log | 1.0 | 127 | 0.4054 | 0.8524 | 0.3817 | 0.7419 | |
| | No log | 2.0 | 254 | 0.3590 | 0.8341 | 0.3853 | 0.8468 | |
| | No log | 3.0 | 381 | 0.3850 | 0.8816 | 0.4429 | 0.7661 | |
| | 0.3487 | 4.0 | 508 | 0.6005 | 0.9302 | 0.4758 | 0.5161 | |
| | 0.3487 | 5.0 | 635 | 0.6057 | 0.9272 | 0.4842 | 0.5565 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.47.1 |
| - Pytorch 2.5.1+cu121 |
| - Datasets 3.2.0 |
| - Tokenizers 0.21.0 |
| |
