Update README.md

README.md

@@ -87,38 +87,7 @@ Optimising the threshold per label to optimise the F1 metric, the metrics (evalu
 | surprise       | 0.329 | 0.318     | 0.340  |  141    |      0.15 |
 | neutral        | 0.634 | 0.528     | 0.794  | 1787    |      0.30 |
 
-Using a fixed threshold of 0.5 to convert the scores to binary predictions for each label, the metrics (evaluated on the go_emotions test split) are:
-
-|                | f1    | precision | recall | support | threshold |
-| -------------- | ----- | --------- | ------ | ------- | --------- |
-| admiration     | 0.497 | 0.731     | 0.377  |  504    | 0.5       |
-| amusement      | 0.484 | 0.793     | 0.348  |  264    | 0.5       |
-| anger          | 0.162 | 0.528     | 0.096  |  198    | 0.5       |
-| annoyance      | 0.042 | 0.636     | 0.022  |  320    | 0.5       |
-| approval       | 0.106 | 0.769     | 0.057  |  351    | 0.5       |
-| caring         | 0.182 | 0.500     | 0.111  |  135    | 0.5       |
-| confusion      | 0.170 | 0.652     | 0.098  |  153    | 0.5       |
-| curiosity      | 0.284 | 0.529     | 0.194  |  284    | 0.5       |
-| desire         | 0.236 | 0.481     | 0.157  |   83    | 0.5       |
-| disappointment | 0.039 | 0.750     | 0.020  |  151    | 0.5       |
-| disapproval    | 0.140 | 0.636     | 0.079  |  267    | 0.5       |
-| disgust        | 0.273 | 0.677     | 0.171  |  123    | 0.5       |
-| embarrassment  | 0.314 | 0.571     | 0.216  |   37    | 0.5       |
-| excitement     | 0.130 | 0.400     | 0.078  |  103    | 0.5       |
-| fear           | 0.527 | 0.667     | 0.436  |   78    | 0.5       |
-| gratitude      | 0.792 | 0.908     | 0.702  |  352    | 0.5       |
-| grief          | 0.385 | 0.250     | 0.833  |    6    | 0.5       |
-| joy            | 0.276 | 0.771     | 0.168  |  161    | 0.5       |
-| love           | 0.606 | 0.800     | 0.487  |  238    | 0.5       |
-| nervousness    | 0.269 | 0.241     | 0.304  |   23    | 0.5       |
-| optimism       | 0.305 | 0.720     | 0.194  |  186    | 0.5       |
-| pride          | 0.375 | 0.375     | 0.375  |   16    | 0.5       |
-| realization    | 0.013 | 0.250     | 0.007  |  145    | 0.5       |
-| relief         | 0.353 | 0.500     | 0.273  |   11    | 0.5       |
-| remorse        | 0.469 | 0.548     | 0.411  |   56    | 0.5       |
-| sadness        | 0.365 | 0.731     | 0.244  |  156    | 0.5       |
-| surprise       | 0.142 | 0.786     | 0.078  |  141    | 0.5       |
-| neutral        | 0.547 | 0.644     | 0.475  | 1787    | 0.5       |
+The thesholds are stored in `thresholds.json`.
 
 ### Use with ONNXRuntime
 
@@ -145,6 +114,8 @@ 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