Instructions to use novakat/nerkor-hubert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use novakat/nerkor-hubert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="novakat/nerkor-hubert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("novakat/nerkor-hubert") model = AutoModelForTokenClassification.from_pretrained("novakat/nerkor-hubert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from novakat/nerkor-hubert: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/novakat/nerkor-hubert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://novakat/nerkor-hubert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/novakat/nerkor-hubert/resolve/main/pytorch_model.bin
440 MB
- Xet hash:
- 832fe90c1d87110a2377be0950558d33de895f9c6bef6b5e75d8b79469c34def
- Size of remote file:
- 440 MB
- SHA256:
- a648dfe4f9f738efaa12d9e1ac6ad926f7e83ea69525a79dd4fc14a7a21df7af
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