RNN+LDA+KN5+cache
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,Brno University of Technology
- Organisation type
- Industry,Academia
- Country
- United States of America, Czechia
- Published
- 1 December 2012
- Authors
- Tomas Mikolov, Geoffrey Zweig
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
- 9M
- Training data
- 929,000 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
- Unreleased
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
- SOTA improvement
- Citations
- 716
- Benchmark data
- RNN+LDA+KN5+cache
"We report perplexity results on the Penn Treebank data, where we achieve a new state-of-the-art"
Sources
Where this record came from and when it was last checked.
- Reference
- Context dependent recurrent neural network language model
- Last updated
- 28 November 2025
What the numbers mean
About this model
RNN+LDA+KN5+cache was published by Microsoft,Brno University of Technology, in the country recorded as United States of America, during December 2012. The category the publisher falls under is industry,Academia.
It works in the domain of Language, and is recorded as performing the task of language modeling.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Training consumed a corpus of around 929,000 tokens of text.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
RNN+LDA+KN5+cache — common questions
RNN+LDA+KN5+cache— what is it used for?
It works in the domain of Language, and is recorded as handling the task of 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.
RNN+LDA+KN5+cache— 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.
RNN+LDA+KN5+cache— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
RNN+LDA+KN5+cache— how many parameters does it have?
It has a parameter count of 9M. 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.
RNN+LDA+KN5+cache— who created it?
It was published by Microsoft,Brno University of Technology, based in United States of America, an organisation categorised as industry,Academia.
RNN+LDA+KN5+cache— when was it released?
It was published in December 2012. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
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.