AdaRNN
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
- Beihang University
- Organisation type
- Academia
- Country
- China
- Published
- 1 June 2014
- Authors
- Li Dong, Furu Wei, Chuanqi Tan, Duyu Tang, M. Zhou, Ke Xu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- 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
- 13K
- Training data
- 6,248 tokens
D=25 "For recursive neural models, the dimension of word vector is set to 25, and f = tanh is used as the nonlinearity function. We employ 10 composition matrices in AdaRNN." Composition matrices: "W ∈ R D×2D is the composition matrix, and b is the bias vector." C=10 "We employ 10 composition matrices in AdaRNN." Combination matrix: "S ∈ R C×(2D+|e|) is the matrix used to determine which composition function we use, vl , vr are the left and right child vectors, and e are external feature vector.…
"Training data consists of 6,248 tweets,"
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
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification
- Last updated
- 28 November 2025
What the numbers mean
About this model
AdaRNN was published by Beihang University, in the country recorded as China, during June 2014. The publishing organisation is categorised as academia.
It works in the domain of Language, and is recorded as performing the task of sentiment classification.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
Training consumed a corpus of around 6,248 tokens of text.
Its inclusion criterion: highly cited.
Answers
AdaRNN — common questions
AdaRNN— when was it released?
It was published in June 2014. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
AdaRNN— what is it used for?
It works in the domain of Language, and is recorded as handling the task of sentiment classification. 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.
AdaRNN— 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.
AdaRNN— 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.
AdaRNN— how many parameters does it have?
It has a parameter count of 13K. D=25 "For recursive neural models, the dimension of word vector is set to 25, and f = tanh is used as the nonlinearity function. We employ 10 composition matrices in AdaRNN." Composition matrices: "W ∈ R D×2D is the composition matrix, and b is the bias vector." C=10 "We employ 10 composition matrices in AdaRNN." Combination matrix: "S ∈ R C×(2D+|e|) is the matrix used to determine which composition function we use, vl , vr are the left and right child vectors, and e are external feature vector. In this work, e is a one-hot binary feature vector which indicates what the dependency type is." |e| > 4: (see Figure 2) Weights: 10 * 25 * 50 + 10 * (50+4) =13040 (ignoring embedding to the 25 dimension embedding space). 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.
AdaRNN— who created it?
It was published by Beihang University, based in China, an organisation categorised as academia.
The other direction
Looking at it from the other side?
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