Token Classification
Transformers
PyTorch
GLiNER
English
code
finance
named-entity-recognition
argilla
Instructions to use bhums/argilla_token_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bhums/argilla_token_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="bhums/argilla_token_classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bhums/argilla_token_classifier", device_map="auto") - GLiNER
How to use bhums/argilla_token_classifier with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("bhums/argilla_token_classifier") 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