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 
text
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
Universal-NER/UniNER-7B-all
model
resource
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.
text
 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.
new_line
new_line
Since then, a little known research paper introduced GLiNER, which was a modified & finetuned variant of the 
text
Since then, a little known research paper introduced GLiNER, which was a modified & finetuned variant of the 
https://huggingface.co/microsoft/deberta-v3-base
microsoft/deberta-v3-base
model
resource
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.
text
 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.
new_line
new_line
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.
text
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.
new_line
new_line
There are two models right now:
text
There are two models right now:
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* base (english): 
text
* base (english): 
https://huggingface.co/urchade/gliner_base
urchade/gliner_base
model
resource
https://huggingface.co/urchade/gliner_base
new_line
* multi (multilingual): 
text
* multi (multilingual): 
https://huggingface.co/urchade/gliner_multi
urchade/gliner_multi
model
resource
https://huggingface.co/urchade/gliner_multi
new_line
new_line
And my demo to experiment with the base model can be found here: 
text
And my demo to experiment with the base model can be found here: 
https://huggingface.co/spaces/tomaarsen/gliner_base
https://huggingface.co/spaces/tomaarsen/gliner_base
link
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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