Instructions to use lauraha/peptide_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lauraha/peptide_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lauraha/peptide_encoder")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lauraha/peptide_encoder") model = AutoModelForMaskedLM.from_pretrained("lauraha/peptide_encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a770572eb7c4e1c025293cab7e1d318c67f7e446bd26f2b48fb61364d3b401d9
- Size of remote file:
- 595 MB
- SHA256:
- d6ba4d71a812c105927020dd8aff8d74e45be6a85e265d775204a8e8d3694276
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