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  - zero-shot
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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 Token Long in comparison to GLiNER are:
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- * **4096 context window!** vs 512-token context in GLiNER. This allows processing a page at a time vs a few sentences!
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- * The possibility to **detect entities that are longer than 12 tokens**, as NuNerZero, it 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 +1% on average
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- * NuZero 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_long.png">
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  </p>
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  ## Installation & Usage
 
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  - zero-shot
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+ NuNER Zero 4k is the long-context (4k tokens) version of [NuNER Zero](https://huggingface.co/numind/NuNER_Zero).
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+ Its performance on short texts are a bit less good than NuNER Zero's, but can outperform NuNER Zero on longer text.
 
 
 
 
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  <p align="center">
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+ <img src="zero_shot_performance_unzero_token_long.png" width="600">
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  </p>
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  ## Installation & Usage