RNN + char4-MS-vec
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
- NTT Communication Science Laboratories,Tohoku University
- Organisation type
- Industry,Academia
- Country
- Japan
- Published
- 17 July 2019
- Authors
- Sho Takase, Jun Suzuki, Masaaki Nagata
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.
- Parameters
- 226M
- Training data
- tokens
226M
size of WT103
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA Tesla P100 PCIe 16GB
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 26
- Benchmark data
- RNN + char4-MS-vec
Sources
Where this record came from and when it was last checked.
- Reference
- Character n-Gram Embeddings to Improve RNN Language Models
- Last updated
- 28 November 2025
What the numbers mean
About this model
RNN + char4-MS-vec was published by NTT Communication Science Laboratories,Tohoku University, in the country recorded as Japan, during July 2019. The publishing organisation is categorised as industry,Academia.
It works in the domain of Language, and is recorded as performing the task of language modeling.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
RNN + char4-MS-vec — common questions
RNN + char4-MS-vec— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
RNN + char4-MS-vec— how many parameters does it have?
It has a parameter count of 226M. 226M. 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.
RNN + char4-MS-vec— who created it?
It was published by NTT Communication Science Laboratories,Tohoku University, based in Japan, an organisation categorised as industry,Academia.
RNN + char4-MS-vec— when was it released?
It was published in July 2019. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
RNN + char4-MS-vec— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
RNN + char4-MS-vec— 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.