Semi-Supervised Embedding for DL
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
- Google,NUANCE Communications,IDIAP,University of Illinois Urbana-Champaign (UIUC)
- Organisation type
- Industry,Industry,Academia,Academia
- Country
- United States of America, Switzerland
- Published
- 5 July 2008
- Authors
- Jason Weston, Frederick, Ratle, Hossein Mobahi, Ronan Collobert
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Other
- Task
- Image classification, Language Structure Modeling, Text classification
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
- 632,000,000 tokens
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
- Citations
- 1,087
Sources
Where this record came from and when it was last checked.
- Reference
- Deep Learning via Semi-Supervised Embedding
- Last updated
- 28 November 2025
What the numbers mean
About this model
Semi-Supervised Embedding for DL was published by Google,NUANCE Communications,IDIAP,University of Illinois Urbana-Champaign (UIUC), in the country recorded as United States of America, during July 2008. The category the publisher falls under is industry,Industry,Academia,Academia.
It works in the domain of Other, and is recorded as performing the task of image classification, Language Structure Modeling, Text classification.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
Training consumed a corpus of around 632,000,000 tokens of text.
Answers
Semi-Supervised Embedding for DL — common questions
Semi-Supervised Embedding for DL— who created it?
It was published by Google,NUANCE Communications,IDIAP,University of Illinois Urbana-Champaign (UIUC), based in United States of America, an organisation categorised as industry,Industry,Academia,Academia.
Semi-Supervised Embedding for DL— when was it released?
It was published in July 2008. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Semi-Supervised Embedding for DL— what is it used for?
It works in the domain of Other, and is recorded as handling the task of image classification, Language Structure Modeling, Text classification. These are the areas it was designed around; they describe intent rather than a hard boundary.
Semi-Supervised Embedding for DL— 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.
Semi-Supervised Embedding for DL— 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.
Semi-Supervised Embedding for DL— 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.