metadata
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
results
This model is a fine-tuned version of 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