Bidirectional RNN
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
- Advanced Telecommunications Research Institute
- Organisation type
- Industry
- Country
- Japan
- Published
- 1 November 1997
- Authors
- M. Schuster, KK Paliwal
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Speech
- Task
- Speech recognition (ASR)
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
- 142,910 tokens
Page 7: "The structures of all networks are adjusted so that each of them has about the same number of free parameters (approximately 13 000 here"
"the training data set consisting of 3696 sentences from 462 speakers" Assuming avg sentence length of 20 words 3696 * 20 total words
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
- 7,990
Sources
Where this record came from and when it was last checked.
- Reference
- Bidirectional recurrent neural networks
- Last updated
- 28 November 2025
What the numbers mean
Background
Bidirectional RNN was published by Advanced Telecommunications Research Institute, in Japan, in November 1997. industry is the category the publisher falls under.
It works in Speech, and is recorded as doing speech recognition (ASR).
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
The training set ran to roughly 142,910 tokens.
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
Bidirectional RNN — common questions
Is Bidirectional RNN open source?
The licensing for Bidirectional RNN 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 Bidirectional RNN have?
Bidirectional RNN has 13K parameters. Page 7: "The structures of all networks are adjusted so that each of them has about the same number of free parameters (approximately 13 000 here". 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 Bidirectional RNN?
Bidirectional RNN was published by Advanced Telecommunications Research Institute, based in Japan, categorised as industry.
When was Bidirectional RNN released?
Bidirectional RNN was published in November 1997. 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 Bidirectional RNN used for?
Bidirectional RNN works in Speech, and is recorded as handling speech recognition (ASR). These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run Bidirectional RNN?
None. Bidirectional RNN 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.