Token Classification
GLiNER2
Safetensors
GLiNER
English
ner
named-entity-recognition
world-bank
datasets
data-use
lora
adapter
forced-displacement
refugees
fcv
Instructions to use ai4data/datause-extraction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use ai4data/datause-extraction with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("ai4data/datause-extraction") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) - GLiNER
How to use ai4data/datause-extraction with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("ai4data/datause-extraction") 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