SDE
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
- Carnegie Mellon University (CMU),Google Brain,Monash University
- Organisation type
- Academia,Industry,Academia
- Country
- United States of America, Australia
- Published
- 9 February 2019
- Authors
- Xinyi Wang, Hieu Pham, Philip Arthur, Graham Neubig
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Translation
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
"The batch size is set to be 1500 words. We evaluate by development set BLEU score for every 2500 training batches"
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Cloud vendor
- Amazon Web Services
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
- Open (non-commercial)
https://github.com/cindyxinyiwang/SDE
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
- Record confidence
- Unknown
"Experiments on a standard dataset of four low-resource languages show consistent improvements over strong multilingual NMT baselines, with gains of up to 2 BLEU on one of the tested languages, achieving the new state-of-the-art on all four language pairs"
Sources
Where this record came from and when it was last checked.
- Reference
- Multilingual Neural Machine Translation With Soft Decoupled Encoding
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
SDE was published by Carnegie Mellon University (CMU),Google Brain,Monash University, in United States of America, in February 2019. The organisation is categorised as academia,Industry,Academia.
It works in Language, and is recorded as doing translation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
Its inclusion criterion is sOTA improvement.
Answers
SDE — common questions
How many parameters does SDE have?
No parameter count has been published for SDE, which is why no memory or speed figure appears on this page.
Who created SDE?
SDE was published by Carnegie Mellon University (CMU),Google Brain,Monash University, based in United States of America, categorised as academia,Industry,Academia.
When was SDE released?
SDE was published in February 2019. 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 SDE used for?
SDE works in Language, and is recorded as handling translation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run SDE?
None. SDE 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 SDE open source?
No. SDE has not had its weights published, so it exists only as a service controlled by its owner.
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.