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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: MALWARE-URL-DETECTION |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# URL-DETECTION |
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With this model, Classifies url addresses as malware and benign. |
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Type the domain name of the url address in the text field for classification in API: Like this: |
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"huggingface.com" |
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To test the model, visit [SITE](https://www.usom.gov.tr/adres). Harmful links used are listed on this site. |
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This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2122 |
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- Accuracy: 0.945 |
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- Precision: 0.9611 |
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- Recall: 0.9287 |
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- F1: 0.9446 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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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: 5e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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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: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| No log | 1.0 | 63 | 0.2153 | 0.921 | 0.9953 | 0.8475 | 0.9155 | |
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| No log | 2.0 | 126 | 0.1927 | 0.946 | 0.9669 | 0.9248 | 0.9453 | |
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| No log | 3.0 | 189 | 0.2122 | 0.945 | 0.9611 | 0.9287 | 0.9446 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.13.3 |
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