Accuracy testing is crucial for evaluating the overall performance of NER models. It involves measuring how well the models can correctly predict outcomes on a test dataset they have not seen before.

How it works:

  • The model processes original text, producing an actual_result.
  • The expected_result in accuracy is the ground truth, which we will get from the dataset.
  • In the evaluation process, we compare the expected_result and actual_result based on the tests within the accuracy category such as precision, recall, F1 score, micro F1 score, macro F1 score, and weighted F1 score.