Vector Space Model
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
- Stanford University
- Organisation type
- Academia
- Country
- United States of America
- Published
- 19 June 2011
- Authors
- Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, A. Ng, Christopher Potts
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Semantic embedding, Sentiment 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.
- Parameters
- 255K
- Training data
- 5,650,000 tokens
"We build a fixed dictionary of the 5,000 most frequent tokens" "For all word vector models, we use 50-dimensional vectors" Parameters: 5000*50 + 5000=255000
"We train a variant of our model which uses 50,000 unlabeled reviews in addition to the labeled set of 25,000 reviews"
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Highly cited,SOTA improvement
- Record confidence
- Confident
"We evaluate the model using small, widely used sentiment and subjectivity corpora and find it out-performs several previously introduced methods for sentiment classification" " Table 2 shows classification performance on our subset of IMDB reviews. Our model showed superior performance to other approaches, and performed best when concatenated with bag of words representation. "
Sources
Where this record came from and when it was last checked.
- Reference
- Learning Word Vectors for Sentiment Analysis
- Last updated
- 28 November 2025
What the numbers mean
About this model
Vector Space Model was published by Stanford University, in the country recorded as United States of America, during June 2011. It comes out of an organisation categorised as academia.
It works in the domain of Language, and is recorded as performing the task of semantic embedding, Sentiment classification.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
The training set ran to roughly 5,650,000 tokens of text.
Its inclusion criterion: highly cited,SOTA improvement.
Answers
Vector Space Model — common questions
Vector Space Model— when was it released?
It was published in June 2011. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Vector Space Model— what is it used for?
It works in the domain of Language, and is recorded as handling the task of semantic embedding, Sentiment classification. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Vector Space Model— 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.
Vector Space Model— 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.
Vector Space Model— how many parameters does it have?
It has a parameter count of 255K. "We build a fixed dictionary of the 5,000 most frequent tokens" "For all word vector models, we use 50-dimensional vectors" Parameters: 5000*50 + 5000=255000. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Vector Space Model— who created it?
It was published by Stanford University, based in United States of America, an organisation categorised as 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.