Instructions to use multimolecule/utrbert-6mer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/utrbert-6mer with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/utrbert-6mer") model = AutoModel.from_pretrained("multimolecule/utrbert-6mer") inputs = tokenizer("UAGCUUAUCAGAGUUGA", return_tensors="pt") outputs = model(**inputs) embeddings = outputs.last_hidden_stateimport multimolecule from transformers import pipeline predictor = pipeline("fill-mask", model="multimolecule/utrbert-6mer") output = predictor("UAGCUUAUCAG<mask>GUUGA") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from multimolecule/utrbert-6mer: direct link, hf CLI and curl.
- Browser
- Download file 395 MB
-
https://huggingface.co/multimolecule/utrbert-6mer/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://multimolecule/utrbert-6mer/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/multimolecule/utrbert-6mer/resolve/main/pytorch_model.bin
395 MB
- Xet hash:
- b872e44186c6c086912b351fb02ac1419f480df3efd72f6ebd44330739e642ee
- Size of remote file:
- 395 MB
- SHA256:
- 0d1715cfea6b66dfd80d546faf3a12e794f42504d68070b341ae448fb56957b0
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