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:
- 3823dd6ce939745dcd00769aabca00a1c7c2fd5ed32817482329117ec98a6319
- Size of remote file:
- 468 MB
- SHA256:
- 6564b64449d139b545840a1a958cabe02bf3b09cf35417b5d3e2fe4c8521ca80
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