GLiNER2
Safetensors
extractor
Token Classification
Zero-Shot Classification
Text Classification
relation extraction
Structured extraction
Instructions to use fastino/gliner2-multi-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use fastino/gliner2-multi-v1 with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("fastino/gliner2-multi-v1") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) - Notebooks
- Google Colab
- Kaggle
Supported language list
#1
by rjmehta - opened
Thanks. Any list of supported languages?
x2
x4 (I can report that multi-v1 delivers significantly better results for Dutch :-)
gliner2-multi-v1 should support most of the languages that mdeBERTa supports (100+ languages).
Performance depends on the specific use case, it's up to the user to evaluate it themselves.