SC-NLM
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
- 10 November 2014
- Authors
- Ryan Kiros, R. Salakhutdinov, R. Zemel
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Multimodal, Vision, Language
- Task
- Image captioning
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
- 5,000,000 tokens
Our LSTM encoder and SC-NLM decoder were trained by concatenating the Flickr30K dataset with the recently released Microsoft COCO dataset [46], which combined give us over 100,000 images and over 500,000 descriptions for training
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Highly cited
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
SC-NLM was published by University of Toronto, in Canada, in November 2014. The organisation is categorised as academia.
It works in Multimodal, Vision, Language, and is recorded as doing image captioning.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Around 5,000,000 tokens went into training it.
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
SC-NLM — common questions
What GPU do I need to run SC-NLM?
None. SC-NLM 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 SC-NLM open source?
The licensing for SC-NLM 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 SC-NLM have?
No parameter count has been published for SC-NLM, which is why no memory or speed figure appears on this page.
Who created SC-NLM?
SC-NLM was published by University of Toronto, based in Canada, categorised as academia.
When was SC-NLM released?
SC-NLM was published in November 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 SC-NLM used for?
SC-NLM works in Multimodal, Vision, Language, and is recorded as handling image captioning. These are the areas it was designed around; they describe intent rather than a hard boundary.
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.