README.md

metadata

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.

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.