Neural LM
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
- University of Montreal / Université de Montréal
- Organisation type
- Academia
- Country
- Canada
- Published
- 28 November 2000
- Authors
- Yoshua Bengio, Réjean Ducharme, Pascal Vincent, Christian Janvin
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
- 6.9M
- Training data
- 32,000,000 tokens
- Epochs
- 10
(30959*100) + (8*100*120) + (120*30959) = 6,906,980 "This is obtained with a network with the direct architecture, 100 randomly initialized words features, 120 hidden units, and n = 8 words of context." "The Hansard corpus (Canadian parliament proceedings, French version) is a stream of about 34 million words, of which 32 millions (set A) was used for training, 1.1 million (set B) was used for validation, and 1.2 million (set C) was used for out-of-sample tests. The original data has 106, 936 di…
The Hansard corpus (Canadian parliament proceedings, French version) is a stream of about 34 million words, of which 32 millions (set A) was used for training, 1.1 million (set B) was used for validation, and 1.2 million (set C) was used for out-of-sample tests.
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 6.3 × 10¹⁵ FLOP
- How it was established
- Operation counting
The authors use a trick to avoid having to calculate the final layer for all possible words in the vocabulary. They precompute a "short list" of the most common word following any 2 precursor words with a smoothed trigram model, and then only calculate the softmax over words on the short list. This means only a negligible fraction of the unembedding parameters get used, so the effective number of parameters appears to be (30959*100) + (8*100*120) = 3,191,900 "Apparent convergence of the stochas…
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Frontier model
- Yes
- Why it is tracked
- Training cost,Historical significance,Highly cited
- Record confidence
- Confident
- Citations
- 7,637
Sources
Where this record came from and when it was last checked.
- Reference
- A Neural Probabilistic Language Model
- Last updated
- 1 January 2026
What the numbers mean
About this model
Neural LM was published by University of Montreal / Université de Montréal, in the country recorded as Canada, during November 2000. It comes out of an organisation categorised as 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.
What went into building it
Training it took a computation budget of roughly 6.3 × 10¹⁵ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
It was trained on a corpus of about 32,000,000 tokens of text.
Its inclusion criterion: training cost,Historical significance,Highly cited.
Answers
Neural LM — common questions
Neural LM— 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.
Neural LM— 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.
Neural LM— how many parameters does it have?
It has a parameter count of 6.9M. (30959*100) + (8*100*120) + (120*30959) = 6,906,980 "This is obtained with a network with the direct architecture, 100 randomly initialized words features, 120 hidden units, and n = 8 words of context." "The Hansard corpus (Canadian parliament proceedings, French version) is a stream of about 34 million words, of which 32 millions (set A) was used for training, 1.1 million (set B) was used for validation, and 1.2 million (set C) was used for out-of-sample tests. The original data has 106, 936 different words, and those with frequency <= 10 were merged into a single token, yielding IVI = 30,959 different words.". 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.
Neural LM— who created it?
It was published by University of Montreal / Université de Montréal, based in Canada, an organisation categorised as academia.
Neural LM— when was it released?
It was published in November 2000. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Neural LM— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling. These are the areas it was designed around; they describe intent rather than a hard boundary.
Neural LM— how much compute was used to train it?
Training consumed around 6.3 × 10¹⁵ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
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.