def predictions_processor()

in src/evaluate/evaluator/token_classification.py [0:0]

• 15 lines of code
• 9 McCabe index (conditional complexity)

    def predictions_processor(self, predictions: List[List[Dict]], words: List[List[str]], join_by: str):
        """
        Transform the pipeline predictions into a list of predicted labels of the same length as the true labels.

        Args:
            predictions (`List[List[Dict]]`):
                List of pipeline predictions, where each token has been labeled.
            words (`List[List[str]]`):
                Original input data to the pipeline, used to build predicted labels of the same length.
            join_by (`str`):
                String to use to join two words. In English, it will typically be " ".

        Returns:
            `dict`: a dictionary holding the predictions
        """
        preds = []

        # iterate over the data rows
        for i, prediction in enumerate(predictions):
            pred_processed = []

            # get a list of tuples giving the indexes of the start and end character of each word
            words_offsets = self.words_to_offsets(words[i], join_by)

            token_index = 0
            for word_offset in words_offsets:
                # for each word, we may keep only the predicted label for the first token, discard the others
                while prediction[token_index]["start"] < word_offset[0]:
                    token_index += 1

                if prediction[token_index]["start"] > word_offset[0]:  # bad indexing
                    pred_processed.append("O")
                elif prediction[token_index]["start"] == word_offset[0]:
                    pred_processed.append(prediction[token_index]["entity"])

            preds.append(pred_processed)

        return {"predictions": preds}