File size: 1,954 Bytes
708d260 2317567 708d260 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | ---
datasets:
- zefang-liu/phishing-email-dataset
language:
- en
base_model:
- google-bert/bert-base-uncased
library_name: transformers
tags:
- phishing
- email
- detection
- scam
---
# BERT Model for Phishing Detection
This repository contains the fine-tuned **BERT model** for detecting phishing emails. The model has been trained to classify emails as either **phishing** or **legitimate** based on their body text.
## Model Details
- **Model Type**: BERT (Bidirectional Encoder Representations from Transformers)
- **Task**: Phishing detection (Binary classification)
- **Fine-Tuning**: The model was fine-tuned on a dataset of phishing and legitimate emails.
## How to Use
1. **Install Dependencies**:
You can use the following command to install the necessary libraries:
```bash
pip install transformers torch
2. **Load Model**:
```bash
from transformers import BertForSequenceClassification, BertTokenizer
import torch
# Replace with your Hugging Face model repo name
model_name = 'ElSlay/BERT-Phishing-Email-Model'
# Load the pre-trained model and tokenizer
model = BertForSequenceClassification.from_pretrained(model_name)
tokenizer = BertTokenizer.from_pretrained(model_name)
# Ensure the model is in evaluation mode
model.eval()
3. **Use the model for Prediction**:
```bash
# Input email text
email_text = "Your email content here"
# Tokenize and preprocess the input text
inputs = tokenizer(email_text, return_tensors="pt", truncation=True, padding='max_length', max_length=512)
# Make the prediction
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predictions = torch.argmax(logits, dim=-1)
# Interpret the prediction
result = "Phishing" if predictions.item() == 1 else "Legitimate"
print(f"Prediction: {result}")
4. **Expected Outputs**:
1: Phishing
0: Legitimate |