Mnemonic Reader
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
- Fudan University,Microsoft Research
- Organisation type
- Academia,Industry
- Country
- China, United States of America
- Published
- 8 May 2017
- Authors
- Minghao Hu, Yuxing Peng, Zhen Huang, Xipeng Qiu, Furu Wei, Ming Zhou
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
- 215,570 tokens
size of SQuAD
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
- 217
from the abstract " Extensive experiments on the Stanford Question Answering Dataset (SQuAD) show that our model achieves state-of-the-art results. Meanwhile, our model outperforms previous systems by over 6% in terms of both Exact Match and F1 metrics on two adversarial SQuAD datasets. "
Sources
Where this record came from and when it was last checked.
- Reference
- Reinforced Mnemonic Reader for Machine Reading Comprehension
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Mnemonic Reader was published by Fudan University,Microsoft Research, in China, in May 2017. It comes out of academia,Industry.
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 215,570 tokens.
The reason it appears in this catalogue at all is sOTA improvement.
Answers
Mnemonic Reader — common questions
What is Mnemonic Reader used for?
Mnemonic Reader 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.
What GPU do I need to run Mnemonic Reader?
None. Mnemonic Reader 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 Mnemonic Reader open source?
The licensing for Mnemonic Reader 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 Mnemonic Reader have?
No parameter count has been published for Mnemonic Reader, which is why no memory or speed figure appears on this page.
Who created Mnemonic Reader?
Mnemonic Reader was published by Fudan University,Microsoft Research, based in China, categorised as academia,Industry.
When was Mnemonic Reader released?
Mnemonic Reader was published in May 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.
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.