def __call__()

in docker_images/stanza/app/pipelines/token_classification.py [0:0]

• 25 lines of code
• 5 McCabe index (conditional complexity)

    def __call__(self, inputs: str) -> List[Dict[str, Any]]:
        """
        Args:
            inputs (:obj:`str`):
                a string containing some text
        Return:
            A :obj:`list`:. The object returned should be like [{"entity_group": "XXX", "word": "some word", "start": 3, "end": 6, "score": 0.82}] containing :
                - "entity_group": A string representing what the entity is.
                - "word": A rubstring of the original string that was detected as an entity.
                - "start": the offset within `input` leading to `answer`. context[start:stop] == word
                - "end": the ending offset within `input` leading to `answer`. context[start:stop] === word
                - "score": A score between 0 and 1 describing how confident the model is for this entity.
        """
        doc = self.model(inputs)

        entities = []
        if "ner_model_path" in self.model.config.keys():
            for entity in doc.entities:
                entity_dict = {
                    "entity_group": entity.type,
                    "word": entity.text,
                    "start": entity.start_char,
                    "end": entity.end_char,
                    "score": 1.0,
                }
                entities.append(entity_dict)

        else:
            for sent in doc.sentences:
                for entity in sent.words:
                    entity_dict = {
                        "entity_group": entity.upos,
                        "word": entity.text,
                        "start": entity.start_char,
                        "end": entity.end_char,
                        "score": 1.0,
                    }

                    entities.append(entity_dict)

        return entities