| | --- |
| | license: apache-2.0 |
| | base_model: jackaduma/SecBERT |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: dnrti_secbert |
| | 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. --> |
| |
|
| | # dnrti_secbert |
| | |
| | This model is a fine-tuned version of [jackaduma/SecBERT](https://huggingface.co/jackaduma/SecBERT) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.2274 |
| | - Precision: 0.7405 |
| | - Recall: 0.7780 |
| | - F1: 0.7588 |
| | - Accuracy: 0.9389 |
| | |
| | ## 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: 8 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 10.0 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | 0.6137 | 0.76 | 500 | 0.3731 | 0.5348 | 0.5951 | 0.5633 | 0.8842 | |
| | | 0.2993 | 1.52 | 1000 | 0.2853 | 0.6684 | 0.6665 | 0.6674 | 0.9131 | |
| | | 0.2157 | 2.28 | 1500 | 0.2624 | 0.6685 | 0.7282 | 0.6971 | 0.9212 | |
| | | 0.152 | 3.04 | 2000 | 0.2414 | 0.6923 | 0.7619 | 0.7254 | 0.9308 | |
| | | 0.1047 | 3.81 | 2500 | 0.2274 | 0.7405 | 0.7780 | 0.7588 | 0.9389 | |
| | | 0.0725 | 4.57 | 3000 | 0.2563 | 0.7262 | 0.7964 | 0.7597 | 0.9370 | |
| | | 0.0589 | 5.33 | 3500 | 0.2615 | 0.7489 | 0.8024 | 0.7747 | 0.9411 | |
| | | 0.0442 | 6.09 | 4000 | 0.2638 | 0.7543 | 0.8061 | 0.7793 | 0.9434 | |
| | | 0.0344 | 6.85 | 4500 | 0.2671 | 0.7635 | 0.8088 | 0.7855 | 0.9448 | |
| | | 0.0282 | 7.61 | 5000 | 0.2861 | 0.7584 | 0.8111 | 0.7839 | 0.9439 | |
| | | 0.0226 | 8.37 | 5500 | 0.2849 | 0.7693 | 0.8093 | 0.7888 | 0.9456 | |
| | | 0.0207 | 9.13 | 6000 | 0.2932 | 0.7643 | 0.8185 | 0.7905 | 0.9456 | |
| | | 0.0181 | 9.89 | 6500 | 0.2952 | 0.7665 | 0.8167 | 0.7908 | 0.9459 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.36.0.dev0 |
| | - Pytorch 2.1.0+cu118 |
| | - Datasets 2.14.6 |
| | - Tokenizers 0.14.1 |
| | |
