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 United States of America, in February 2015. The organisation is categorised as industry,Academia.
It works in Language, and is recorded as doing 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 about 319,000 tokens of text.
Answers
Constituency-Tree LSTM — common questions
Is Constituency-Tree LSTM open source?
The licensing for Constituency-Tree LSTM 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 Constituency-Tree LSTM have?
Constituency-Tree LSTM has 205.2K parameters. 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.
Who created Constituency-Tree LSTM?
Constituency-Tree LSTM was published by MetaMind Inc,Stanford University, based in United States of America, categorised as industry,Academia.
When was Constituency-Tree LSTM released?
Constituency-Tree LSTM 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.
What is Constituency-Tree LSTM used for?
Constituency-Tree LSTM works in Language, and is recorded as handling semantic embedding. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Constituency-Tree LSTM?
None. Constituency-Tree LSTM 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.