Instructions to use be-dantonio/model_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use be-dantonio/model_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="be-dantonio/model_3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("be-dantonio/model_3") model = AutoModelForTokenClassification.from_pretrained("be-dantonio/model_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from be-dantonio/model_3: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/be-dantonio/model_3/resolve/main/training_args.bin
- Command line
-
hf download hf://be-dantonio/model_3/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/be-dantonio/model_3/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- d86f80e77ab5a3dcc87ef3b2ca1224449c4a0e898c6a62aa47c6c431fa73f47b
- Size of remote file:
- 3.06 kB
- SHA256:
- deab6a9ef95c6845ccdc93cca733afd4fa9a69d99b4efcc681a9e4c24b82aa25
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