Part-of-sentence tagging model
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)
- Organisation type
- Academia
- Country
- United States of America
- Published
- 29 May 2016
- Authors
- Xuehe Ma, Eduard Hovy
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
- Numerical format
- FP32
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
- 912,344 tokens
- Epochs
- 50
Table 2
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
- 1.5 × 10¹⁷ FLOP
- How it was established
- Hardware
12 hours of training for POS tagging GeForce GTX TITAN X GPU 0.33 utilization rate
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA GeForce GTX TITAN X
- Chips used
- 1
- Chip-hours
- 12
- Wall-clock time
- 12 hours
- Power draw
- 290 W
"the model training requires about 12 hours for POS tagging and 8 hours for NER"
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 3,193
Sources
Where this record came from and when it was last checked.
- Reference
- End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Part-of-sentence tagging model was published by Carnegie Mellon University (CMU), in United States of America, in May 2016. It comes out of academia.
It works in Language, and is recorded as doing part-of-speech tagging.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
The training run consumed about 1.5 × 10¹⁷ FLOP, on NVIDIA GeForce GTX TITAN X. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
The training set ran to roughly 912,344 tokens.
Answers
Part-of-sentence tagging model — common questions
Is Part-of-sentence tagging model open source?
The licensing for Part-of-sentence tagging model 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 Part-of-sentence tagging model have?
No parameter count has been published for Part-of-sentence tagging model, which is why no memory or speed figure appears on this page.
Who created Part-of-sentence tagging model?
Part-of-sentence tagging model was published by Carnegie Mellon University (CMU), based in United States of America, categorised as academia.
When was Part-of-sentence tagging model released?
Part-of-sentence tagging model was published in May 2016. 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 Part-of-sentence tagging model used for?
Part-of-sentence tagging model works in Language, and is recorded as handling part-of-speech tagging. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train Part-of-sentence tagging model?
Around 1.5 × 10¹⁷ FLOP, on NVIDIA GeForce GTX TITAN X. 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 Part-of-sentence tagging model?
None. Part-of-sentence tagging model 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.