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tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: codebert-base-Password_Strength_Classifier
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. -->
# codebert-base-Password_Strength_Classifier
This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0077
- Accuracy: 0.9975
- Weighted f1: 0.9975
- Micro f1: 0.9975
- Macro f1: 0.9963
- Weighted recall: 0.9975
- Micro recall: 0.9975
- Macro recall: 0.9978
- Weighted precision: 0.9975
- Micro precision: 0.9975
- Macro precision: 0.9948
## 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: 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
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