Instructions to use multimolecule/dnabert-5mer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/dnabert-5mer with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/dnabert-5mer") model = AutoModel.from_pretrained("multimolecule/dnabert-5mer") inputs = tokenizer("ACTCCCCTGCCCTCAGATGTTTTGCCAACTGGCCAAGACCTGCCCTGTGCAGCTGTGGGTTGATTCCACACCCCCGCCCGGCACCCGCGTCCGCGCCATGGCCATCTACAAGCAGTCACAGCACATGACGGAGGTTGTGAGGCGCTGCCCCCACCATGAGCGCTGCTCAGATAGCGATGG", return_tensors="pt") outputs = model(**inputs) embeddings = outputs.last_hidden_stateimport multimolecule from transformers import pipeline predictor = pipeline("fill-mask", model="multimolecule/dnabert-5mer") output = predictor("ACTCCCCTGCCCTC<mask>GATGTTTTGCCAACTGGCCAAGACCTGCCCTGTGCAGCTGTGGGTTGATTCCACACCCCCGCCCGGCACCCGCGTCCGCGCCATGGCCATCTACAAGCAGTCACAGCACATGACGGAGGTTGTGAGGCGCTGCCCCCACCATGAGCGCTGCTCAGATAGCGATGG") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 415781ba3f38f1512c83a67ee53758ccb9b140cd145b0d7c22a4ad9b9b3f8320
- Size of remote file:
- 347 MB
- SHA256:
- c9b593256624ed0908d7c6285c6964772b7089107a041de7dd3b0f1af3d1955a
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