O-labels treatment

[bschembri-UoM]

I would like to understand how seqeval treats sentences with no expected entities.

Taking the bellow example (adapted from the documentation);
actuals = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O'], ['O','O','O','O']]
preds = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O'], ['O','O','O','O']]
print(classification_report(actuals, preds, mode='strict', scheme=IOB2, digits=4))

I get the following output;

              precision    recall  f1-score   support

        MISC     0.0000    0.0000    0.0000         1
         PER     1.0000    1.0000    1.0000         1

   micro avg     0.5000    0.5000    0.5000         2
   macro avg     0.5000    0.5000    0.5000         2
weighted avg     0.5000    0.5000    0.5000         2

In case when a sentence is correctly predicted with no entities, isn't this sentence (labels) meant to be added to the metric calculations?
Looking at the support figure of "2" I believe that this implies that the last sentence is not taken into consideration.

Can you clarify this please?

• Operating System: Ubuntu 20.04
• Python Version: 3.7
• Package Version: 1.2.2

[r-erd]

(I know this is old, but I hope that after I provide this answer, the issue might get closed)

The support is the number of sample used per class in calculation of classification metrics.
The last sentence without any annotations is still taken into consideration, but it doesnt add any true positives.