Downloads · 30 days
10
36% of all-time downloads
phishing_clf_fine_tuned_bert
Abdelllm2025/phishing_clf_fine_tuned_bert
phishing_clf_fine_tuned_bert is a text classification model from Abdelllm2025. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
28
Public
Parameters
109M
4.4 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors438 MB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Apr 2, 2025
- Updated
- Apr 3, 2025
- SHA
- 303134f4
Try a prompt
Base models
Model card
From the Hugging Face model README
<!-- 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_clf_fine_tuned_bert
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3747
- Accuracy: 0.8627
- F1: 0.8624
- Precision: 0.8622
- Recall: 0.8627
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.2591 | 1.0 | 3915 | 0.3506 | 0.8317 | 0.8334 | 0.8537 | 0.8317 |
| 0.3208 | 2.0 | 7830 | 0.3600 | 0.8633 | 0.8626 | 0.8628 | 0.8633 |
| 0.3701 | 3.0 | 11745 | 0.4911 | 0.8010 | 0.7856 | 0.8338 | 0.8010 |
| 0.3687 | 4.0 | 15660 | 0.5187 | 0.8100 | 0.8044 | 0.8140 | 0.8100 |
| 0.6933 | 5.0 | 19575 | 0.6734 | 0.6013 | 0.4515 | 0.3615 | 0.6013 |
| 0.6813 | 6.0 | 23490 | 0.6727 | 0.6013 | 0.4515 | 0.3615 | 0.6013 |
| 0.6847 | 7.0 | 27405 | 0.6725 | 0.6013 | 0.4515 | 0.3615 | 0.6013 |
| 0.678 | 8.0 | 31320 | 0.6735 | 0.6013 | 0.4515 | 0.3615 | 0.6013 |
| 0.5732 | 9.0 | 35235 | 0.5080 | 0.8269 | 0.8267 | 0.8265 | 0.8269 |
| 0.3976 | 10.0 | 39150 | 0.3747 | 0.8627 | 0.8624 | 0.8622 | 0.8627 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Apr 2, 2025
- Updated
- Apr 3, 2025