Token Classification
GLiFormer
PyTorch
GLiNER
English
deberta
named-entity-recognition
text-classification
relation-extraction
structured-extraction
feature-extraction
document-understanding
Instructions to use knowledgator/gliformer-base-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiFormer
How to use knowledgator/gliformer-base-v1 with GLiFormer:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- GLiNER
How to use knowledgator/gliformer-base-v1 with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("knowledgator/gliformer-base-v1") 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
Demo for this model on Spaces
#1 opened 22 days ago
by
multimodalart