---
license: apache-2.0
base_model: bert-large-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: bert-finetuned-phishing
  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. -->

# bert-finetuned-phishing

This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1953
- Accuracy: 0.9717
- Precision: 0.9658
- Recall: 0.9670
- False Positive Rate: 0.0249

## 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: 4

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | False Positive Rate |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:-------------------:|
| 0.1487        | 1.0   | 3866  | 0.1454          | 0.9596   | 0.9709    | 0.9320 | 0.0203              |
| 0.0805        | 2.0   | 7732  | 0.1389          | 0.9691   | 0.9663    | 0.9601 | 0.0243              |
| 0.0389        | 3.0   | 11598 | 0.1779          | 0.9683   | 0.9778    | 0.9461 | 0.0156              |
| 0.0091        | 4.0   | 15464 | 0.1953          | 0.9717   | 0.9658    | 0.9670 | 0.0249              |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.1+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1