fix: correct ONNX inference example — TF-IDF is embedded, pass raw strings directly

README.md

@@ -102,25 +102,21 @@ Keyword lists (WH starters, command verbs, polite modals, negation words) are lo
 
 ### ONNX Inference (Recommended)
 
+The ONNX model is a full pipeline — TF-IDF featurisation is embedded. Pass raw text strings directly; no vectorizer setup needed.
+
 ```python
 import onnxruntime as rt
-import numpy as np
-from sklearn.feature_extraction.text import TfidfVectorizer
+import json
 
-# Load ONNX model
 sess = rt.InferenceSession("sentence_type_EN_0.8.0.onnx")
-input_name = sess.get_inputs()[0].name
-label_name = sess.get_outputs()[0].name
-
-# Create vectorizer (same parameters as training)
-vectorizer = TfidfVectorizer(analyzer="word", ngram_range=(1, 2),
-                             min_df=1, max_df=0.9, sublinear_tf=True)
-
-# Predict
-text = "Could you pass the salt?"
-vec = vectorizer.transform([text]).toarray().astype(np.float32)
-pred = sess.run([label_name], {input_name: vec})
-print(pred)  # e.g., [2] → class index
+
+# Class labels are stored in model metadata
+classes = json.loads(sess.get_modelmeta().custom_metadata_map["classes"])
+
+texts = ["Could you pass the salt?", "What time is it?", "Close the door."]
+pred_indices = sess.run(None, {"input": texts})[0]
+print([classes[i] for i in pred_indices])
+# → ['request', 'wh_question', 'command']
 ```
 
 ## License