Update README.md

README.md

@@ -16,4 +16,49 @@ library_name: transformers
 tags:
 - moderation
 - abuse_detection
----
+
+
+To use this model follow this.
+
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("iamrazi/text-moderation")
+model = AutoModelForSequenceClassification.from_pretrained("iamrazi/text-moderation")
+
+model.eval()  # Set model to evaluation mode
+
+def predict_abuse(text: str, threshold: float = 0.5):
+    """
+    Predict if a text is abusive or not.
+    
+    Args:
+        text (str): Input text.
+        threshold (float): Probability threshold for classification.
+    
+    Returns:
+        label (int): 0 for non-abusive, 1 for abusive
+        proba (float): Probability of being abusive
+    """
+    # Tokenize
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
+    
+    # Forward pass
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probas = torch.sigmoid(logits)  # if your model output layer is logits
+    
+    # For binary classification, take the probability of class 1
+    prob = probas[0][1].item() if probas.shape[1] > 1 else probas[0][0].item()
+    
+    # Determine label
+    label = 1 if prob >= threshold else 0
+    
+    return label, prob
+
+
+text = "तुम बहुत गंदे हो 😡"
+label, proba = predict_abuse(text)
+
+Output: Label: 0, Probability: 0.08