| from typing import Any, Dict, List |
| import os |
| from flair.data import Sentence |
| from flair.models import SequenceTagger |
|
|
| class EndpointHandler(): |
| def __init__( |
| self, |
| path: str, |
| ): |
| self.tagger = SequenceTagger.load(os.path.join(path,"pytorch_model.bin")) |
|
|
| def __call__(self, data: Dict[str, Any]) -> 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 substring 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. |
| """ |
| inputs = data.pop("inputs", data) |
| sentence: Sentence = Sentence(inputs) |
|
|
| |
| self.tagger.predict(sentence, label_name="predicted") |
|
|
| entities = [] |
| for span in sentence.get_spans("predicted"): |
| if len(span.tokens) == 0: |
| continue |
| current_entity = { |
| "entity_group": span.tag, |
| "word": span.text, |
| "start": span.tokens[0].start_position, |
| "end": span.tokens[-1].end_position, |
| "score": span.score, |
| } |
|
|
| entities.append(current_entity) |
|
|
| return entities |
