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 United States of America, in September 2000. The organisation is categorised as academia.
It works in Language, and is recorded as doing 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 about 99,232 tokens of text.
Answers
FrameNet role labeling — common questions
Who created FrameNet role labeling?
FrameNet role labeling was published by University of Rochester, based in United States of America, categorised as academia.
When was FrameNet role labeling released?
FrameNet role labeling 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.
What is FrameNet role labeling used for?
FrameNet role labeling works in Language, and is recorded as handling language Structure Modeling. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run FrameNet role labeling?
None. FrameNet role labeling 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 FrameNet role labeling open source?
The licensing for FrameNet role labeling was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does FrameNet role labeling have?
No parameter count has been published for FrameNet role labeling, 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.