SEST
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)
- Organisation type
- Academia
- Country
- United States of America
- Published
- 2 March 2017
- Authors
- Rui Liu, Junjie Hu, Wei Wei, Zi Yang, Eric Nyberg
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Question answering
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
SQuAD 1.1 contains 107,785 question-answer pairs on 536 articles. The paper isn't super clear on how they train the model, but it looks as though there is a backward pass for each answer (not per token).
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA GeForce GTX 1080
- Chips used
- 1
- Power draw
- 207 W
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 53
Sources
Where this record came from and when it was last checked.
- Reference
- Structural Embedding of Syntactic Trees for Machine Comprehension
- Last updated
- 28 November 2025
What the numbers mean
About this model
SEST was published by Carnegie Mellon University (CMU), in United States of America, in March 2017. The organisation is categorised as academia.
It works in Language, and is recorded as doing question answering.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
SEST — common questions
How many parameters does SEST have?
No parameter count has been published for SEST, which is why no memory or speed figure appears on this page.
Who created SEST?
SEST was published by Carnegie Mellon University (CMU), based in United States of America, categorised as academia.
When was SEST released?
SEST was published in March 2017. 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 SEST used for?
SEST works in Language, and is recorded as handling question answering. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run SEST?
None. SEST 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 SEST open source?
The licensing for SEST 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.