Order embeddings with layer norm
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
- University of Toronto
- Organisation type
- Academia
- Country
- Canada
- Published
- 21 July 2016
- Authors
- Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image captioning
- Base model
- Order-Embeddings of Images and Language
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
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Layer Normalization
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Order embeddings with layer norm was published by University of Toronto, in Canada, in July 2016. It comes out of academia.
It works in Vision, and is recorded as doing image captioning.
It is derived from Order-Embeddings of Images and Language rather than trained from scratch, which is the usual way a specialised model is produced.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
Order embeddings with layer norm — common questions
How many parameters does Order embeddings with layer norm have?
No parameter count has been published for Order embeddings with layer norm, which is why no memory or speed figure appears on this page.
Who created Order embeddings with layer norm?
Order embeddings with layer norm was published by University of Toronto, based in Canada, categorised as academia.
When was Order embeddings with layer norm released?
Order embeddings with layer norm was published in July 2016. 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 Order embeddings with layer norm used for?
Order embeddings with layer norm works in Vision, and is recorded as handling image captioning. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Order embeddings with layer norm?
None. Order embeddings with layer norm 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 Order embeddings with layer norm open source?
The licensing for Order embeddings with layer norm was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
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.