Instructions to use Cabooose/token_classification_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cabooose/token_classification_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Cabooose/token_classification_model")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Cabooose/token_classification_model") model = AutoModelForTokenClassification.from_pretrained("Cabooose/token_classification_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d447c2e2f9ab54fbd6cc789d365cb293d439f05cebb1f8f91b5a33ec5d370c7b
- Size of remote file:
- 3.64 kB
- SHA256:
- 739be59b0b9caed55b5b6475526432d21aa108a86013a80164c97263f2202e2e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.