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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 | |