Added ONNX inference Code

README.md

@@ -80,6 +80,48 @@ 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]
+```
+
+Optimum with ONNX
+
+Loading the model requires the 🤗 Optimum library installed.
+```shell
+pip install transformers optimum[onnxruntime] optimum
+```
+
+```python
+model_path = "philomath-1209/programming-language-identification"
+
+from transformers import pipeline, AutoTokenizer
+from optimum.onnxruntime import ORTModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = ORTModelForSequenceClassification.from_pretrained(model_path, export=True)
+
 text = """
   PROGRAM Triangle
      IMPLICIT NONE