RNN Baseline
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
- Massachusetts Institute of Technology (MIT),Rey Juan Carlos University
- Organisation type
- Academia,Academia
- Country
- United States of America, Spain
- Published
- 14 July 2019
- Authors
- Sho Takase, Jun Suzuki, Masaaki Nagata
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation
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
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
- Benchmark data
- RNN Baseline
Sources
Where this record came from and when it was last checked.
- Reference
- Character n-Gram Embeddings to Improve RNN Language Models
- Last updated
- 28 November 2025
What the numbers mean
Background
RNN Baseline was published by Massachusetts Institute of Technology (MIT),Rey Juan Carlos University, in the country recorded as United States of America, during July 2019. The publishing organisation is categorised as academia,Academia.
It works in the domain of Language, and is recorded as performing the task of language modeling/generation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
RNN Baseline — common questions
RNN Baseline— when was it released?
It was published in July 2019. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
RNN Baseline— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling/generation. 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.
RNN Baseline— what GPU do I need to run it?
None. This 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.
RNN Baseline— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
RNN Baseline— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
RNN Baseline— who created it?
It was published by Massachusetts Institute of Technology (MIT),Rey Juan Carlos University, based in United States of America, an organisation categorised as academia,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.