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Luqman bin Daut

luqman2511
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updated a model 23 days ago
Luq2211/MmlLoq1.0
liked a model 10 months ago
Luq2211/MmlLoq1.0
reacted to tomaarsen's post with ❤️ 10 months ago
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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