| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: markusbayer/CySecBERT |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| model-index: |
| - name: cysecbert-ttp |
| 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. --> |
|
|
| # cysecbert-ttp |
|
|
| This model is a fine-tuned version of [markusbayer/CySecBERT](https://huggingface.co/markusbayer/CySecBERT) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0182 |
| - F1 Micro: 0.3613 |
| - F1 Macro: 0.0811 |
| - Precision: 0.7753 |
| - Recall: 0.2355 |
|
|
| ## 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: 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 |
| - num_epochs: 5 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Precision | Recall | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:| |
| | 0.0359 | 1.0 | 1226 | 0.0316 | 0.0 | 0.0 | 0.0 | 0.0 | |
| | 0.0278 | 2.0 | 2452 | 0.0278 | 0.0202 | 0.0036 | 0.8571 | 0.0102 | |
| | 0.0242 | 3.0 | 3678 | 0.0230 | 0.2709 | 0.0438 | 0.7462 | 0.1655 | |
| | 0.0233 | 4.0 | 4904 | 0.0193 | 0.2881 | 0.0567 | 0.8361 | 0.1741 | |
| | 0.0186 | 5.0 | 6130 | 0.0182 | 0.3613 | 0.0811 | 0.7753 | 0.2355 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.57.5 |
| - Pytorch 2.2.0 |
| - Datasets 4.5.0 |
| - Tokenizers 0.22.2 |
|
|
