Token Classification
GLiNER
PyTorch
ONNX
Portuguese
English
ner
pii
mdeberta-v3
span-level
markerV0
legacy
experiment-log
Instructions to use arthrod/c3750-mdeberta_base-vanilla-1-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use arthrod/c3750-mdeberta_base-vanilla-1-2 with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("arthrod/c3750-mdeberta_base-vanilla-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
- .ipynb_checkpoints
- checkpoint-1000
- checkpoint-1250
- checkpoint-1500
- checkpoint-1750
- checkpoint-2000
- checkpoint-2250
- checkpoint-250
- checkpoint-2500
- checkpoint-2750
- checkpoint-3000
- checkpoint-3250
- checkpoint-3500
- checkpoint-3750
- checkpoint-500
- checkpoint-750
- onnx
- 2.57 kB
- 13.3 kB
- 3.31 kB
- 1.27 kB
- 1.16 GB xet
- 16.4 MB xet
- 20.2 kB
- 76.8 kB xet