Word Representations
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 Montreal / Université de Montréal,University of Illinois Urbana-Champaign (UIUC)
- Organisation type
- Academia,Academia
- Country
- Canada, United States of America
- Published
- 1 June 2010
- Authors
- Joseph Turian, Lev-Arie Ratinov, Yoshua Bengio
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
- 37,000,000 tokens
Section 6: "After cleaning, there are 37 million words (58% of the original) in 1.3 million sentences"
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 2,510
Sources
Where this record came from and when it was last checked.
- Reference
- Word Representations: A Simple and General Method for Semi-Supervised Learning
- Last updated
- 28 November 2025
What the numbers mean
About this model
Word Representations was published by University of Montreal / Université de Montréal,University of Illinois Urbana-Champaign (UIUC), in the country recorded as Canada, during June 2010. The category the publisher falls under is academia,Academia.
It works in the domain of Language, and is recorded as performing the task of language Structure Modeling.
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 37,000,000 tokens of text.
Answers
Word Representations — common questions
Word Representations— when was it released?
It was published in June 2010. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Word Representations— 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.
Word Representations— 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.
Word Representations— 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.
Word Representations— 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.
Word Representations— who created it?
It was published by University of Montreal / Université de Montréal,University of Illinois Urbana-Champaign (UIUC), based in Canada, an organisation categorised as academia,Academia.
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.