---
library_name: transformers
license: apache-2.0
base_model: cybersectony/phishing-email-detection-distilbert_v2.4.1
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results
  results: []

inference_providers:
  - name: "CustomProvider"
    type: "python"
    path: "provider.py"
    description: "Runs inference using my custom backend"
---

<!-- 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. -->

# results

This model is a fine-tuned version of [cybersectony/phishing-email-detection-distilbert_v2.4.1](https://huggingface.co/cybersectony/phishing-email-detection-distilbert_v2.4.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0222
- Accuracy: 0.9964
- Precision: 0.9965
- Recall: 0.9964
- F1: 0.9964

## 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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.0213        | 1.0   | 12716 | 0.0208          | 0.9964   | 0.9964    | 0.9964 | 0.9964 |
| 0.0071        | 2.0   | 25432 | 0.0222          | 0.9964   | 0.9965    | 0.9964 | 0.9964 |


### Framework versions

- Transformers 4.57.2
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1