Deep Multitask NLP Network
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
- NEC Laboratories
- Organisation type
- Industry
- Country
- United States of America
- Published
- 5 July 2008
- Authors
- Ronan Collobert, Jason Weston
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
- Approach
- Unsupervised
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
- 1.5M
- Training data
- 633,000,000 tokens
With a word vector size of 50 and a vocabulary size of 30,000, the embedding matrix has 1,500,000 parameters. There are also some small convolutional and dense layers with far fewer parameters.
Section 7: "631 million words from Wikipedia"
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Wall-clock time
- 168 hours (7 days)
1 week on 1 computer
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,SOTA improvement
- Record confidence
- Speculative
- Citations
- 7,095
Sources
Where this record came from and when it was last checked.
- Reference
- A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Deep Multitask NLP Network was published by NEC Laboratories, in United States of America, in July 2008. The organisation is categorised as industry.
It works in Language, and is recorded as doing language modeling.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
It was trained on about 633,000,000 tokens of text.
Its inclusion criterion is highly cited,SOTA improvement.
Answers
Deep Multitask NLP Network — common questions
Is Deep Multitask NLP Network open source?
The licensing for Deep Multitask NLP Network 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 Deep Multitask NLP Network have?
Deep Multitask NLP Network has 1.5M parameters. With a word vector size of 50 and a vocabulary size of 30,000, the embedding matrix has 1,500,000 parameters. There are also some small convolutional and dense layers with far fewer parameters. 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 Deep Multitask NLP Network?
Deep Multitask NLP Network was published by NEC Laboratories, based in United States of America, categorised as industry.
When was Deep Multitask NLP Network released?
Deep Multitask NLP Network was published in July 2008. 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 Deep Multitask NLP Network used for?
Deep Multitask NLP Network works in Language, and is recorded as handling language modeling. 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.
What GPU do I need to run Deep Multitask NLP Network?
None. Deep Multitask NLP Network 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.