DMN
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
- Salesforce
- Organisation type
- Industry
- Country
- United States of America
- Published
- 20 June 2016
- Authors
- Ankit Kumar, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, Richard Socher
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Question answering, Text classification, 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.
- 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
- Unknown
- Citations
- 1,216
Sources
Where this record came from and when it was last checked.
- Reference
- Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
DMN was published by Salesforce, in United States of America, in June 2016. The organisation is categorised as industry.
It works in Language, and is recorded as doing question answering, Text classification, Language modeling.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
DMN — common questions
Who created DMN?
DMN was published by Salesforce, based in United States of America, categorised as industry.
When was DMN released?
DMN was published in June 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 DMN used for?
DMN works in Language, and is recorded as handling question answering, Text classification, Language modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run DMN?
None. DMN 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 DMN open source?
The licensing for DMN 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 DMN have?
No parameter count has been published for DMN, which is why no memory or speed figure appears on this page.
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.