src/nlp/experimental/ner/nu_ner.py

from gliner import GLiNER

def merge_entities(entities):
    if not entities:
        return []
    merged = []
    current = entities[0]
    for next_entity in entities[1:]:
        if next_entity['label'] == current['label'] and (next_entity['start'] == current['end'] + 1 or next_entity['start'] == current['end']):
            current['text'] = text[current['start']: next_entity['end']].strip()
            current['end'] = next_entity['end']
        else:
            merged.append(current)
            current = next_entity
    # Append the last entity
    merged.append(current)
    return merged


model = GLiNER.from_pretrained("numind/NuNerZero")

# NuZero requires labels to be lower-cased!
labels = ["organization", "initiative", "project"]
labels = [l.lower() for l in labels]

text = "At the annual technology summit, the keynote address was delivered by a senior member of the Association for Computing Machinery Special Interest Group on Algorithms and Computation Theory, which recently launched an expansive initiative titled 'Quantum Computing and Algorithmic Innovations: Shaping the Future of Technology'. This initiative explores the implications of quantum mechanics on next-generation computing and algorithm design and is part of a broader effort that includes the 'Global Computational Science Advancement Project'. The latter focuses on enhancing computational methodologies across scientific disciplines, aiming to set new benchmarks in computational efficiency and accuracy."

entities = model.predict_entities(text, labels)

entities = merge_entities(entities)

for entity in entities:
    print(entity["text"], "=>", entity["label"])