Instructions to use Amna100/DebertaMLMnc2c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Amna100/DebertaMLMnc2c with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Amna100/DebertaMLMnc2c")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Amna100/DebertaMLMnc2c") model = AutoModelForMaskedLM.from_pretrained("Amna100/DebertaMLMnc2c", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Amna100/DebertaMLMnc2c: direct link, hf CLI and curl.
- Browser
- Download file 4.6 kB
-
https://huggingface.co/Amna100/DebertaMLMnc2c/resolve/main/training_args.bin
- Command line
-
hf download hf://Amna100/DebertaMLMnc2c/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Amna100/DebertaMLMnc2c/resolve/main/training_args.bin
4.6 kB
- Xet hash:
- 2184bc5dd3d9f3c80ed9ba4cc63cdc61e379132093c38516b4bdc5389dc75143
- Size of remote file:
- 4.6 kB
- SHA256:
- 1a6ee3e080fd515c3ea60532453923b3eb076bf053710d5b35ca3dde35315f41
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