Instructions to use urchade/gliner_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use urchade/gliner_base with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("urchade/gliner_base") text = "Cristiano Ronaldo dos Santos Aveiro was born on 5 February 1985 in Funchal, Madeira, Portugal." labels = ["person", "date", "location"] entities = model.predict_entities(text, labels) for entity in entities: print(entity["text"], "=>", entity["label"]) - Notebooks
- Google Colab
- Kaggle
Incorrect number of parameters in models?
#12
by UssuHug - opened
I’ve worked with the gliner_medium and gliner_multi models, and although their model cards state that both have 209M parameters, I found this isn't accurate. The number of trainable parameters is actually 195M for gliner_medium and 289M for gliner_multi. This discrepancy is at least due to the difference in vocabulary sizes: 128,004 for gliner_medium and 250,105 for gliner_multi. I encountered this issue while comparing the quantized versions of the models and trying to understand the size differences between them.