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--- |
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license: mit |
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datasets: |
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- numind/NuNER |
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library_name: gliner |
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language: |
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- en |
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pipeline_tag: token-classification |
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tags: |
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- entity recognition |
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- NER |
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- named entity recognition |
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- zero shot |
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- zero-shot |
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--- |
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NuNerZero - is the family of Zero-Shot Entity Recognition models inspired by [GLiNER](https://huggingface.co/papers/2311.08526) and built with insights we gathered throughout our work on [NuNER](https://huggingface.co/collections/numind/nuner-token-classification-and-ner-backbones-65e1f6e14639e2a465af823b). |
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The key differences between NuNerZero Long in comparison to GLiNER are: |
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* The possibility to **detect entities that are longer than 12 tokens**, as NuZero Token operates on the token level rather than on the span level. |
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* a more powerful version of GLiNER-large-v2.1, surpassing it by **+3.1% on average** |
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* NuNerZero family is trained on the **diverse dataset tailored for real-life use cases** - NuNER v2.0 dataset |
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<p align="center"> |
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<img src="zero_shot_performance_unzero_token.png"> |
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</p> |
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## Installation & Usage |
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``` |
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!pip install gliner |
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``` |
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**NuZero requires labels to be lower-cased** |
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```python |
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from gliner import GLiNER |
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def merge_entities(entities): |
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if not entities: |
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return [] |
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merged = [] |
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current = entities[0] |
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for next_entity in entities[1:]: |
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if next_entity['label'] == current['label'] and (next_entity['start'] == current['end'] + 1 or next_entity['start'] == current['end']): |
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current['text'] = text[current['start']: next_entity['end']].strip() |
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current['end'] = next_entity['end'] |
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else: |
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merged.append(current) |
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current = next_entity |
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# Append the last entity |
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merged.append(current) |
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return merged |
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model = GLiNER.from_pretrained("numind/NuNerZero") |
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# NuZero requires labels to be lower-cased! |
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labels = ["organization", "initiative", "project"] |
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labels = [l.lower() for l in labels] |
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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." |
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entities = model.predict_entities(text, labels) |
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entities = merge_entities(entities) |
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for entity in entities: |
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print(entity["text"], "=>", entity["label"]) |
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``` |
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``` |
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Association for Computing Machinery Special Interest Group on Algorithms and Computation Theory => organization |
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Quantum Computing and Algorithmic Innovations: Shaping the Future of Technology => initiative |
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Global Computational Science Advancement Project => project |
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``` |
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## Fine-tuning |
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A fine-tuning script can be found [here](https://colab.research.google.com/drive/1-hk5AIdX-TZdyes1yx-0qzS34YYEf3d2?usp=sharing). |
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## Citation |
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### This work |
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```bibtex |
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@misc{bogdanov2024nuner, |
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title={NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data}, |
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author={Sergei Bogdanov and Alexandre Constantin and Timothée Bernard and Benoit Crabbé and Etienne Bernard}, |
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year={2024}, |
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eprint={2402.15343}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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### Previous work |
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```bibtex |
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@misc{zaratiana2023gliner, |
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title={GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer}, |
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author={Urchade Zaratiana and Nadi Tomeh and Pierre Holat and Thierry Charnois}, |
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year={2023}, |
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eprint={2311.08526}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
