S-Norm
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
- University of Washington,Allen Institute for AI
- Organisation type
- Academia,Research collective
- Country
- United States of America
- Published
- 29 October 2017
- Authors
- Christopher Clark, Matt Gardner
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
- 1,060,000 tokens
- Epochs
- 1
"530k question-document training pairs" average question length of 14 words and document length of 2895 words, per https://www.cs.utexas.edu/~eunsol/files/papers/acl17jcwz.pdf 530,000 * 2895 words on average * 1.33 tokens/word = ~2,000,000,000
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
- Confident
- Citations
- 483
"Overall, we are able to achieve a score of 71.3 F1 on the web portion of TriviaQA, a large improvement from the 56.7 F1 of the previous best system."
Sources
Where this record came from and when it was last checked.
- Reference
- Simple and Effective Multi-Paragraph Reading Comprehension
- Last updated
- 25 May 2026
What the numbers mean
About this model
S-Norm was published by University of Washington,Allen Institute for AI, in the country recorded as United States of America, during October 2017. The publishing organisation is categorised as academia,Research collective.
It works in the domain of Language, and is recorded as performing the task of question answering.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
Training consumed a corpus of around 1,060,000 tokens of text.
Its inclusion criterion: sOTA improvement.
Answers
S-Norm — common questions
S-Norm— when was it released?
It was published in October 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.
S-Norm— what is it used for?
It works in the domain of Language, and is recorded as handling the task of question answering. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
S-Norm— 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.
S-Norm— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
S-Norm— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
S-Norm— who created it?
It was published by University of Washington,Allen Institute for AI, based in United States of America, an organisation categorised as academia,Research collective.
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.