created readme file

**#Model Description**

PhishFinder is a fine-tuned DistilBERT-based sequence classification model specifically designed to detect phishing URLs with high accuracy. This model analyzes URL patterns, structures, and characteristics to identify malicious websites attempting to steal sensitive information through social engineering attacks.


**Model Architecture**

• Base Model: DistilBERT (distilbert-base-uncased)
• Task: Binary Sequence Classification
• Framework: PyTorch + Transformers
• Model Type: Deep Learning Neural Network
• Training Objective: Phishing URL Detection (Legitimate vs Phishing)

**📊 Model Performance**

• Accuracy: ~99% on test dataset
• Inference Speed: <2 seconds average
• Input Format: Raw URLs (up to 256 characters)
• Response Time: Real-time detection suitable for production use


**Direct API Usage**

<code>import requests

API_URL = "https://router.huggingface.co/hf-inference/pelz-y3mi/phishing-detector"
headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}

def query(url):
response = requests.post(
API_URL,
headers=headers,
json={"inputs": url}
)
return response.json()

Test with a URL
result = query("https://www.google.com")
print(result)</code>

README.md

@@ -0,0 +1,20 @@
+---
+license: apache-2.0
+datasets:
+- ealvaradob/phishing-dataset
+language:
+- en
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+base_model:
+- pelz-y3mi/phishing-detector
+library_name: transformers
+tags:
+- phishing_urls
+- phishing
+- text_classifaction
+- machine_learning
+---