Rahul

Rjp97

AI & ML interests

None yet

Recent Activity

updated a model 10 days ago

Rjp97/microsoft-deberta-v3-large

published a model 10 days ago

Rjp97/microsoft-deberta-v3-large

replied to urchade's post 8 months ago

**Some updates on GLiNER** 🆕 A new commercially permissible multilingual version is available urchade/gliner_multiv2.1 🐛 A subtle bug that causes performance degradation on some models has been corrected. Thanks to @yyDing1 for raising the issue. ``` from gliner import GLiNER # Initialize GLiNER model = GLiNER.from_pretrained("urchade/gliner_multiv2.1") text = "This is a text about Bill Gates and Microsoft." # Labels for entity prediction labels = ["person", "organization", "email"] entities = model.predict_entities(text, labels, threshold=0.5) for entity in entities: print(entity["text"], "=>", entity["label"]) ```

View all activity

Organizations

None yet

Rjp97's activity

updated a model 10 days ago

Rjp97/microsoft-deberta-v3-large

Text Classification • Updated 10 days ago • 8

published a model 10 days ago

Rjp97/microsoft-deberta-v3-large

Text Classification • Updated 10 days ago • 8

replied to urchade's post 8 months ago

I have domain specific usecase. So i need to fine-tune the model. Is it possible to fine-tune this model urchade/gliner_multiv2.1. If so, how should be the structure of the training dataset. IS there any documents where i can refer?