src/transformers/pipelines/token_classification.py/TokenClassificationPipeline/aggregate
class TokenClassificationPipeline:    def aggregate(self, pre_entities: List[dict], aggregation_strategy: AggregationStrategy) -> List[dict]:
        if aggregation_strategy in {AggregationStrategy.NONE, AggregationStrategy.SIMPLE}:
            entities = []
            for pre_entity in pre_entities:
                entity_idx = pre_entity["scores"].argmax()
                score = pre_entity["scores"][entity_idx]
                entity = {
                    "entity": self.model.config.id2label[entity_idx],
                    "score": score,
                    "index": pre_entity["index"],
                    "word": pre_entity["word"],
                    "start": pre_entity["start"],
                    "end": pre_entity["end"],
                }
                entities.append(entity)
        else:
            entities = self.aggregate_words(pre_entities, aggregation_strategy)

        if aggregation_strategy == AggregationStrategy.NONE:
            return entities
        return self.group_entities(entities)