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README.md
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@@ -55,7 +55,6 @@ Using a fixed threshold of 0.5 to convert the scores to binary predictions for e
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This is a multi-label, multi-class dataset, so each label is effectively a separate binary classification and metrics are better measured per label.
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Optimising the threshold per label to optimise the F1 metric, the metrics (evaluated on the go_emotions test split) are:
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| | f1 | precision | recall | support | threshold |
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| -------------- | ----- | --------- | ------ | ------- | --------- |
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| admiration | 0.561 | 0.517 | 0.613 | 504 | 0.25 |
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| 55 |
This is a multi-label, multi-class dataset, so each label is effectively a separate binary classification and metrics are better measured per label.
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| 56 |
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| 57 |
Optimising the threshold per label to optimise the F1 metric, the metrics (evaluated on the go_emotions test split) are:
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| | f1 | precision | recall | support | threshold |
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| -------------- | ----- | --------- | ------ | ------- | --------- |
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| admiration | 0.561 | 0.517 | 0.613 | 504 | 0.25 |
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