numind / NuNER_Zero-span

NuNER Zero-span is the span-prediction version of NuNER Zero .

NuNER Zero-span shows slightly better performance than NuNER Zero but cannot detect entities that are larger than 12 tokens.

Installation & Usage

!pip install gliner

NuZero requires labels to be lower-cased

from gliner import GLiNER

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

# 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)

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

Association for Computing Machinery Special Interest Group on Algorithms and Computation Theory => organization
Quantum Computing and Algorithmic Innovations: Shaping the Future of Technology => initiative
Global Computational Science Advancement Project => project

Fine-tuning

A fine-tuning script can be found here .

Citation

This work

@misc{bogdanov2024nuner,
      title={NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data}, 
      author={Sergei Bogdanov and Alexandre Constantin and Timothée Bernard and Benoit Crabbé and Etienne Bernard},
      year={2024},
      eprint={2402.15343},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Previous work

@misc{zaratiana2023gliner,
      title={GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer}, 
      author={Urchade Zaratiana and Nadi Tomeh and Pierre Holat and Thierry Charnois},
      year={2023},
      eprint={2311.08526},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}