Upload README.md

README.md

@@ -1,3 +1,118 @@
----
-license: apache-2.0
----
+---
+language:
+- en
+base_model:
+- CrabInHoney/urlbert-tiny-base-v4
+pipeline_tag: text-classification
+tags:
+- url
+- cybersecurity
+- urls
+- links
+- classification
+- phishing-detection
+- tiny
+- phishing
+- malware
+- defacement
+- transformers
+- urlbert
+- bert
+- malicious
+license: apache-2.0
+---
+
+# URLBERT-Tiny-v4 Malicious URL Classifier
+
+This is a lightweight version of BERT, specifically fine-tuned for classifying URLs into four categories: benign, phishing, malware, and defacement.
+
+## Model Details
+
+- **Model size**: 3.69M parameters  
+- **Tensor type**: F32  
+- **Model weight size**: 14.8 MB  
+- **Base model**: [CrabInHoney/urlbert-tiny-base-v4](https://huggingface.co/CrabInHoney/urlbert-tiny-base-v4)  
+- **Dataset**: [Malicious URLs Dataset](https://www.kaggle.com/datasets/sid321axn/malicious-urls-dataset)  
+
+## Model Evaluation Results
+
+The model was evaluated on a test set with the following classification metrics:
+
+
+| Metric | Model V3 | Model V4 (this model) |
+|--------|----------|----------|
+| **Overall Accuracy** | 0.9837 | **0.9922** |
+| **F1-score (Benign)** | 0.9907 | **0.9955** |
+| **F1-score (Defacement)** | 0.9937 | **0.9984** |
+| **F1-score (Malware)** | 0.9741 | **0.9845** |
+| **F1-score (Phishing)** | 0.9444 | **0.9734** |
+| **Weighted Average F1-score** | 0.9836 | **0.9922** |
+
+
+
+## Usage Example
+
+Below is an example of how to use the model for URL classification using the Hugging Face `transformers` library:
+
+```python
+from transformers import BertTokenizerFast, BertForSequenceClassification, pipeline
+import torch
+
+# Определение устройства (GPU или CPU)
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+print(f"Используемое устройство: {device}")
+
+# Загрузка модели и токенизатора
+model_name = "CrabInHoney/urlbert-tiny-v4-malicious-url-classifier"
+tokenizer = BertTokenizerFast.from_pretrained(model_name)
+model = BertForSequenceClassification.from_pretrained(model_name)
+model.to(device)
+
+# Создание pipeline для классификации
+classifier = pipeline(
+    "text-classification",
+    model=model,
+    tokenizer=tokenizer,
+    device=0 if torch.cuda.is_available() else -1,
+    return_all_scores=True
+)
+
+# Примеры URL для тестирования
+test_urls = [
+    "wikiobits.com/Obits/TonyProudfoot",
+    "http://www.824555.com/app/member/SportOption.php?uid=guest&langx=gb",
+]
+
+# Маппинг меток на понятные названия классов
+label_mapping = {
+    "LABEL_0": "benign",
+    "LABEL_1": "defacement",
+    "LABEL_2": "malware",
+    "LABEL_3": "phishing"
+}
+
+# Классификация URL
+for url in test_urls:
+    results = classifier(url)
+    print(f"\nURL: {url}")
+    for result in results[0]: 
+        label = result['label']
+        score = result['score']
+        friendly_label = label_mapping.get(label, label)
+        print(f"{friendly_label}, %: {score:.4f}")
+```
+
+### Example Output:
+```
+URL: wikiobits.com/Obits/TonyProudfoot
+benign, %: 0.9996
+defacement, %: 0.0000
+malware, %: 0.0000
+phishing, %: 0.0003
+
+URL: http://www.824555.com/app/member/SportOption.php?uid=guest&langx=gb
+benign, %: 0.0000
+defacement, %: 0.0001
+malware, %: 0.9998
+phishing, %: 0.0001
+```