Token Classification
GLiNER
PyTorch
ONNX
Portuguese
English
ner
pii
mdeberta-v3
span-level
markerV0
domain-adaptation
spam
legacy
experiment-log
Instructions to use arthrod/c5k-deberta_base-token_level-1-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use arthrod/c5k-deberta_base-token_level-1-2 with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("arthrod/c5k-deberta_base-token_level-1-2") 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
Ctrl+K
- checkpoint-1000
- checkpoint-1500
- checkpoint-2000
- checkpoint-2500
- checkpoint-3000
- checkpoint-3500
- checkpoint-4000
- checkpoint-4500
- checkpoint-500
- checkpoint-5000
- checkpoint-5500
- checkpoint-6000
- checkpoint-6500
- onnx
- 2.44 kB
- 13.9 kB
- 2.76 kB
- 1.17 kB
- 1.16 GB xet
- 8.56 kB
- 16 MB xet
- 2.9 kB
- 5.59 kB xet
- 18.8 kB xet
- 18.6 kB