AI models
BERT-RBP
Training compute
1.4×10²⁰ FLOP
Parameters
110M
Published
Apr 7, 2022
BERT-RBP is an AI model developed by Waseda University (Japan), first published in April 2022. It works in the biology domain, on tasks such as proteins, protein interaction prediction and rna-protein interaction prediction.
Training it took an estimated 1.4×10²⁰ FLOP of compute (estimation method: hardware). The model has 110,000,000 parameters.
Access: Open weights (non-commercial). Its weights are openly available. It is built on top of DNABERT. The reference paper has 68 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Waseda University
- Country of organization
- Japan
- Domain
- Biology
- Task
- Proteins, Protein interaction prediction, RNA-Protein interaction prediction
- Training compute
- 1.4×10²⁰ FLOP
- Compute estimation method
- Hardware
- Parameters
- 110,000,000
- Model accessibility
- Open weights (non-commercial)
- Open weights
- Yes
- Base model
- DNABERT
- Citations
- 68
- Epoch confidence
- Confident
SourceEpoch AI, 'AI Models'. Published online at epoch.ai. Retrieved 2026-07-29 from https://epoch.ai/data/ai-models. Licensed under CC BY 4.0.
← All ai models