RECONTRA-categorized
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
- 2 June 1999
- Authors
- M. A. Castaño, F. Casacuberta
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Translation
- Approach
- Supervised
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
- 66.8K
- Training data
- 40,000 tokens
- Epochs
- 500
6*50*140+140*140+140*37=66780 Table 1
5000*8=40000 words The number of words of the categorized sentences ranged from 3 to 13 for the Spanish ones and from 3 to 12 for the English ones.
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
- 8 × 10¹² FLOP
- How it was established
- Operation counting
2*66780*3*500*5000*8=8013600000000=8e12 "The number of words of the categorized sentences ranged from 3 to 13 for the Spanish ones and from 3 to 12 for the English ones." "was trained up to 500 epochs using the 5,000 categorized pairs"
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
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- Text-to-text machine translation using the RECONTRA connectionist model
- Last updated
- 28 November 2025
What the numbers mean
What this model is
RECONTRA-categorized was published by its authors, in June 1999.
It works in Language, and is recorded as doing translation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
Producing it required around 8 × 10¹² FLOP of arithmetic, which is a statement about the training budget rather than about inference.
Around 40,000 tokens went into training it.
Its inclusion criterion is training cost.
Answers
RECONTRA-categorized — common questions
How many parameters does RECONTRA-categorized have?
RECONTRA-categorized has 66.8K parameters. 6*50*140+140*140+140*37=66780 Table 1. 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 RECONTRA-categorized released?
RECONTRA-categorized was published in June 1999. 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 RECONTRA-categorized used for?
RECONTRA-categorized works in Language, and is recorded as handling translation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train RECONTRA-categorized?
Around 8 × 10¹² 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 RECONTRA-categorized?
None. RECONTRA-categorized 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 RECONTRA-categorized open source?
The licensing for RECONTRA-categorized was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
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.