This modelcard aims to be a base template for new models. It has been generated using this raw template.
Model Details
Model Description
The model is a pre-trained model.This model can be used for finetuning a sequence classificatin model for a binary classification task of predicting
paired TCR-petide-HLA-I binding based on amino acid sequence inputs. It is a transformer model that is built on DeBERTa architecture.
Developed by: Justin Barton and Trupti Gore
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Model type: DeBERTa Transformer
Language(s) (NLP): Python
License: [More Information Needed]
Finetuned from model [optional]: [More Information Needed]
Model Sources [optional]
Repository: [More Information Needed]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]
Uses
How to Use
from transformers import (DebertaForSequenceClassification,DebertaTokenizerFast)
model = DebertaForSequenceClassification.from_pretrained(f'shepherdgroup/nuTCRacker', num_labels=2)
tokenizer=DebertaTokenizerFast.from_pretrained('shepherdgroup/nuTCRacker')
example="'[cdra1]SSVPPY[cdra2]YTSAATLV[cdra3]CAVSAGDYKLSF[cdrb1]KGHDR[cdrb2]SFDVKD[cdrb3]CATSDSVAGNQPQHF','[peptide]ATDALMTGF[mhc]YFAMYQENMAHTDANTLYIIYRDYTWVARVYRGY'"
encoded_example=tokenizer(example,return_tensors='pt')
output=model(**encoded_example)
output
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Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
[More Information Needed]
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
