Instructions to use multimolecule/dnabert-6mer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/dnabert-6mer with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/dnabert-6mer") model = AutoModel.from_pretrained("multimolecule/dnabert-6mer") inputs = tokenizer("ACTCCCCTGCCCTCAATGTTTTGCCAACTGGCCAAGACCTGCCCTGTGCAGCTGTGGGTTGATTCCACACCCCCGCCCGGCACCCGCGTCCGCGCCATGGCCATCTACAAGCAGTCACAGCACATGACGGAGGTTGTGAGGCGCTGCCCCCACCATGAGCGCTGCTCAGATAGCGATGG", return_tensors="pt") outputs = model(**inputs) embeddings = outputs.last_hidden_stateimport multimolecule from transformers import pipeline predictor = pipeline("fill-mask", model="multimolecule/dnabert-6mer") output = predictor("ACTCCCCTGCCCTC<mask>ATGTTTTGCCAACTGGCCAAGACCTGCCCTGTGCAGCTGTGGGTTGATTCCACACCCCCGCCCGGCACCCGCGTCCGCGCCATGGCCATCTACAAGCAGTCACAGCACATGACGGAGGTTGTGAGGCGCTGCCCCCACCATGAGCGCTGCTCAGATAGCGATGG") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 28572077096d2e461e5a78eff862f32d3114abdc3c22b20527688cb9533e3fc9
- Size of remote file:
- 357 MB
- SHA256:
- 9178e184f81fd848ec569b7f5a11a0abe6a083b44b6504cf128abefbc232f499
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