Instructions to use yangheng/rnabert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use yangheng/rnabert with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("yangheng/rnabert") model = AutoModel.from_pretrained("yangheng/rnabert") inputs = tokenizer("UAGCAUAUCAGACUGAUGUUGA", return_tensors="pt") outputs = model(**inputs) embeddings = outputs.last_hidden_stateimport multimolecule from transformers import pipeline predictor = pipeline("fill-mask", model="yangheng/rnabert") output = predictor("UAGC<mask>UAUCAGACUGAUGUUGA") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 19ca52955a038cfdbfca8884e8aac37bb674adfb73e0bfe9a97b7f6dca1dbcd4
- Size of remote file:
- 2.17 MB
- SHA256:
- 7473e7b3cbf3a61a4d6c737d441d02060a3b01136bf88d0759c5ea2cf7760192
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.