Update README.md

README.md

@@ -84,12 +84,12 @@ input_feed_dict = {
   "attention_mask": [t.attention_mask for t in tokens_obj]
 }
 
-def sigmoid(_outputs):
-  return 1.0 / (1.0 + np.exp(-_outputs))
-
 logits = model.run(output_names=output_names, input_feed=input_feed_dict)[0]
 # produces a numpy array, one row per input item, one col per label
 
+def sigmoid(_outputs):
+  return 1.0 / (1.0 + np.exp(-_outputs))
+
 # Post-processing. Gets the scores per label in range.
 # Auto done by Transformers' pipeline, but we must do it manually with ORT.
 model_outputs = sigmoid(logits)