Markov-driven POS tagger
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
- EURECOM
- Organisation type
- Academia
- Country
- France
- Published
- 1 June 1994
- Authors
- Bernard Merialdo
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- 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.
- Parameters
- 2.4M
- Training data
- tokens
"The total number of free parameters is then: (Nw - 1).NT + (NT - 1).NT.NT." Where: Nw= Vocabulary size NT = Number of tags "In the treebank 159 different tags are used. These tags were projected on a smaller system of 76 tags designed by Evelyne Tzoukermann and Peter Brown (see Appendix). The results quoted in this paper all refer to this smaller system" So NT = 76 https://www.aclweb.org/anthology/J94-2001/ There is no direct reference to Nw, but the data is from "Lexicon and grammar in prob…
"We use the "treebank" data described in Beale (1988). It contains 42,186 sentences (about one million words) from the Associated Press." https://www.aclweb.org/anthology/J94-2001.pdf
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 788
Sources
Where this record came from and when it was last checked.
- Reference
- Tagging English Text with a Probabilistic Model
- Last updated
- 28 November 2025
What the numbers mean
Background
Markov-driven POS tagger was published by EURECOM, in France, in June 1994. The organisation is categorised as academia.
It works in Language, and is recorded as doing part-of-speech tagging.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
Markov-driven POS tagger — common questions
Who created Markov-driven POS tagger?
Markov-driven POS tagger was published by EURECOM, based in France, categorised as academia.
When was Markov-driven POS tagger released?
Markov-driven POS tagger was published in June 1994. 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 Markov-driven POS tagger used for?
Markov-driven POS tagger works in Language, and is recorded as handling part-of-speech tagging. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
What GPU do I need to run Markov-driven POS tagger?
None. Markov-driven POS tagger 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 Markov-driven POS tagger open source?
The licensing for Markov-driven POS tagger 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 Markov-driven POS tagger have?
Markov-driven POS tagger has 2.4M parameters. "The total number of free parameters is then: (Nw - 1).NT + (NT - 1).NT.NT." Where: Nw= Vocabulary size NT = Number of tags "In the treebank 159 different tags are used. These tags were projected on a smaller system of 76 tags designed by Evelyne Tzoukermann and Peter Brown (see Appendix). The results quoted in this paper all refer to this smaller system" So NT = 76 https://www.aclweb.org/anthology/J94-2001/ There is no direct reference to Nw, but the data is from "Lexicon and grammar in probabilistic tagging of written English." which says "(the new CLAWS lexicón has almost 26,500 entries)" So tentatively Nw=26500 https://dl.acm.org/doi/10.3115/982023.982049. 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.
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.