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from typing import Any, Dict, List |
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import os |
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from flair.data import Sentence |
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from flair.models import SequenceTagger |
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class EndpointHandler(): |
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def __init__( |
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self, |
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path: str, |
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): |
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self.tagger = SequenceTagger.load("pytorch_model.bin") |
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def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: |
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""" |
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Args: |
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inputs (:obj:`str`): |
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a string containing some text |
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Return: |
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A :obj:`list`:. The object returned should be like [{"entity_group": "XXX", "word": "some word", "start": 3, "end": 6, "score": 0.82}] containing : |
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- "entity_group": A string representing what the entity is. |
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- "word": A substring of the original string that was detected as an entity. |
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- "start": the offset within `input` leading to `answer`. context[start:stop] == word |
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- "end": the ending offset within `input` leading to `answer`. context[start:stop] === word |
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- "score": A score between 0 and 1 describing how confident the model is for this entity. |
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""" |
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inputs = data.pop("inputs", data) |
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sentence: Sentence = Sentence(inputs) |
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self.tagger.predict(sentence, label_name="predicted") |
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entities = [] |
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for span in sentence.get_spans("predicted"): |
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if len(span.tokens) == 0: |
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continue |
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current_entity = { |
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"entity_group": span.tag, |
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"word": span.text, |
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"start": span.tokens[0].start_position, |
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"end": span.tokens[-1].end_position, |
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"score": span.score, |
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} |
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entities.append(current_entity) |
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return entities |
