Instructions to use pierrerappolt-okta/app with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pierrerappolt-okta/app with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="pierrerappolt-okta/app")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("pierrerappolt-okta/app") model = AutoModelForTokenClassification.from_pretrained("pierrerappolt-okta/app", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from pierrerappolt-okta/app: direct link, hf CLI and curl.
- Browser
- Download file 265 MB
-
https://huggingface.co/pierrerappolt-okta/app/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://pierrerappolt-okta/app/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/pierrerappolt-okta/app/resolve/main/pytorch_model.bin
265 MB
- Xet hash:
- a9c54da897f0470554a5f7a60fc5f52beeb5ad6cbdd251bd2e1049607425a73b
- Size of remote file:
- 265 MB
- SHA256:
- fbced37f1e64596616487a8c0f847dcac05e35347363c3fe3a88c0a2a0ecf6ba
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.