Instructions to use EMBO/bio-lm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EMBO/bio-lm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="EMBO/bio-lm")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("EMBO/bio-lm") model = AutoModelForMaskedLM.from_pretrained("EMBO/bio-lm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 253e4d999200b931ea0277e4e32cc9a89b964815c0a631dcf890691c7a97edf1
- Size of remote file:
- 1.9 kB
- SHA256:
- b592a56d29de8e195373397ab8bc32bc0ae504abcabb64016a9064b06f350b2e
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