Update README.md

README.md

@@ -16,21 +16,23 @@ should probably proofread and complete it, then remove this comment. -->
 
 # malicious-url-detector
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
+malicious-url-detector is a fine-tuned version of distilbert-base-uncased designed to classify URLs as malicious or benign using natural language and pattern-based representations.
+It leverages LoRA (Low-Rank Adaptation) via the PEFT library for lightweight, efficient fine-tuning.
 
 ## Model description
-
-More information needed
+This model learns to identify potentially harmful URLs based on patterns commonly found in phishing, malware delivery, and command-and-control links.
+It was fine-tuned on a curated dataset of labeled URLs containing both malicious and safe samples.
 
 ## Intended uses & limitations
+Intended uses:
+- Integrate into threat detection systems or browser security tools
+- Use for phishing URL classification or malware link filtering
+- Educational and research purposes in cybersecurity automation
 
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
+Limitations:
+- Should not be solely relied upon for production-grade URL blocking
+- May misclassify newly obfuscated or encrypted URLs
+- Requires regular retraining with updated datasets to maintain accuracy
 
 ### Training hyperparameters
 
@@ -43,10 +45,6 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 3
 
-### Training results
-
-
-
 ### Framework versions
 
 - PEFT 0.17.1