Update README.md

README.md

@@ -14,45 +14,9 @@ language:
 - en
 library_name: transformers
 ---
-from transformers import pipeline
 
-class ContentFilter:
-    def __init__(self):
-        # Load pre-trained model for text classification
-        self.classifier = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-offensive")
-    
-    def filter_content(self, text, threshold=0.75):
-        # Classify the input text
-        result = self.classifier(text)[0]
-        
-        # Determine if the content is offensive based on the classification
-        is_offensive = result['label'] == 'LABEL_1' and result['score'] > threshold
-        
-        return {
-            "text": text,
-            "is_offensive": is_offensive,
-            "confidence": result['score']
-        }
 
-# Create an instance of the ContentFilter
-content_filter = ContentFilter()
 
-# Function to test the filter
-def test_filter():
-    texts = [
-        "Have a nice day!",
-        "You are an idiot!",
-        "The weather is lovely.",
-        "I hate you so much."
-    ]
-    
-    for text in texts:
-        result = content_filter.filter_content(text)
-        print(f"Text: {result['text']}")
-        print(f"Is offensive: {result['is_offensive']}")
-        print(f"Confidence: {result['confidence']:.4f}")
-        print()
 
-# Run the test
-if __name__ == "__main__":
-    test_filter()
+
+