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--- |
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license: mit |
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base_model: prajjwal1/bert-small |
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tags: |
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- generated_from_trainer |
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- small_BERT |
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- phishing_classifier |
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- classification |
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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: bert-small-phishing |
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results: [] |
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widget: |
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- text: "the other side of * galicismos * * galicismo * is a spanish term which names the improper introduction of french words which are spanish sounding and thus very deceptive to the ear . * galicismo * is often considered to be a * barbarismo * . what would be the term which designatesthe opposite phenomenon , that is unlawful words of spanish origin which may have crept into french ? can someone provide examples ? thank you joseph m kozono < kozonoj @ gunet . georgetown . edu >" |
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example_title: Safe Example 1 |
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- text: "Question?Do you want a different job? Do you want to be your own boss? Do you need extra income? Do you need to start a new life? Does your current job seem to go nowhere?If you answered yes to these questions,then here is your solution.We are a fortune 500 company looking for motivated individuals who are looking to a substantial income working from home.Thousands of individual are currently do this RIGHT NOW. So if you are looking to be employed at home, with a career that will provide you vast opportunities and a substantial income, please fill out our online information request form here now:http://ter.netblah.com:27000To miss out on this opportunity, click herehttp://ter.netblah.com:27000/remove.html" |
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example_title: Phishing Example 1 |
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- text: "re : testing ir & fx var nick and winston , i understand that ir & fx var numbers are calculated every day in risktrac . this results are overwritten everyday in the database table by the official numbers calculated with the old version of the code . for the consistent testing we need historical results for each ir and fx sub - portfolio . can we store the numbers every day ? tanya" |
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example_title: Safe Example 2 |
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- text: "software at incredibly low prices ( 86 % lower ) . drapery seventeen term represent any sing . feet wild break able build . tail , send subtract represent .job cow student inch gave . let still warm , family draw , land book . glass plan include . sentence is , hat silent nothing . order , wild famous long their . inch such , saw , person , save . face, especially sentence science . certain , cry does . two depend yes , written carry ." |
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example_title: Phishing Example 2 |
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datasets: |
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- David-Egea/phishing-texts |
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language: |
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- en |
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pipeline_tag: text-classification |
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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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# bert-small-phishing |
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This model is a fine-tuned version of [prajjwal1/bert-small](https://huggingface.co/prajjwal1/bert-small) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1006 |
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- Accuracy: 0.9766 |
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- Precision: 0.9713 |
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- Recall: 0.9669 |
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- F1: 0.9691 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 4 |
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- mixed_precision_training: Native AMP |
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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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| 0.202 | 1.0 | 762 | 0.0941 | 0.9717 | 0.9728 | 0.9520 | 0.9623 | |
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| 0.077 | 2.0 | 1524 | 0.0964 | 0.9764 | 0.9757 | 0.9617 | 0.9686 | |
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| 0.0428 | 3.0 | 2286 | 0.0992 | 0.9786 | 0.9739 | 0.9695 | 0.9717 | |
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| 0.0248 | 4.0 | 3048 | 0.1006 | 0.9766 | 0.9713 | 0.9669 | 0.9691 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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