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---
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
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