Update README.md

README.md

@@ -32,12 +32,6 @@ The 28 labels from the [go_emotions](https://huggingface.co/datasets/go_emotions
 
 This is a multi-label, multi-class dataset, so each label is effectively a separate binary classification. Evaluating across all labels per item in the go_emotions test split the metrics are shown below.
 
-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:
-
-- Precision: 0.602
-- Recall: 0.250
-- F1: 0.303
-
 Optimising the threshold per label to optimise the F1 metric, the metrics (evaluated on the go_emotions test split) are:
 
 - Precision: 0.445
@@ -50,6 +44,12 @@ Weighted by the relative support of each label in the dataset, this is:
 - Recall: 0.582
 - F1: 0.514
 
+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, and unweighted by support) are:
+
+- Precision: 0.602
+- Recall: 0.250
+- F1: 0.303
+
 ### Metrics (per-label)
 
 This is a multi-label, multi-class dataset, so each label is effectively a separate binary classification and metrics are better measured per label.