Instructions to use Amna100/fold_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Amna100/fold_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Amna100/fold_3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Amna100/fold_3") model = AutoModelForTokenClassification.from_pretrained("Amna100/fold_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Amna100/fold_3: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/Amna100/fold_3/resolve/main/training_args.bin
- Command line
-
hf download hf://Amna100/fold_3/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Amna100/fold_3/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- 5514e48e3928a2232afa524102cebf2a94dcef87d28a431a97a4af713da7a18a
- Size of remote file:
- 5.18 kB
- SHA256:
- f01d4dfcac57d0674c43f522a42bc190f1f5a2e7e0a79e9c178f5c4f09037fa7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.