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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)
# Also show scores for recognized NEs
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 |