PLUS-RNN
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Seoul National University,LG AI Research,NAVER,Kangwon National University
- Organisation type
- Academia,Industry,Industry,Academia
- Country
- Korea (Republic of)
- Published
- 3 September 2021
- Authors
- Seonwoo Min, Seunghyun Park, Siwon Kim, Hyun-Soo Choi, Byunghan Lee, Sungroh Yoon
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein or nucleotide language model (pLM/nLM)
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
14,670,860 protein sequences × 150 amino acids/sequence = 2.2 billion datapoints (2.2 × 10^9 total tokens)
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 69
Sources
Where this record came from and when it was last checked.
- Reference
- Pre-Training of Deep Bidirectional Protein Sequence Representations With Structural Information
- Last updated
- 28 November 2025
What the numbers mean
What this model is
PLUS-RNN was published by Seoul National University,LG AI Research,NAVER,Kangwon National University, in Korea (Republic of), in September 2021. The organisation is categorised as academia,Industry,Industry,Academia.
It works in Biology, and is recorded as doing protein or nucleotide language model (pLM/nLM).
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
PLUS-RNN — common questions
When was PLUS-RNN released?
PLUS-RNN was published in September 2021. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is PLUS-RNN used for?
PLUS-RNN works in Biology, and is recorded as handling protein or nucleotide language model (pLM/nLM). A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
What GPU do I need to run PLUS-RNN?
None. PLUS-RNN is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is PLUS-RNN open source?
The licensing for PLUS-RNN was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does PLUS-RNN have?
No parameter count has been published for PLUS-RNN, which is why no memory or speed figure appears on this page.
Who created PLUS-RNN?
PLUS-RNN was published by Seoul National University,LG AI Research,NAVER,Kangwon National University, based in Korea (Republic of), categorised as academia,Industry,Industry,Academia.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.