codebert-base-password-strength-classifier-normal-weight-balancing
This model is a fine-tuned version of microsoft/codebert-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0083
- Accuracy: 0.9977
- Weighted f1: 0.9977
- Micro f1: 0.9977
- Macro f1: 0.9966
- Weighted recall: 0.9977
- Micro recall: 0.9977
- Macro recall: 0.9979
- Weighted precision: 0.9977
- Micro precision: 0.9977
- Macro precision: 0.9953
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0345 | 1.0 | 37667 | 0.0522 | 0.9825 | 0.9829 | 0.9825 | 0.9755 | 0.9825 | 0.9825 | 0.9915 | 0.9844 | 0.9825 | 0.9619 |
| 0.0099 | 2.0 | 75334 | 0.0083 | 0.9977 | 0.9977 | 0.9977 | 0.9966 | 0.9977 | 0.9977 | 0.9979 | 0.9977 | 0.9977 | 0.9953 |
Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.6.dev0
- Tokenizers 0.13.3