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I remember very well that about two years ago, 0-shot named entity recognition (i.e. where you can choose any labels on the fly) was completely infeasible. Fast forward a year, and 
I remember very well that about two years ago, 0-shot named entity recognition (i.e. where you can choose any labels on the fly) was completely infeasible. Fast forward a year, and 
resource
https://huggingface.co/Universal-NER/UniNER-7B-all
model
Universal-NER/UniNER-7B-all
https://huggingface.co/Universal-NER/UniNER-7B-all
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 surprised me by showing that 0-shot NER is possible! However, I had a bunch of concerns that prevented me from ever adopting it myself. For example, the model was 7B parameters, only worked with 1 custom label at a time, and it had a cc-by-nc-4.0 license.
 surprised me by showing that 0-shot NER is possible! However, I had a bunch of concerns that prevented me from ever adopting it myself. For example, the model was 7B parameters, only worked with 1 custom label at a time, and it had a cc-by-nc-4.0 license.
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Since then, a little known research paper introduced GLiNER, which was a modified & finetuned variant of the 
Since then, a little known research paper introduced GLiNER, which was a modified & finetuned variant of the 
resource
https://huggingface.co/microsoft/deberta-v3-base
model
microsoft/deberta-v3-base
https://huggingface.co/microsoft/deberta-v3-base
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 line of models. Notably, GLiNER outperforms UniNER-7B, despite being almost 2 orders of magnitude smaller! It also allows for multiple labels at once, supports nested NER, and the models are Apache 2.0.
 line of models. Notably, GLiNER outperforms UniNER-7B, despite being almost 2 orders of magnitude smaller! It also allows for multiple labels at once, supports nested NER, and the models are Apache 2.0.
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Very recently, the models were uploaded to Hugging Face, and I was inspired to create a demo for the English model. The demo runs on CPU, and can still very efficiently compute labels with great performance. I'm very impressed at the models.
Very recently, the models were uploaded to Hugging Face, and I was inspired to create a demo for the English model. The demo runs on CPU, and can still very efficiently compute labels with great performance. I'm very impressed at the models.
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There are two models right now:
There are two models right now:
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* base (english): 
* base (english): 
resource
https://huggingface.co/urchade/gliner_base
model
urchade/gliner_base
https://huggingface.co/urchade/gliner_base
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* multi (multilingual): 
* multi (multilingual): 
resource
https://huggingface.co/urchade/gliner_multi
model
urchade/gliner_multi
https://huggingface.co/urchade/gliner_multi
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And my demo to experiment with the base model can be found here: 
And my demo to experiment with the base model can be found here: 
link
https://huggingface.co/spaces/tomaarsen/gliner_base
https://huggingface.co/spaces/tomaarsen/gliner_base
I remember very well that about two years ago, 0-shot named entity recognition (i.e. where you can choose any labels on the fly) was completely infeasible. Fast forward a year, and https://huggingface.co/Universal-NER/UniNER-7B-all surprised me by showing that 0-shot NER is possible! However, I had a bunch of concerns that prevented me from ever adopting it myself. For example, the model was 7B parameters, only worked with 1 custom label at a time, and it had a cc-by-nc-4.0 license.

Since then, a little known research paper introduced GLiNER, which was a modified & finetuned variant of the https://huggingface.co/microsoft/deberta-v3-base line of models. Notably, GLiNER outperforms UniNER-7B, despite being almost 2 orders of magnitude smaller! It also allows for multiple labels at once, supports nested NER, and the models are Apache 2.0.

Very recently, the models were uploaded to Hugging Face, and I was inspired to create a demo for the English model. The demo runs on CPU, and can still very efficiently compute labels with great performance. I'm very impressed at the models.

There are two models right now:
* base (english): https://huggingface.co/urchade/gliner_base
* multi (multilingual): https://huggingface.co/urchade/gliner_multi

And my demo to experiment with the base model can be found here: https://huggingface.co/spaces/tomaarsen/gliner_base
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Tonic erickdp
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Rajiv Shah
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Deepanshu Sharma
deepsh2207
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Urchade Zaratiana
urchade
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