Semantic Hashing
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 December 2008
- Authors
- R Salakhutdinov, G Hinton
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Search
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
- 2.6M
- Training data
- 1,511,035,000 tokens
Section 4.1
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 1,487
Sources
Where this record came from and when it was last checked.
- Reference
- Semantic Hashing
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Semantic Hashing was published by University of Toronto, in the country recorded as Canada, during December 2008. It comes out of an organisation categorised as academia.
It works in the domain of Language, and is recorded as performing the task of search.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
The training set ran to roughly 1,511,035,000 tokens of text.
Answers
Semantic Hashing — common questions
Semantic Hashing— how many parameters does it have?
It has a parameter count of 2.6M. 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.
Semantic Hashing— who created it?
It was published by University of Toronto, based in Canada, an organisation categorised as academia.
Semantic Hashing— when was it released?
It was published in December 2008. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Semantic Hashing— what is it used for?
It works in the domain of Language, and is recorded as handling the task of search. These are the areas it was designed around; they describe intent rather than a hard boundary.
Semantic Hashing— 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.
Semantic Hashing— is it open source?
The licensing 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.