Instructions to use ShuklaGroupIllinois/PeptideESM2_650M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ShuklaGroupIllinois/PeptideESM2_650M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ShuklaGroupIllinois/PeptideESM2_650M")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ShuklaGroupIllinois/PeptideESM2_650M") model = AutoModelForMaskedLM.from_pretrained("ShuklaGroupIllinois/PeptideESM2_650M") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
Please cite Substrate Prediction for RiPP Biosynthetic Enzymes via Masked Language Modeling and Transfer Learning.
@article{Clark2024,
title = {Substrate prediction for RiPP biosynthetic enzymes via masked language modeling and transfer learning},
ISSN = {2635-098X},
url = {http://dx.doi.org/10.1039/D4DD00170B},
DOI = {10.1039/d4dd00170b},
journal = {Digital Discovery},
publisher = {Royal Society of Chemistry (RSC)},
author = {Clark, Joseph D. and Mi, Xuenan and Mitchell, Douglas A. and Shukla, Diwakar},
year = {2024}
}
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