
<strong><span style="color:red">WARNING ...</span></strong>

This is **NOT** the final BERT model trained for phishing detection. It only corresponds to an evaluation of BERT performance against web samples.

This model has the following performance in web phishing detection:

- Accuracy: 0.651332
- Precision: 0.000000
- Recall: 0.000000
- AUC: 0.644219

_Why did BERT perform so poorly in the website ranking when, in the combined phishing dataset, where there are also 
website samples, it generated excellent results?_

The hypothesis is as follows: Websites by nature are highly heterogeneous data, as they include human language, URLs, scripts, CSS and HTML code on top of that. 
This complexity could be the reason why BERT has such a hard time identifying malicious patterns in them. However, when this data is combined with more phishing-related 
information, a synergistic complementarity effect is possible. This effect has the potential to stimulate interactions that provide additional context, consequently 
improving BERT's ability to detect phishing. This may explain the behavior of BERT observed in the experiments with websites and why it was able to correctly classify 
this type of data in the final phishing dataset.
{"license": "apache-2.0", "datasets": ["ealvaradob/phishing-dataset"]}
transformers safetensors bert text-classification dataset:ealvaradob/phishing-dataset license:apache-2.0 autotrain_compatible endpoints_compatible region:us