| --- |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: codebert-base-Password_Strength_Classifier |
| results: [] |
| license: mit |
| --- |
| |
| # codebert-base-Password_Strength_Classifier |
|
|
| This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base). |
|
|
| It achieves the following results on the evaluation set: |
| - Loss: 0.0077 |
| - Accuracy: 0.9975 |
| - F1 |
| - Weighted: 0.9975 |
| - Micro: 0.9975 |
| - Macro: 0.9963 |
| - Recall |
| - Weighted: 0.9975 |
| - Micro: 0.9975 |
| - Macro: 0.9978 |
| - Precision |
| - Weighted: 0.9975 |
| - Macro: 0.9948 |
| - Micro: 0.9975 |
|
|
| ## Model description |
|
|
| The model classifies passwords as one of the following: |
| 1) Weak |
| 2) Medium |
| 3) Strong |
|
|
| For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Password%20Strength%20Classification%20(MC)/CodeBERT-Base%20-%20Password_Classifier.ipynb |
|
|
| ## Intended uses & limitations |
|
|
| This is intended to show the possibilities. It is mainly limited by the input data. |
|
|
| ## Training and evaluation data |
|
|
| Dataset Source: https://www.kaggle.com/datasets/bhavikbb/password-strength-classifier-dataset |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 64 |
| - eval_batch_size: 64 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 3 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted F1 | Micro F1 | Macro F1 | Weighted Recall | Micro Recall | Macro Recall | Weighted Precision | Micro Precision | Macro Precision | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:| |
| | 0.0438 | 1.0 | 8371 | 0.0112 | 0.9956 | 0.9956 | 0.9956 | 0.9935 | 0.9956 | 0.9956 | 0.9963 | 0.9957 | 0.9956 | 0.9908 | |
| | 0.0133 | 2.0 | 16742 | 0.0092 | 0.9966 | 0.9967 | 0.9966 | 0.9951 | 0.9966 | 0.9966 | 0.9966 | 0.9967 | 0.9966 | 0.9935 | |
| | 0.0067 | 3.0 | 25113 | 0.0077 | 0.9975 | 0.9975 | 0.9975 | 0.9963 | 0.9975 | 0.9975 | 0.9978 | 0.9975 | 0.9975 | 0.9948 | |
|
|
| ### Framework versions |
|
|
| - Transformers 4.27.4 |
| - Pytorch 2.0.0 |
| - Datasets 2.11.0 |
| - Tokenizers 0.13.3 |
|
|
|
|
| ## License Notice |
| This model is a fine-tuned derivative of a pretrained model. |
| Users must comply with the original model license. |
|
|
|
|
| ## Dataset Notice |
| This model was fine-tuned on third-party datasets which may have separate licenses or usage restrictions. |
