Word2Vec (small)
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
- Organisation type
- Industry
- Country
- United States of America
- Published
- 16 October 2013
- Authors
- T Mikolov, I Sutskever, K Chen, GS Corrado
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Semantic embedding
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
- 207.6M
- Training data
- 10,000,000,000 tokens
"We discarded from the vocabulary all words that occurred less than 5 times in the training data, which resulted in a vocabulary of size 692K [...] Starting with the same news data as in the previous experiments, we first constructed the phrase based training corpus and then we trained several Skip-gram models using different hyperparameters. As before, we used vector dimensionality 300 and context size 5."
"For training the Skip-gram models, we have used a large dataset consisting of various news articles (an internal Google dataset with one billion words). We discarded from the vocabulary all words that occurred less than 5 times in the training data, which resulted in a vocabulary of size 692K"
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
- Citations
- 35,201
Sources
Where this record came from and when it was last checked.
- Reference
- Distributed Representations of Words and Phrases and their Compositionality
- Last updated
- 25 May 2026
What the numbers mean
Background
Word2Vec (small) was published by Google, in the country recorded as United States of America, during October 2013. The publishing organisation is categorised as industry.
It works in the domain of Language, and is recorded as performing the task of semantic embedding.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
It was trained on a corpus of about 10,000,000,000 tokens of text.
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
Word2Vec (small) — common questions
Word2Vec (small)— when was it released?
It was published in October 2013. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Word2Vec (small)— what is it used for?
It works in the domain of Language, and is recorded as handling the task of semantic embedding. These are the areas it was designed around; they describe intent rather than a hard boundary.
Word2Vec (small)— 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.
Word2Vec (small)— 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.
Word2Vec (small)— how many parameters does it have?
It has a parameter count of 207.6M. "We discarded from the vocabulary all words that occurred less than 5 times in the training data, which resulted in a vocabulary of size 692K [...] Starting with the same news data as in the previous experiments, we first constructed the phrase based training corpus and then we trained several Skip-gram models using different hyperparameters. As before, we used vector dimensionality 300 and context size 5.". 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.
Word2Vec (small)— who created it?
It was published by Google, based in United States of America, an organisation categorised as industry.
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.