Tagging via Viterbi Decoding
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
- AT&T
- Organisation type
- Industry
- Country
- United States of America
- Published
- 1 June 2002
- Authors
- Michael Collins
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Binary classification, Part-of-speech tagging
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.
- Training data
- 929,000 tokens
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
- Citations
- 2,582
Sources
Where this record came from and when it was last checked.
- Reference
- Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Tagging via Viterbi Decoding was published by AT&T, in the country recorded as United States of America, during June 2002. It comes out of an organisation categorised as industry.
It works in the domain of Language, and is recorded as performing the task of binary classification, Part-of-speech tagging.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
The training set ran to roughly 929,000 tokens of text.
Answers
Tagging via Viterbi Decoding — common questions
Tagging via Viterbi Decoding— 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.
Tagging via Viterbi Decoding— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
Tagging via Viterbi Decoding— who created it?
It was published by AT&T, based in United States of America, an organisation categorised as industry.
Tagging via Viterbi Decoding— when was it released?
It was published in June 2002. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Tagging via Viterbi Decoding— what is it used for?
It works in the domain of Language, and is recorded as handling the task of binary classification, Part-of-speech tagging. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Tagging via Viterbi Decoding— 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.
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.