Instructions to use multimolecule/bpfold with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/bpfold with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/bpfold") model = AutoModel.from_pretrained("multimolecule/bpfold") inputs = tokenizer("UAGCUUAUCAGACUGAUGUUGA", return_tensors="pt") outputs = model(**inputs) embeddings = outputs.last_hidden_stateimport multimolecule from transformers import pipeline predictor = pipeline("rna-secondary-structure", model="multimolecule/bpfold") output = predictor("UAGCUUAUCAGACUGAUGUUGA") print(output["secondary_structure"]) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c366567bdff814edbe5d62941cc3b2dcd16ff410335731e1da1c0c89c4b6221a
- Size of remote file:
- 194 MB
- SHA256:
- cbd854621e3d66b2f34ee530d1d069966b9c282c31765d5a060c162ac1efdb19
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.