Reading Twice for NLU
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
- DeepMind
- Organisation type
- Industry
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 8 June 2017
- Authors
- Dirk Weissenborn, Tomáš Kočiský, Chris Dyer
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
- 200,000 tokens
both datasets have around 100k training examples. SQuAD have around 4M words. TriviaQA is larger "We use 2 recent DQAbenchmark training and evaluation datasets, SQuAD (Rajpurkar et al., 2016) and TriviaQA (Joshi et al., 2017). "
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
- Unknown
- Citations
- 62
"Our results are competitive with the best systems, achieving a new state of the art on the recent TriviaQA benchmarks."
Sources
Where this record came from and when it was last checked.
- Reference
- Dynamic Integration of Background Knowledge in Neural NLU Systems
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Reading Twice for NLU was published by DeepMind, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during June 2017. It comes out of an organisation categorised as industry.
It works in the domain of Language, and is recorded as performing the task of question answering.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Training consumed a corpus of around 200,000 tokens of text.
Its inclusion criterion: sOTA improvement.
Answers
Reading Twice for NLU — common questions
Reading Twice for NLU— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
Reading Twice for NLU— who created it?
It was published by DeepMind, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as industry.
Reading Twice for NLU— when was it released?
It was published in June 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.
Reading Twice for NLU— what is it used for?
It works in the domain of Language, and is recorded as handling the task of question answering. 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.
Reading Twice for NLU— what GPU do I need to run it?
None. This 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.
Reading Twice for NLU— is it open source?
The licensing 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.