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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 074b59641f2846652178870173753faa657706cd0ae2242dde9241c245ebdc7a
- Size of remote file:
- 499 MB
- SHA256:
- d674427b55d9ba7fbebf0efc83383e28a5af890787436ec6c89f0cb6951d1155
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