learn_hf_phishing_not_phishing_text_classifier
This model is a fine-tuned version of DeepPavlov/rubert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0943
- Accuracy: 0.9796
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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5017 | 1.0 | 7 | 0.4998 | 0.8367 |
| 0.275 | 2.0 | 14 | 0.0606 | 0.9796 |
| 0.0377 | 3.0 | 21 | 0.0091 | 1.0 |
| 0.0408 | 4.0 | 28 | 0.0078 | 1.0 |
| 0.0013 | 5.0 | 35 | 0.0117 | 1.0 |
| 0.0007 | 6.0 | 42 | 0.0678 | 0.9796 |
| 0.0005 | 7.0 | 49 | 0.0906 | 0.9796 |
| 0.0004 | 8.0 | 56 | 0.0987 | 0.9796 |
| 0.0004 | 9.0 | 63 | 0.1012 | 0.9796 |
| 0.0004 | 10.0 | 70 | 0.0943 | 0.9796 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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Base model
DeepPavlov/rubert-base-cased