---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-phishing-classifier_teacher
  results: []
datasets:
- shawhin/phishing-site-classification
---

<!-- 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. -->

# bert-phishing-classifier_teacher

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2881
- Accuracy: 0.867
- Auc: 0.951

## Model description

Teacher model for knowledge distillation example.

[Video](https://youtu.be/FLkUOkeMd5M) | [Blog](https://towardsdatascience.com/compressing-large-language-models-llms-9f406eea5b5e) | [Example code](https://github.com/ShawhinT/YouTube-Blog/tree/main/LLMs/model-compression)

## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc   |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|
| 0.4916        | 1.0   | 263  | 0.4228          | 0.784    | 0.915 |
| 0.3894        | 2.0   | 526  | 0.3586          | 0.818    | 0.932 |
| 0.3837        | 3.0   | 789  | 0.3144          | 0.86     | 0.939 |
| 0.3574        | 4.0   | 1052 | 0.4494          | 0.807    | 0.942 |
| 0.3517        | 5.0   | 1315 | 0.3287          | 0.86     | 0.947 |
| 0.3518        | 6.0   | 1578 | 0.3042          | 0.871    | 0.949 |
| 0.3185        | 7.0   | 1841 | 0.2900          | 0.862    | 0.949 |
| 0.3267        | 8.0   | 2104 | 0.2958          | 0.876    | 0.95  |
| 0.3153        | 9.0   | 2367 | 0.2881          | 0.867    | 0.951 |
| 0.3061        | 10.0  | 2630 | 0.2963          | 0.873    | 0.951 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.2.2
- Datasets 2.21.0
- Tokenizers 0.19.1