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--- |
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tags: |
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- pytorch_model_hub_mixin |
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- model_hub_mixin |
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license: gpl-3.0 |
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--- |
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This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: |
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- Library: [More Information Needed] |
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- Docs: [More Information Needed] |
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## Steps to run model |
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- First install [transforna](https://github.com/gitHBDX/TransfoRNA/tree/master) |
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- Example code: |
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``` |
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from transforna import GeneEmbeddModel,RnaTokenizer |
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import torch |
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model_name = 'Seq-Struct' |
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model_path = f"HBDX/{model_name}-TransfoRNA" |
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#load model and tokenizer |
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model = GeneEmbeddModel.from_pretrained(model_path) |
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model.eval() |
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#init tokenizer. |
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tokenizer = RnaTokenizer.from_pretrained(model_path,model_name=model_name) |
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output = tokenizer(['AAAGTCGGAGGTTCGAAGACGATCAGATAC','TTTTCGGAACTGAGGCCATGATTAAGAGGG']) |
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#inference |
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#gene_embedds and second input embedds are the latent space representation of the input sequence's odd tokens and even tokens respectively. |
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#In this case, the second input would be the secondary structure of the sequence |
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gene_embedd, second_input_embedd, activations,attn_scores_first,attn_scores_second = \ |
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model(output['input_ids']) |
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#get sub class labels |
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sub_class_labels = model.convert_ids_to_labels(activations) |
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#get major class labels |
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major_class_labels = model.convert_subclass_to_majorclass(sub_class_labels) |
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``` |
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