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:
- b8d62389b229ae3616bade072160dc255a359393c71a9b91d112c6887283c0e2
- Size of remote file:
- 499 MB
- SHA256:
- 2293ca53e176ebe5358b3ba8b73a1a1af4d696a4bcbd64b2457d2b1c5034a076
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