Instructions to use mgh6/TCS_MLM_SaProt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mgh6/TCS_MLM_SaProt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mgh6/TCS_MLM_SaProt")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mgh6/TCS_MLM_SaProt") model = AutoModelForMaskedLM.from_pretrained("mgh6/TCS_MLM_SaProt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mgh6/TCS_MLM_SaProt: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/mgh6/TCS_MLM_SaProt/resolve/5d2affef4c81b7f43715c84ee06830e80098b7e4/training_args.bin
- Command line
-
hf download hf://mgh6/TCS_MLM_SaProt@5d2affef4c81b7f43715c84ee06830e80098b7e4/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mgh6/TCS_MLM_SaProt/resolve/5d2affef4c81b7f43715c84ee06830e80098b7e4/training_args.bin
5.18 kB
- Xet hash:
- d88e60dc6458e7cdb92d904deb9ada6a43dbaa622919afb8a246b5035581d5e8
- Size of remote file:
- 5.18 kB
- SHA256:
- 4840eb255419b6a4fe1195daf6ab9d725525660e993088713d953a74afaa7c0f
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