Inference Code added

README.md

@@ -50,10 +50,13 @@ Model is trained for 25 epochs on Azure for nearly 26000 Datapoints for above Me
 
 ## Below is the Training Result for 25 epochs.
 <ul>
-    <li>Training Computer Configuration: GPU:1xNvidia Tesla T4, 
-      VRam: 16GB,
-      Ram:112GB,
-      Cores:6 Cores </li>
+    <li>Training Computer Configuration: <ul>
+       <li>GPU:1xNvidia Tesla T4, </li>
+       <li>VRam: 16GB,</li>
+       <li>Ram:112GB,</li>
+       <li>Cores:6 Cores </li>
+    </ul></li>
+         
 <li>Training Time taken: exactly 7 hours for 25 epochs</li>
 <li>Training Hyper-parameters: </li>
   </ul>
@@ -64,4 +67,41 @@ Model is trained for 25 epochs on Azure for nearly 26000 Datapoints for above Me
 
 
 
-![training detail.png](https://cdn-uploads.huggingface.co/production/uploads/645c859ad90782b1a6a3e957/Oi9TuJ8nEjtt6Z_W56myn.png)
+![training detail.png](https://cdn-uploads.huggingface.co/production/uploads/645c859ad90782b1a6a3e957/Oi9TuJ8nEjtt6Z_W56myn.png)
+
+## Inference Code
+
+  ```
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
+model_name = 'Maninder120996/programming-language-identification'
+loaded_tokenizer = AutoTokenizer.from_pretrained(model_name)
+loaded_model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+  
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+text = """
+  PROGRAM Triangle
+     IMPLICIT NONE
+     REAL :: a, b, c, Area
+     PRINT *, 'Welcome, please enter the&
+              &lengths of the 3 sides.'
+     READ *, a, b, c
+     PRINT *, 'Triangle''s area:  ', Area(a,b,c)
+    END PROGRAM Triangle
+    FUNCTION Area(x,y,z)
+     IMPLICIT NONE
+     REAL :: Area            ! function type
+     REAL, INTENT( IN ) :: x, y, z
+     REAL :: theta, height
+     theta = ACOS((x**2+y**2-z**2)/(2.0*x*y))
+     height = x*SIN(theta); Area = 0.5*y*height
+    END FUNCTION Area
+
+"""
+inputs = loaded_tokenizer(text, return_tensors="pt",truncation=True)
+with torch.no_grad():
+    logits = loaded_model(**inputs).logits
+predicted_class_id = logits.argmax().item()
+loaded_model.config.id2label[predicted_class_id]
+```