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--- |
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license: apache-2.0 |
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datasets: |
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- shawhin/phishing-site-classification |
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metrics: |
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- accuracy |
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- recall |
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- precision |
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- f1 |
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base_model: distilbert/distilbert-base-uncased |
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pipeline_tag: text-classification |
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library_name: transformers |
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--- |
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# bert-phishing-classifier_student |
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This model is modified version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) trained via knowledge distillation from [shawhin/bert-phishing-classifier_teacher](https://huggingface.co/shawhin/bert-phishing-classifier_teacher) using the [shawhin/phishing-site-classification](https://huggingface.co/datasets/shawhin/phishing-site-classification) dataset. |
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It achieves the following results on the testing set: |
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- Loss (training): 0.0563 |
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- Accuracy: 0.9022 |
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- Precision: 0.9426 |
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- Recall: 0.8603 |
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- F1 Score: 0.8995 |
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## Model description |
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Student model for knowledge distillation example. |
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[Video](https://youtu.be/FLkUOkeMd5M) | [Blog](https://towardsdatascience.com/compressing-large-language-models-llms-9f406eea5b5e) | [Example code](https://github.com/ShawhinT/YouTube-Blog/tree/main/LLMs/model-compression) |
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## Intended uses & limitations |
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This model was created for educational purposes. |
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## Training and evaluation data |
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The Training, Testing, and Validation data are available here: [shawhin/phishing-site-classification](https://huggingface.co/datasets/shawhin/phishing-site-classification). |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- num_epochs: 5 |
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- temperature: 2.0 |
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- adam optimizer alpha: 0.5 |
