DBN for NLP
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
- Microsoft,University of Toronto
- Organisation type
- Industry,Academia
- Country
- United States of America, Canada
- Published
- 11 February 2014
- Authors
- R Sarikaya, GE Hinton, A Deoras
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Text classification
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
- 1M
- Training data
- 27,000 tokens
Assuming 1000 input features, 35 classes and 3 hidden layers of 500 units each
The training data has 27K automatically transcribed utterances amounting to 178K words.
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 445
Sources
Where this record came from and when it was last checked.
- Reference
- Application of Deep Belief Networks for Natural Language Understanding
- Last updated
- 28 November 2025
What the numbers mean
What this model is
DBN for NLP was published by Microsoft,University of Toronto, in United States of America, in February 2014. The organisation is categorised as industry,Academia.
It works in Language, and is recorded as doing text classification.
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
It was trained on about 27,000 tokens of text.
Answers
DBN for NLP — common questions
How many parameters does DBN for NLP have?
DBN for NLP has 1M parameters. Assuming 1000 input features, 35 classes and 3 hidden layers of 500 units each. 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 DBN for NLP?
DBN for NLP was published by Microsoft,University of Toronto, based in United States of America, categorised as industry,Academia.
When was DBN for NLP released?
DBN for NLP was published in February 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 DBN for NLP used for?
DBN for NLP works in Language, and is recorded as handling text classification. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run DBN for NLP?
None. DBN for NLP 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 DBN for NLP open source?
The licensing for DBN for NLP 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.