kpe_ranker.py

from kpe import KPE
import utils
import os
from sentence_transformers import SentenceTransformer
import ranker

class KpeRanker:
    def __init__(self):      
        TRAINED_MODEL_ADDR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'trained_model', 'trained_model_10000.pt')
        self.kpe = KPE(trained_kpe_model= TRAINED_MODEL_ADDR, flair_ner_model='flair/ner-english-ontonotes-large', device='cpu')
        self.ranker_transformer = SentenceTransformer('paraphrase-multilingual-mpnet-base-v2',  device='cpu')

 
    def extract(self, text, count, using_ner, return_sorted):
        text = utils.normalize(text)
        kps = self.kpe.extract(text, using_ner=using_ner)    
        if return_sorted:
            kps = ranker.get_sorted_keywords(self.ranker_transformer, text, kps)
        else:
            kps = [(kp, 1) for kp in kps]
        if len(kps) > count:
            kps = kps[:count]
        return kps