DCN+
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
- Salesforce Research
- Organisation type
- Industry
- Country
- United States of America
- Published
- 31 October 2017
- Authors
- Caiming Xiong, Victor Zhong, Richard Socher
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
from https://paperswithcode.com/dataset/squad SQuAD have 107,785 question-answer pairs download-ed dataset from: https://www.kaggle.com/datasets/stanfordu/stanford-question-answering-dataset?resource=download wc -w on train-v.1.1 returns 4017471 words so around 5.4M tokens Looks like they probably trained on each token in SQuAD rather than QA pairs, but uncertain.
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
- 121
"On the Stanford Question Answering Dataset, our model achieves state-of-the-art results with 75.1% exact match accuracy and 83.1% F1, while the ensemble obtains 78.9% exact match accuracy and 86.0% F1. " https://paperswithcode.com/paper/dcn-mixed-objective-and-deep-residual
Sources
Where this record came from and when it was last checked.
- Reference
- DCN+: Mixed Objective and Deep Residual Coattention for Question Answering
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
DCN+ was published by Salesforce Research, in United States of America, in October 2017. The organisation is categorised as industry.
It works in Language, and is recorded as doing question answering.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
The training set ran to roughly 215,570 tokens.
Its inclusion criterion is sOTA improvement.
Answers
DCN+ — common questions
Is DCN+ open source?
No. DCN+ has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does DCN+ have?
No parameter count has been published for DCN+, which is why no memory or speed figure appears on this page.
Who created DCN+?
DCN+ was published by Salesforce Research, based in United States of America, categorised as industry.
When was DCN+ released?
DCN+ was published in October 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.
What is DCN+ used for?
DCN+ works in Language, and is recorded as handling 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.
What GPU do I need to run DCN+?
None. DCN+ 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.
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.