Text Classification
GLiNER2
GLiNER
English
relation_verifier
relation-extraction
ner
information-extraction
Instructions to use oneryalcin/gliner2-relation-verifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use oneryalcin/gliner2-relation-verifier with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("oneryalcin/gliner2-relation-verifier") # 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 oneryalcin/gliner2-relation-verifier with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("oneryalcin/gliner2-relation-verifier") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "relation_verifier", | |
| "input_dim": 3105, | |
| "hidden_dim": 768, | |
| "dropout": 0.1, | |
| "default_threshold": 0.55, | |
| "encoder_name": "microsoft/deberta-v3-base", | |
| "distance_buckets": [0, 5, 10, 20, 50], | |
| "architectures": ["RelationVerifierModel"], | |
| "version": "2.0" | |
| } | |