SRN-Encoded Grammatical Structures
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 California San Diego
- Organisation type
- Academia
- Country
- United States of America
- Published
- 1 September 1991
- Authors
- J. L. Elman
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language Structure 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
- 177,800 tokens
4 training sets of 10k sentences each. Total number of words calculated by multiplying 10k and the avg. number of words per sentence in the training set.
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 1,717
Sources
Where this record came from and when it was last checked.
- Reference
- Distributed representations, simple recurrent networks, and grammatical structure
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
SRN-Encoded Grammatical Structures was published by University of California San Diego, in the country recorded as United States of America, during September 1991. The category the publisher falls under is academia.
It works in the domain of Language, and is recorded as performing the task of language Structure Modeling.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
It was trained on a corpus of about 177,800 tokens of text.
Answers
SRN-Encoded Grammatical Structures — common questions
SRN-Encoded Grammatical Structures— 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.
SRN-Encoded Grammatical Structures— 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.
SRN-Encoded Grammatical Structures— 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.
SRN-Encoded Grammatical Structures— who created it?
It was published by University of California San Diego, based in United States of America, an organisation categorised as academia.
SRN-Encoded Grammatical Structures— when was it released?
It was published in September 1991. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
SRN-Encoded Grammatical Structures— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language Structure Modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
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.