Update README.md

README.md

@@ -150,7 +150,26 @@ This model aims to help developers, especially those with little to no experienc
 ### Direct Use
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-Just use the model from hugging face directly
+Just use the model from hugging face directly. Following is an example
+    
+    from transformers import XLMRobertaForSequenceClassification, AutoTokenizer
+    import torch
+    
+    model_name = "Jayveersinh-Raj/PolyGuard"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = XLMRobertaForSequenceClassification.from_pretrained(model_name)
+
+    text = "Jayveer is a great NLP engineer, and a noob in CV"
+    inputs = tokenizer.encode(text, return_tensors="pt", max_length=512, truncation=True)
+    outputs = model(inputs)[0]
+    probabilities = torch.softmax(outputs, dim=1)
+    predicted_class = torch.argmax(probabilities).item()
+    if predicted_class == 1:
+      print("Toxic")
+    else:
+      print("Not toxic")
+
+    
 
 
 ### Downstream Use [optional]