Bankruptcy-NN
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.
- Published
- 17 June 1990
- Authors
- M. Odom, R. Sharda
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Other
- Task
- Binary classification
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
- 0K
- Training data
- 74 tokens
- Epochs
- 191,400
"The input layer consisted of the five nodes, one for each of the ratios. The hidden layer consisted of 5 node.;. The output layer consisted of only one neuron" 30 weights + 6 biases = 36
"The first (training) subsample of 74 firms data" 5 inputs per firm 74*5 = 370
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
- 3.1 × 10⁹ FLOP
- How it was established
- Operation counting
2*36*3*74*191400=3,059,337,600=3.06e9 "Convergence was reached after 191,400 iterations"
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
- 24 hours
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
- Historical significance,Highly cited
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- A neural network model for bankruptcy prediction
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Bankruptcy-NN was published by its authors, in June 1990.
It works in Other, and is recorded as doing binary classification.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
Training it took roughly 3.1 × 10⁹ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.
Around 74 tokens went into training it.
Its inclusion criterion is historical significance,Highly cited.
Answers
Bankruptcy-NN — common questions
What is Bankruptcy-NN used for?
Bankruptcy-NN works in Other, and is recorded as handling binary classification. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train Bankruptcy-NN?
Around 3.1 × 10⁹ 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.
What GPU do I need to run Bankruptcy-NN?
None. Bankruptcy-NN 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.
Is Bankruptcy-NN open source?
The licensing for Bankruptcy-NN 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 Bankruptcy-NN have?
Bankruptcy-NN has 0K parameters. "The input layer consisted of the five nodes, one for each of the ratios. The hidden layer consisted of 5 node.;. The output layer consisted of only one neuron" 30 weights + 6 biases = 36. 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.
When was Bankruptcy-NN released?
Bankruptcy-NN was published in June 1990. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
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.