FrameNet role labeling
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 Rochester
- Organisation type
- Academia
- Country
- United States of America
- Published
- 1 September 2000
- Authors
- Daniel Gildea, Daniel Jurafsky
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
- 99,232 tokens
Abstract: "The system is based on statistical classifiers trained on roughly 50,000 sentences"
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 2,499
Sources
Where this record came from and when it was last checked.
- Reference
- Automatic Labeling of Semantic Roles
- Last updated
- 28 November 2025
What the numbers mean
About this model
FrameNet role labeling was published by University of Rochester, in the country recorded as United States of America, during September 2000. The publishing organisation is categorised as academia.
It works in the domain of Language, and is recorded as performing the task of language Structure Modeling.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
It was trained on a corpus of about 99,232 tokens of text.
Answers
FrameNet role labeling — common questions
FrameNet role labeling— who created it?
It was published by University of Rochester, based in United States of America, an organisation categorised as academia.
FrameNet role labeling— when was it released?
It was published in September 2000. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
FrameNet role labeling— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language Structure Modeling. These are the areas it was designed around; they describe intent rather than a hard boundary.
FrameNet role labeling— 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.
FrameNet role labeling— 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.
FrameNet role labeling— 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.
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.