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")# 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fd8f8616dcf54b351bee9e5e5dcef6d862cfdbafedb3899d716c975c604acbc0
- Size of remote file:
- 781 MB
- SHA256:
- b0b7f2e5c260ab9ff14c736eb6ec84ccef932186c10387dd8494bf8a3cf4ec5f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.