Constituency-Tree LSTM
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
- MetaMind Inc,Stanford University
- Organisation type
- Industry,Academia
- Country
- United States of America
- Published
- 28 February 2015
- Authors
- KS Tai, R Socher, CD Manning
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
- 205.2K
- Training data
- 319,000 tokens
Table 1 https://arxiv.org/abs/1503.00075
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 3,248
Sources
Where this record came from and when it was last checked.
- Reference
- Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
Constituency-Tree LSTM was published by MetaMind Inc,Stanford University, in the country recorded as United States of America, during February 2015. The publishing organisation is categorised as industry,Academia.
It works in the domain of Language, and is recorded as performing the task of semantic embedding.
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 a corpus of about 319,000 tokens of text.
Answers
Constituency-Tree LSTM — common questions
Constituency-Tree LSTM— 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.
Constituency-Tree LSTM— how many parameters does it have?
It has a parameter count of 205.2K. Table 1 https://arxiv.org/abs/1503.00075. 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.
Constituency-Tree LSTM— who created it?
It was published by MetaMind Inc,Stanford University, based in United States of America, an organisation categorised as industry,Academia.
Constituency-Tree LSTM— when was it released?
It was published in February 2015. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Constituency-Tree LSTM— what is it used for?
It works in the domain of Language, and is recorded as handling the task of semantic embedding. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Constituency-Tree LSTM— 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.
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.