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nuTCRacker model (pre-trained)
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]
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- 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
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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.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training hyperparameters
vocab_size=len(tokenizer),
num_attention_heads=8,
num_hidden_layers=16,
hidden_size=512,
intermediate_size=2048,
hidden_act='gelu',
hidden_dropout_prob=0.15,
relative_attention=True,
pos_att_type='c2p|p2c',
max_relative_positions=-1,
position_biased_input=False,
attention_probs_dropout_prob=0.15,
initializer_range=0.02,
layer_norm_eps=1e-7,
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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Citation [optional]
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Glossary [optional]
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Model Card Authors [optional]
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Model Card Contact
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