Instructions to use multimolecule/bpnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/bpnet with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/bpnet") model = AutoModel.from_pretrained("multimolecule/bpnet") inputs = tokenizer("ACTCCCCTGCCCTCAACAAGATGTTTTGCCAACTGGCCAAGACCTGCCCTGTGCAGCTGTGGGTTGATTCCACACCCCCGCCCGGCACCCGCGTCCGCGCCATGGCCATCTACAAGCAGTCACAGCACATGACGGAGGTTGTGAGGCGCTGCCCCCACCATGAGCGCTGCTCAGATAGCGATGG", return_tensors="pt") outputs = model(**inputs) embeddings = outputs.last_hidden_state - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c6801a887d34937e981adf9d95ca9ca9fb112a61374ca466fb9aa79f85940f70
- Size of remote file:
- 544 kB
- SHA256:
- 4daec9388c9e759a5417d63d542bd419ff9e445d24762990d98ea5e3931131b8
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