MemoReader
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
- Samsung,Korea University
- Organisation type
- Industry,Academia
- Country
- Korea (Republic of)
- Published
- 31 October 2018
- Authors
- Seohyun Back, Seunghak Yu, Sathish Indurthi, Jihie Kim, Jaegul Choo
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
- 1,057,958 tokens
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 M40
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
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
- 17
"TriviaQA. As shown in Table 2, our model, even without DEBS, outperforms the existing state-of-the-art method such as ‘BiDAF + SA + SN’ by a large margin in all the cases"
Sources
Where this record came from and when it was last checked.
- Reference
- MemoReader: Large-Scale Reading Comprehension through Neural Memory Controller
- Last updated
- 28 November 2025
What the numbers mean
Background
MemoReader was published by Samsung,Korea University, in Korea (Republic of), in October 2018. It comes out of industry,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.
What went into building it
The training set ran to roughly 1,057,958 tokens.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
MemoReader — common questions
What GPU do I need to run MemoReader?
None. MemoReader 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 MemoReader open source?
No. MemoReader has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does MemoReader have?
No parameter count has been published for MemoReader, which is why no memory or speed figure appears on this page.
Who created MemoReader?
MemoReader was published by Samsung,Korea University, based in Korea (Republic of), categorised as industry,Academia.
When was MemoReader released?
MemoReader was published in October 2018. 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 MemoReader used for?
MemoReader works in Language, and is recorded as handling question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.
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.