Instructions to use lathashree01/my_awesome_wnut_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lathashree01/my_awesome_wnut_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="lathashree01/my_awesome_wnut_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("lathashree01/my_awesome_wnut_model") model = AutoModelForTokenClassification.from_pretrained("lathashree01/my_awesome_wnut_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from lathashree01/my_awesome_wnut_model: direct link, hf CLI and curl.
- Browser
- Download file 3.84 kB
-
https://huggingface.co/lathashree01/my_awesome_wnut_model/resolve/main/training_args.bin
- Command line
-
hf download hf://lathashree01/my_awesome_wnut_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/lathashree01/my_awesome_wnut_model/resolve/main/training_args.bin
3.84 kB
- Xet hash:
- 25a3e94273c8013d25c4dd60d22fd095a15386489e16500f01c4dd8927dc7941
- Size of remote file:
- 3.84 kB
- SHA256:
- 9b11f49f3106b5a4a99170e73e359f8bfa55bbdefdc2f2591da4fdf6ac51e062
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