GLiNER2
Safetensors
English
French
Spanish
extractor
Text classification
Named Entity Recognition
Relation Extraction
Intent classification
Sentiment Analysis
Topic classification
Structured extraction
Json extraction
Instructions to use fastino/gliner2-large-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use fastino/gliner2-large-v1 with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("fastino/gliner2-large-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
Language coverage?
#2
by tadusko - opened
Hi! Thanks for providing an interesting model.
To be clear about language coverage: is it trained with only/mostly English examples or is there multilingual support? Which languages if there is?
Sorry if I just missed this, I couldn't find it in the preprint.
urchade changed discussion status to closed