Upload README.md

README.md

@@ -1,3 +1,92 @@
----
-license: apache-2.0
----
+---
+datasets:
+- ealvaradob/phishing-dataset
+language:
+- en
+base_model:
+- CrabInHoney/urlbert-tiny-base-v4
+pipeline_tag: text-classification
+tags:
+- url
+- urls
+- links
+- classification
+- tiny
+- phishing
+- urlbert
+license: apache-2.0
+---
+This is a very small version of BERT, designed to categorize links into phishing and non-phishing links
+
+An updated, lighter version of the old classification model for URL analysis
+
+Old version: https://huggingface.co/CrabInHoney/urlbert-tiny-v3-phishing-classifier
+##### Comparison with the previous version of urlbert phishing-classifier:
+
+| Version  | Accuracy  | Precision  | Recall  |  F1-score |
+| ------------ | ------------ | ------------ | ------------ | ------------ |
+|  v2 |  0.9665 |  0.9756 |  0.9522 | 0.9637  |
+| v3 | 0.9819  |  0.9876 | 0.9734  | 0.9805|
+| **v4 (this model)** | **0.9907**  |  **0.9945** | **0.9855**  | **0.9900** |
+
+Model size
+
+3.69M params
+
+Tensor type
+
+F32
+
+[Dataset](https://huggingface.co/datasets/ealvaradob/phishing-dataset "Dataset")
+(urls.json only)
+
+Example:
+
+
+
+    from transformers import BertTokenizerFast, BertForSequenceClassification, pipeline
+    import torch
+    
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    print(f"Используемое устройство: {device}")
+    
+    model_name = "CrabInHoney/urlbert-tiny-v4-phishing-classifier"
+    
+    tokenizer = BertTokenizerFast.from_pretrained(model_name)
+    model = BertForSequenceClassification.from_pretrained(model_name)
+    model.to(device)
+    
+    classifier = pipeline(
+        "text-classification",
+        model=model,
+        tokenizer=tokenizer,
+        device=0 if torch.cuda.is_available() else -1,
+        return_all_scores=True
+    )
+    
+    test_urls = [
+        "huggingface.co/",
+        "hu991ngface.com.ru/"
+    ]
+    
+    label_mapping = {"LABEL_0": "good", "LABEL_1": "fish"}
+    
+    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"Класс: {friendly_label}, вероятность: {score:.4f}")
+
+
+Используемое устройство: cuda
+
+URL: huggingface.co/
+Класс: good, вероятность: 0.9710
+Класс: fish, вероятность: 0.0290
+
+URL: hu991ngface.com.ru/
+Класс: good, вероятность: 0.0013
+Класс: fish, вероятность: 0.9987