NLP from scratch
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,Princeton University
- Organisation type
- Industry,Academia
- Country
- United States of America
- Published
- 8 November 2011
- Authors
- Ronan Collobert, J. Weston, L. Bottou, Michael Karlen, K. Kavukcuoglu, P. Kuksa
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language Structure 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
- 5M
- Training data
- 852,000,000 tokens
"The capacity of our network architectures lies mainly in the word lookup table, which contains 50 × 100,000 parameters to train. [...] most of the trainable parameters are located in the lookup tables."
"Section 4 leverages large unlabeled data sets (∼ 852 million words)"
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
- 72 hours
"Chunking and NER take about one hour to train, POS takes few hours, and SRL takes about three days." SRL is the longest task.
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
- 7,640
Sources
Where this record came from and when it was last checked.
- Reference
- Natural Language Processing (Almost) from Scratch
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
NLP from scratch was published by NEC Laboratories,Princeton University, in the country recorded as United States of America, during November 2011. It comes out of an organisation categorised as industry,Academia.
It works in the domain of Language, and is recorded as performing the task of language Structure Modeling.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Training consumed a corpus of around 852,000,000 tokens of text.
Its inclusion criterion: highly cited.
Answers
NLP from scratch — common questions
NLP from scratch— 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.
NLP from scratch— 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.
NLP from scratch— how many parameters does it have?
It has a parameter count of 5M. "The capacity of our network architectures lies mainly in the word lookup table, which contains 50 × 100,000 parameters to train. [...] most of the trainable parameters are located in the lookup tables.". 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.
NLP from scratch— who created it?
It was published by NEC Laboratories,Princeton University, based in United States of America, an organisation categorised as industry,Academia.
NLP from scratch— when was it released?
It was published in November 2011. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
NLP from scratch— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language Structure Modeling. These are the areas it was designed around; they describe intent rather than a hard boundary.
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.