RaSoR
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
- Korea University,Princeton University
- Organisation type
- Academia,Academia
- Country
- Korea (Republic of), United States of America
- Published
- 23 December 2020
- Authors
- Jinhyuk Lee, Mujeen Sung, Jaewoo Kang, Danqi Chen
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
- tokens
4000000 words in SQuAD * 4/3 tokens per word = 5,333,333 tokens
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
- 154
Sources
Where this record came from and when it was last checked.
- Reference
- Learning Recurrent Span Representations for Extractive Question Answering
- Last updated
- 1 January 2026
What the numbers mean
Where it came from
RaSoR was published by Korea University,Princeton University, in Korea (Republic of), in December 2020. It comes out of academia,Academia.
It works in Language, and is recorded as doing question answering.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
RaSoR — common questions
What is RaSoR used for?
RaSoR 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 RaSoR?
None. RaSoR 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 RaSoR open source?
The licensing for RaSoR 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 RaSoR have?
No parameter count has been published for RaSoR, which is why no memory or speed figure appears on this page.
Who created RaSoR?
RaSoR was published by Korea University,Princeton University, based in Korea (Republic of), categorised as academia,Academia.
When was RaSoR released?
RaSoR was published in December 2020. 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.