Instructions to use Katia2001/my_awesome_wnut_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Katia2001/my_awesome_wnut_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Katia2001/my_awesome_wnut_model")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Katia2001/my_awesome_wnut_model") model = AutoModelForTokenClassification.from_pretrained("Katia2001/my_awesome_wnut_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Katia2001/my_awesome_wnut_model: direct link, hf CLI and curl.
- Browser
- Download file 5.2 kB
-
https://huggingface.co/Katia2001/my_awesome_wnut_model/resolve/main/training_args.bin
- Command line
-
hf download hf://Katia2001/my_awesome_wnut_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Katia2001/my_awesome_wnut_model/resolve/main/training_args.bin
5.2 kB
- Xet hash:
- 119eee721d6b1c2149f0e672541f8603bc9b6657350db3b0730da01e62b53bab
- Size of remote file:
- 5.2 kB
- SHA256:
- 4ef0164f3eb6f08d82c5e6e9d32cf2aa0131cbfc485b25cdaa4daa4caa2b3d04
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