Instructions to use HBDX/Seq-TransfoRNA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HBDX/Seq-TransfoRNA with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("HBDX/Seq-TransfoRNA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download scripts/test_inference_api.py from HBDX/Seq-TransfoRNA: direct link, hf CLI and curl.
- Browser
- Download file 1.32 kB
-
https://huggingface.co/HBDX/Seq-TransfoRNA/resolve/main/scripts/test_inference_api.py
- Command line
-
hf download hf://HBDX/Seq-TransfoRNA/scripts/test_inference_api.py
-
curl -L -o test_inference_api.py https://huggingface.co/HBDX/Seq-TransfoRNA/resolve/main/scripts/test_inference_api.py
1.32 kB
| from transforna import predict_transforna, predict_transforna_all_models | |
| seqs = [ | |
| 'AACGAAGCTCGACTTTTAAGG', | |
| 'GTCCACCCCAAAGCGTAGG'] | |
| path_to_models = '/path/to/tcga/models/' | |
| sc_preds_id_df = predict_transforna_all_models(seqs,path_to_models = path_to_models) #/models/tcga/ | |
| #%% | |
| #get sc predictions for models trained on id (in distribution) | |
| sc_preds_id_df = predict_transforna(seqs, model="seq",trained_on='id',path_to_models = path_to_models) | |
| #get sc predictions for models trained on full (all sub classes) | |
| sc_preds_df = predict_transforna(seqs, model="seq",path_to_models = path_to_models) | |
| #predict using models trained on major class | |
| mc_preds_df = predict_transforna(seqs, model="seq",mc_or_sc='major_class',path_to_models = path_to_models) | |
| #get logits | |
| logits_df = predict_transforna(seqs, model="seq",logits_flag=True,path_to_models = path_to_models) | |
| #get embedds | |
| embedd_df = predict_transforna(seqs, model="seq",embedds_flag=True,path_to_models = path_to_models) | |
| #get the top 4 similar sequences | |
| sim_df = predict_transforna(seqs, model="seq",similarity_flag=True,n_sim=4,path_to_models = path_to_models) | |
| #get umaps | |
| umaps_df = predict_transforna(seqs, model="seq",umaps_flag=True,path_to_models = path_to_models) | |
| all_preds_df = predict_transforna_all_models(seqs,path_to_models=path_to_models) | |
| all_preds_df | |
| # %% | |