Update README.md

README.md

@@ -83,6 +83,8 @@ Optimising the threshold per label to optimise the F1 metric, the metrics (evalu
 | surprise       | 0.314 |     0.303 |  0.326 |     141 |      0.15 |
 | neutral        | 0.622 |     0.482 |  0.879 |    1787 |      0.25 |
 
+The thesholds are stored in `thresholds.json`.
+
 ### Use with ONNXRuntime
 
 The input to the model is called `logits`, and there is one output per label. Each output produces a 2d array, with 1 row per input row, and each row having 2 columns - the first being a proba output for the negative case, and the second being a proba output for the positive case.
@@ -109,6 +111,8 @@ print(outputs[0])
 > surprise
 print(preds_onnx[0])
 > array([[0.97136074, 0.02863926]], dtype=float32)
+
+# load thresholds.json and use that (per label) to convert the positive case score to a binary prediction 
 ```
 
 ### Commentary on the dataset