Cross-Lingual 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
- Carnegie Mellon University (CMU),Google Research
- Organisation type
- Academia,Industry
- Country
- United States of America
- Published
- 19 June 2011
- Authors
- Dipanjan Das, Slav Petrov
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.
- Training data
- tokens
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
- SOTA improvement
- Record confidence
- Unknown
- Citations
- 318
"Across eight European languages, our approach results in an average absolute improvement of 10.4% over a state-of-the-art baseline, and 16.7% over vanilla hidden Markov models induced with the Expectation Maximization algorithm." evaluation datasets: CoNLL-X and CoNLL-2007
Sources
Where this record came from and when it was last checked.
- Reference
- Unsupervised Part-of-Speech Tagging with Bilingual Graph-Based Projections
- Last updated
- 1 December 2025
What the numbers mean
Where it came from
Cross-Lingual POS Tagger was published by Carnegie Mellon University (CMU),Google Research, in the country recorded as United States of America, during June 2011. The publishing organisation is categorised as academia,Industry.
It works in the domain of Language, and is recorded as performing the task of part-of-speech tagging.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
Its inclusion criterion: sOTA improvement.
Answers
Cross-Lingual POS Tagger — common questions
Cross-Lingual POS Tagger— 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.
Cross-Lingual POS Tagger— 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.
Cross-Lingual POS Tagger— who created it?
It was published by Carnegie Mellon University (CMU),Google Research, based in United States of America, an organisation categorised as academia,Industry.
Cross-Lingual POS Tagger— when was it released?
It was published in June 2011. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Cross-Lingual POS Tagger— what is it used for?
It works in the domain of Language, and is recorded as handling the task of part-of-speech tagging. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Cross-Lingual POS Tagger— 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.