metadata
tags:
- pytorch_model_hub_mixin
- model_hub_mixin
license: gpl-3.0
This model has been pushed to the Hub using the PytorchModelHubMixin integration:
- Library: [More Information Needed]
- Docs: [More Information Needed]
Steps to run model
- First install transforna
- Example code:
from transforna import GeneEmbeddModel,RnaTokenizer
import torch
model_name = 'Seq-Struct'
model_path = f"HBDX/{model_name}-TransfoRNA"
#load model and tokenizer
model = GeneEmbeddModel.from_pretrained(model_path)
model.eval()
#init tokenizer. Tokenizer will automatically get secondary structure of sequence using Vienna RNA package
tokenizer = RnaTokenizer.from_pretrained(model_path,model_name=model_name)
output = tokenizer(['AAAGTCGGAGGTTCGAAGACGATCAGATAC','TTTTCGGAACTGAGGCCATGATTAAGAGGG'])
#inference
#gene_embedds and second input embedds are the latent space representation of the input sequence and the second input respectively.
#In this case, the second input would be the secondary structure of the sequence
gene_embedd, second_input_embedd, activations,attn_scores_first,attn_scores_second = \
model(output['input_ids'])
#get sub class labels
sub_class_labels = model.convert_ids_to_labels(activations)
#get major class labels
major_class_labels = model.convert_subclass_to_majorclass(sub_class_labels)