DeBERTaV3large + KEAR
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
- Microsoft
- Organisation type
- Industry
- Country
- United States of America
- Published
- 4 May 2022
- Authors
- Yichong Xu, Chenguang Zhu, Shuohang Wang, Siqi Sun, Hao Cheng, Xiaodong Liu, Jianfeng Gao, Pengcheng He, Michael Zeng, Xuedong Huang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Question answering, Language modeling/generation
- Base model
- DeBERTaV3large
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
- 418M
- Training data
- tokens
- Epochs
- 10
DeBERTaV3-large had 418M params, per Table 2
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Open source
no attributed license https://github.com/microsoft/KEAR?tab=readme-ov-file
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
- 63
"The proposed system, Knowledgeable External Attention for commonsense Reasoning (KEAR), reaches human parity on the open CommonsenseQA research benchmark with an accuracy of 89.4\% in comparison to the human accuracy of 88.9\%." SOTA per https://paperswithcode.com/sota/common-sense-reasoning-on-commonsenseqa
Sources
Where this record came from and when it was last checked.
- Reference
- Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention
- Last updated
- 25 May 2026
What the numbers mean
Background
DeBERTaV3large + KEAR was published by Microsoft, in United States of America, in May 2022. It comes out of industry.
It works in Language, and is recorded as doing question answering, Language modeling/generation.
It builds on DeBERTaV3large, which is why it shares that model's general shape and size.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
The reason it appears in this catalogue at all is sOTA improvement.
Answers
DeBERTaV3large + KEAR — common questions
What is DeBERTaV3large + KEAR used for?
DeBERTaV3large + KEAR works in Language, and is recorded as handling question answering, Language modeling/generation. 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 DeBERTaV3large + KEAR?
None. DeBERTaV3large + KEAR 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 DeBERTaV3large + KEAR open source?
No. DeBERTaV3large + KEAR has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does DeBERTaV3large + KEAR have?
DeBERTaV3large + KEAR has 418M parameters. DeBERTaV3-large had 418M params, per Table 2. 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.
Who created DeBERTaV3large + KEAR?
DeBERTaV3large + KEAR was published by Microsoft, based in United States of America, categorised as industry.
When was DeBERTaV3large + KEAR released?
DeBERTaV3large + KEAR was published in May 2022. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
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.