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 GLiNER2 model = GLiNER2.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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8af23d2dce752caa2b83b12c7d9f4cdf50b072356884a6f56a14b21507a39a01
- Size of remote file:
- 30.4 MB
- SHA256:
- 219080a9707cbffafd5b4548b1fbe6723798ae2f622ea57d13d4a42f0ca02e51
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