TensorReasoner
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
- 1 December 2013
- Authors
- R Socher, D Chen, CD Manning, A Ng
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language 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
- 316,232 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
- 2,050
Sources
Where this record came from and when it was last checked.
- Reference
- Reasoning With Neural Tensor Networks for Knowledge Base Completion
- Last updated
- 1 January 2026
What the numbers mean
Where it came from
TensorReasoner was published by Stanford University, in United States of America, in December 2013. It comes out of academia.
It works in Language, and is recorded as doing language modeling.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
It was trained on about 316,232 tokens of text.
Answers
TensorReasoner — common questions
Is TensorReasoner open source?
The licensing for TensorReasoner 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 TensorReasoner have?
No parameter count has been published for TensorReasoner, which is why no memory or speed figure appears on this page.
Who created TensorReasoner?
TensorReasoner was published by Stanford University, based in United States of America, categorised as academia.
When was TensorReasoner released?
TensorReasoner was published in December 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.
What is TensorReasoner used for?
TensorReasoner works in Language, and is recorded as handling language modeling. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
What GPU do I need to run TensorReasoner?
None. TensorReasoner 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.
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.