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--- |
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license: apache-2.0 |
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base_model: albert-base-v2 |
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tags: |
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- generated_from_trainer |
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- URL |
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- Security |
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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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model-index: |
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- name: albert-base-v2-Malicious_URLs |
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results: [] |
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pipeline_tag: text-classification |
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--- |
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# albert-base-v2-Malicious_URLs |
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This model is a fine-tuned version of [albert-base-v2](https://huggingface.co./albert-base-v2). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8368 |
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- Accuracy: 0.7267 |
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- F1: |
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- Weighted: 0.6482 |
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- Micro: 0.7267 |
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- Macro: 0.4521 |
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- Recall |
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- Weighted: 0.7267 |
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- Micro: 0.7267 |
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- Macro: 0.4294 |
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- Precision |
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- Weighted: 0.6262 |
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- Micro: 0.7267 |
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- Macro: 0.5508 |
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## Model description |
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For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Multiclass%20Classification/Malicious%20URLs%20-%20ALBERT-Base_v2/Malicious%20URLs%20ALBERT-Base%20v2.ipynb |
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## Intended uses & limitations |
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This model is intended to demonstrate my ability to solve a complex problem using technology. |
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## Training and evaluation data |
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Dataset Source: https://www.kaggle.com/datasets/sid321axn/malicious-urls-dataset |
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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: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted F1 | Micro F1 | Macro F1 | Weighted Recall | Micro Recall | Macro Recall | Weighted Precision | Micro Precision | Macro Precision | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:| |
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| 0.7839 | 1.0 | 51087 | 0.8368 | 0.7267 | 0.6482 | 0.7267 | 0.4521 | 0.7267 | 0.7267 | 0.4294 | 0.6262 | 0.7267 | 0.5508 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
