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from kpe import KPE
import utils
import os
from sentence_transformers import SentenceTransformer
import ranker
from huggingface_hub import hf_hub_download
class KpeRanker:
def __init__(self):
model_path = "/root/.cache/huggingface/hub/models--ahdsoft--persian-keyphrase-extraction-model/trained_model_10000.pt"
if os.path.isfile(file_path):
TRAINED_MODEL_ADDR = model_path
else:
hf_hub_download(repo_id="lysandre/arxiv-nlp", filename="config.json")
TRAINED_MODEL_ADDR = model_path
# 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
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