| --- |
| tags: |
| - pytorch_model_hub_mixin |
| - model_hub_mixin |
| license: gpl |
| --- |
| |
| This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: |
| - Library: [More Information Needed] |
| - Docs: [More Information Needed] |
|
|
| ## Steps to run model |
| - First install [transforna](https://github.com/gitHBDX/TransfoRNA/tree/master) |
| - Example code: |
| ``` |
| from transforna import GeneEmbeddModel,RnaTokenizer |
| import torch |
| model_name = 'Seq' |
| model_path = f"HBDX/{model_name}-TransfoRNA" |
| |
| #load model and tokenizer |
| model = GeneEmbeddModel.from_pretrained(model_path) |
| model.eval() |
| |
| #init tokenizer. |
| tokenizer = RnaTokenizer.from_pretrained(model_path,model_name=model_name) |
| output = tokenizer(['AAAGTCGGAGGTTCGAAGACGATCAGATAC','TTTTCGGAACTGAGGCCATGATTAAGAGGG']) |
| |
| #inference |
| #gene_embedds is the latent space representation of the input sequence. |
| |
| gene_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) |
| |
| ``` |
|
|
