Instructions to use multimolecule/rnabert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/rnabert with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/rnabert") model = AutoModel.from_pretrained("multimolecule/rnabert") inputs = tokenizer("UAGCUUAUCAGACUGAUGUUGA", return_tensors="pt") outputs = model(**inputs) embeddings = outputs.last_hidden_stateimport multimolecule from transformers import pipeline predictor = pipeline("fill-mask", model="multimolecule/rnabert") output = predictor("UAGCUUAUCAG<mask>CUGAUGUUGA") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7a808a6991877eafa048a4bd12a5e5208c5806c26f006f0929c533b1ff837ea6
- Size of remote file:
- 2.11 MB
- SHA256:
- 879423f8186148c14fcc3e7eb08c720b5fe148ef6fdc0b3be474cb34f99c9e18
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.