Instructions to use multimolecule/dnabert2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/dnabert2 with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/dnabert2") model = AutoModel.from_pretrained("multimolecule/dnabert2") inputs = tokenizer("ACTCCCCTGCCCTCAACAAGATGTTTTGCCAACTGGCCAAGACCTGCCCTGTGCAGCTGTGGGTTGATTCCACACCCCCGCCCGGCACCCGCGTCCGCGCCATGGCCATCTACAAGCAGTCACAGCACATGACGGAGGTTGTGAGGCGCTGCCCCCACCATGAGCGCTGCTCAGATAGCGATGG", return_tensors="pt") outputs = model(**inputs) embeddings = outputs.last_hidden_stateimport multimolecule from transformers import pipeline predictor = pipeline("fill-mask", model="multimolecule/dnabert2") output = predictor("ACTCCCCTGCCCTC<mask>ACAAGATGTTTTGCCAACTGGCCAAGACCTGCCCTGTGCAGCTGTGGGTTGATTCCACACCCCCGCCCGGCACCCGCGTCCGCGCCATGGCCATCTACAAGCAGTCACAGCACATGACGGAGGTTGTGAGGCGCTGCCCCCACCATGAGCGCTGCTCAGATAGCGATGG") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8644bd214ad48f469c9285bef9ea5634b883c7bcb9e25977c5a405e04ff54ee4
- Size of remote file:
- 468 MB
- SHA256:
- c98d46ae2bf6fbd37b4d85791ea9466aaa5f8c1c0905d156b00a5ad3147318e0
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