Update README.md

README.md

@@ -26,9 +26,9 @@ Training Dataset: Custom dataset of clinical notes, ICD codes, and supporting ev
 Task: Causal Language Modeling for code prediction
 
 ## Usage
-
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch, re
+    #
+    from transformers import AutoTokenizer, AutoModelForCausalLM
+    import torch, re
 
     # Load tokenizer and model
     tokenizer = AutoTokenizer.from_pretrained("Kavyaah/medical-coding-llm")
@@ -49,21 +49,13 @@ import torch, re
     match = re.search(r"\b[A-Z]\d{1,3}\.?[A-Z0-9]*\b", result)
     return match.group(0).strip() if match else result.strip()
 
-#### Example
-statement = "Patient diagnosed with Type 2 diabetes mellitus without complications."
-print(get_code(statement))
-#### Output: E11.9
+    # Example
+    statement = "Patient diagnosed with Type 2 diabetes mellitus without complications."
+    print(get_code(statement))
+    # Output: E11.9
 
 ## Evaluation
 
-Tested on a small example set:
-
-Statement	True Code	Predicted Code
-Type 2 diabetes	E11.9	E11.9
-Acute bronchitis	J20.0	J20.9
-Routine child health exam	Z00.129	99395
-Essential hypertension	I10	99213
-
 Exact match accuracy: 25%
 
 Semantic accuracy (ICD block match): 50%