---
license: apache-2.0
base_model: bert-large-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: phishing_3_1
  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. -->

# phishing_3_1

This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5678
- Accuracy: 0.9837
- Precision: 0.9884
- Recall: 0.9788
- False Positive Rate: 0.0115

## 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: 12
- eval_batch_size: 12
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:-------------------:|
| 0.5925        | 1.0   | 3025  | 0.5767          | 0.9743   | 0.9853    | 0.9630 | 0.0143              |
| 0.5784        | 2.0   | 6050  | 0.5709          | 0.9802   | 0.9764    | 0.9841 | 0.0238              |
| 0.5766        | 3.0   | 9075  | 0.6025          | 0.9490   | 0.9968    | 0.9008 | 0.0029              |
| 0.5682        | 4.0   | 12100 | 0.5678          | 0.9837   | 0.9884    | 0.9788 | 0.0115              |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2