Instructions to use ronig/protein_biencoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ronig/protein_biencoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ronig/protein_biencoder", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ronig/protein_biencoder", trust_remote_code=True) model = AutoModel.from_pretrained("ronig/protein_biencoder", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
updating model peptriever_2023-06-23T16:07:24.508460
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README.md
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## Peptriever
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The model and training process is outlined in [this application note](). Training code can be found [here](https://github.com/RoniGurvich/Peptriever).
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## Usage
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datasets:
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- ronig/protein_binding_sequences
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## Peptriever: A Bi-Encoder for large-scale protein-peptide binding search
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For training details see our [Application Note](https://www.biorxiv.org/content/10.1101/2023.07.13.548811v1).
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Training code can be found in our [Github repo](https://github.com/RoniGurvich/Peptriever).
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A live demo is available on our [application page](https://peptriever.app)
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## Usage
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