NuNER Zero EU AI Act Compliance Profile

NuNER Zero is a zero-shot Named Entity Recognition (NER) Model. (Check NuNER for the few-shot setting).

NuNER Zero uses the GLiNER architecture: its input should be a concatenation of entity types and text.

Unlike GliNER, NuNER Zero is a token classifier, which allows detect arbitrary long entities.

NuNER Zero was trained on NuNER v2.0 dataset, which combines subsets of Pile and C4 annotated via LLMs using NuNER's procedure.

NuNER Zero is (at the time of its release) the best compact zero-shot NER model (+3.1% token-level F1-Score over GLiNER-large-v2.1 on GLiNERS's benchmark)

Installation & Usage

NuZero requires labels to be lower-cased

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 Algor

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