| # https://blog.knowledgator.com/meet-the-new-zero-shot-ner-architecture-30ffc2cb1ee0 | |
| # https://labelstud.io/blog/fine-tuning-generalist-models-for-named-entity-recognition/ | |
| import psutil | |
| from gliner import GLiNER | |
| import streamlit as st | |
| LABELS = ["eventTitle", "eventLocation", "date", "time", "street", "city"] | |
| class GlinerHandler: | |
| def __init__(self, model_name="urchade/gliner_multi-v2.1"): | |
| self.model = GLiNER.from_pretrained(model_name) | |
| st.info("Using NER Model Gliner") | |
| def extract_entities(self, text, labels=None, threshold=0.3): | |
| if labels is None: | |
| labels = LABELS | |
| entities = self.model.predict_entities(text, labels, threshold=threshold) | |
| return entities | |
| # gliner = GlinerHandler() | |
| # entities = gliner.extract_entities("Test 20.03.10, In Nürnberg") | |
| # print(entities) |