Instructions to use multimolecule/dnabert-3mer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/dnabert-3mer with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/dnabert-3mer") model = AutoModel.from_pretrained("multimolecule/dnabert-3mer") inputs = tokenizer("ACTCCCCTGCCCTCAAAGATGTTTTGCCAACTGGCCAAGACCTGCCCTGTGCAGCTGTGGGTTGATTCCACACCCCCGCCCGGCACCCGCGTCCGCGCCATGGCCATCTACAAGCAGTCACAGCACATGACGGAGGTTGTGAGGCGCTGCCCCCACCATGAGCGCTGCTCAGATAGCGATGG", return_tensors="pt") outputs = model(**inputs) embeddings = outputs.last_hidden_stateimport multimolecule from transformers import pipeline predictor = pipeline("fill-mask", model="multimolecule/dnabert-3mer") output = predictor("ACTCCCCTGCCCTC<mask>AAGATGTTTTGCCAACTGGCCAAGACCTGCCCTGTGCAGCTGTGGGTTGATTCCACACCCCCGCCCGGCACCCGCGTCCGCGCCATGGCCATCTACAAGCAGTCACAGCACATGACGGAGGTTGTGAGGCGCTGCCCCCACCATGAGCGCTGCTCAGATAGCGATGG") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 046de9d4361f70978404659aaa04f1529a6ef1dfbd7ff89733045182e8422526
- Size of remote file:
- 344 MB
- SHA256:
- d3818e596038d71061ec561a6d68f6e56482882767946dda151e6321a353813c
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