The Attentive Reader
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
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
- Google DeepMind
- Organisation type
- Industry
- Country
- United States of America
- Published
- 19 November 2015
- Authors
- Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, Phil Blunsom
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
Table 1 shows statistics of documents and queries used to construct examples. Training examples are document-query-answer pairs Estimating query-answer pairs at ~30 tokens Total tokens: CNN: (762+30)*380000=300960000 Daily Mail: (812+30)*880000=740960000 Total: 1041920000
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
- Open — downloadable
- Model access
- Open weights (unrestricted)
- Training code
- Open source
https://github.com/cooijmanstim/Attentive_reader/tree/bn BSD 3-Clause "New" or "Revised" License
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Teaching Machines to Read and Comprehend
- Last updated
- 28 November 2025
What the numbers mean
What this model is
The Attentive Reader was published by Google DeepMind, in the country recorded as United States of America, during November 2015. The category the publisher falls under is industry.
It works in the domain of Language, and is recorded as performing the task of question answering.
The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold.
Answers
The Attentive Reader — common questions
The Attentive Reader— what is it used for?
It works in the domain of Language, and is recorded as handling the task of question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.
The Attentive Reader— where can I download it?
The weights are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
The Attentive Reader— what GPU do I need to run it?
We cannot say. It has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
The Attentive Reader— is it open source?
Its weights are published, so it can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
The Attentive Reader— 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.
The Attentive Reader— who created it?
It was published by Google DeepMind, based in United States of America, an organisation categorised as industry.
The Attentive Reader— when was it released?
It was published in November 2015. 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.