PhraseCond
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),University of Pittsburgh
- Organisation type
- Academia,Academia
- Country
- United States of America
- Published
- 28 October 2017
- Authors
- Rui Liu, Wei Wei, Weiguang Mao, Maria Chikina
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Question answering
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
- 160,000 tokens
10% held out for test, so 100k * 0.9 = 90k
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
- Confident
- Citations
- 22
"We demonstrate the effectiveness of our proposed model PhaseCond on the SQuAD dataset, showing that our model significantly outperforms both state-of-the-art single-layered and multiple-layered attention models."
Sources
Where this record came from and when it was last checked.
- Reference
- Phase Conductor on Multi-layered Attentions for Machine Comprehension
- Last updated
- 28 November 2025
What the numbers mean
What this model is
PhraseCond was published by Carnegie Mellon University (CMU),University of Pittsburgh, in United States of America, in October 2017. academia,Academia is the category the publisher falls under.
It works in Language, and is recorded as doing question answering.
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 set ran to roughly 160,000 tokens.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
PhraseCond — common questions
How many parameters does PhraseCond have?
No parameter count has been published for PhraseCond, which is why no memory or speed figure appears on this page.
Who created PhraseCond?
PhraseCond was published by Carnegie Mellon University (CMU),University of Pittsburgh, based in United States of America, categorised as academia,Academia.
When was PhraseCond released?
PhraseCond was published in October 2017. 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 PhraseCond used for?
PhraseCond works in Language, and is recorded as handling question answering. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run PhraseCond?
None. PhraseCond 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 PhraseCond open source?
The licensing for PhraseCond 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.