Update README.md

README.md

@@ -33,24 +33,29 @@ pip install onnxruntime transformers
 
 ### Python Example
 ```python
-
-from transformers import AutoTokenizer
+from tokenizers import Tokenizer
 import onnxruntime as ort
 import numpy as np
 
-# 1. Load Tokenizer
-tokenizer = AutoTokenizer.from_pretrained("broadfield-dev/bert-small-ner-pii-tuned-12261022-onnx")
+# 1. Load the lightweight tokenizer (No Transformers dependency needed)
+tokenizer = Tokenizer.from_pretrained("broadfield-dev/bert-small-ner-pii-tuned-12261022-onnx")
 
-# 2. Load Model
+# 2. Load the ONNX model
 session = ort.InferenceSession("model.onnx")
 
-# 3. Preprocess
-text = "This is a test sentence."
-inputs = tokenizer(text, return_tensors="np")
+# 3. Preprocess (Simple text encoding)
+text = "Run inference on mobile!"
+encoding = tokenizer.encode(text)
+
+# Prepare inputs (Exact names vary by model, usually input_ids + attention_mask)
+inputs = {{
+    "input_ids": np.array([encoding.ids], dtype=np.int64),
+    "attention_mask": np.array([encoding.attention_mask], dtype=np.int64)
+}}
 
-# 4. Inference
-outputs = session.run(None, dict(inputs))
-print(outputs[0])
+# 4. Run Inference
+outputs = session.run(None, inputs)
+print("Output logits shape:", outputs[0].shape)
 
 ```