SCRN (Structurally Constrained Recurrent Network)
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
- Facebook AI Research
- Organisation type
- Industry
- Country
- United States of America, France
- Published
- 24 December 2014
- Authors
- Tomas Mikolov, Armand Joulin, Sumit Chopra, Michael Mathieu, Marc'Aurelio Ranzato
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
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.
- Parameters
- 26.5M
- 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
- Open source
code, BSD license: https://github.com/facebookarchive/SCRNNs
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 306
- Benchmark data
- SCRN(Structurally Constrained Recurrent Network)
Sources
Where this record came from and when it was last checked.
- Reference
- Learning Longer Memory in Recurrent Neural Networks
- Last updated
- 11 February 2026
What the numbers mean
About this model
SCRN (Structurally Constrained Recurrent Network) was published by Facebook AI Research, in United States of America, in December 2014. It comes out of industry.
It works in Language, and is recorded as doing language modeling.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
SCRN (Structurally Constrained Recurrent Network) — common questions
How many parameters does SCRN (Structurally Constrained Recurrent Network) have?
SCRN (Structurally Constrained Recurrent Network) has 26.5M parameters. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created SCRN (Structurally Constrained Recurrent Network)?
SCRN (Structurally Constrained Recurrent Network) was published by Facebook AI Research, based in United States of America, categorised as industry.
When was SCRN (Structurally Constrained Recurrent Network) released?
SCRN (Structurally Constrained Recurrent Network) was published in December 2014. 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 SCRN (Structurally Constrained Recurrent Network) used for?
SCRN (Structurally Constrained Recurrent Network) works in Language, and is recorded as handling language modeling. 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 SCRN (Structurally Constrained Recurrent Network)?
None. SCRN (Structurally Constrained Recurrent Network) 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 SCRN (Structurally Constrained Recurrent Network) open source?
No. SCRN (Structurally Constrained Recurrent Network) has not had its weights published, so it exists only as a service controlled by its owner.
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.