| | --- |
| | library_name: transformers |
| | license: llama3.1 |
| | base_model: meta-llama/Prompt-Guard-86M |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - precision |
| | - recall |
| | - f1 |
| | model-index: |
| | - name: results |
| | 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. --> |
| |
|
| | # results |
| |
|
| | This model is a fine-tuned version of [meta-llama/Prompt-Guard-86M](https://huggingface.co/meta-llama/Prompt-Guard-86M) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.3940 |
| | - Accuracy: 0.8083 |
| | - Precision: 0.8493 |
| | - Recall: 0.8083 |
| | - F1: 0.8004 |
| |
|
| | ## 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: 3e-05 |
| | - train_batch_size: 1 |
| | - eval_batch_size: 1 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 8 |
| | - total_train_batch_size: 8 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_steps: 5 |
| | - num_epochs: 10 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| | |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | | 0.4309 | 0.9895 | 59 | 0.3940 | 0.8083 | 0.8493 | 0.8083 | 0.8004 | |
| | | 0.2471 | 1.9958 | 119 | 0.4489 | 0.8667 | 0.8809 | 0.8667 | 0.8646 | |
| | | 0.308 | 2.9853 | 178 | 0.4891 | 0.875 | 0.8890 | 0.875 | 0.8745 | |
| | | 0.0769 | 3.9916 | 238 | 0.5789 | 0.875 | 0.8763 | 0.875 | 0.8751 | |
| | | 0.0185 | 4.9979 | 298 | 0.5860 | 0.9083 | 0.9091 | 0.9083 | 0.9082 | |
| | | 0.1513 | 5.9874 | 357 | 0.7945 | 0.8417 | 0.8548 | 0.8417 | 0.8411 | |
| | | 0.0262 | 6.9937 | 417 | 0.7072 | 0.8917 | 0.8917 | 0.8917 | 0.8916 | |
| | | 0.0011 | 8.0 | 477 | 0.6887 | 0.9083 | 0.9108 | 0.9083 | 0.9080 | |
| | | 0.0008 | 8.9895 | 536 | 0.7496 | 0.8917 | 0.8917 | 0.8917 | 0.8916 | |
| | | 0.0007 | 9.8952 | 590 | 0.7500 | 0.9 | 0.9003 | 0.9 | 0.8999 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.44.2 |
| | - Pytorch 2.5.0+cu121 |
| | - Datasets 3.0.2 |
| | - Tokenizers 0.19.1 |
| |
|
