AdvSoft + 4 layer QRNN + dynamic evaluation (WT103)
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
- University of Texas at Austin
- Organisation type
- Academia
- Country
- United States of America
- Published
- 10 June 2019
- Authors
- Dilin Wang, Chengyue Gong, Qiang Liu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling, Translation
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
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
- Training code
- Unreleased
there's a repo, but the WT-103 experiments aren't there: https://github.com/ChengyueGongR/advsoft
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 126
- Benchmark data
- AdvSoft + 4 layer QRNN + dynamic evaluation
Sources
Where this record came from and when it was last checked.
- Reference
- Improving Neural Language Modeling via Adversarial Training
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
AdvSoft + 4 layer QRNN + dynamic evaluation (WT103) was published by University of Texas at Austin, in United States of America, in June 2019. academia is the category the publisher falls under.
It works in Language, and is recorded as doing language modeling, Translation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
AdvSoft + 4 layer QRNN + dynamic evaluation (WT103) — common questions
What is AdvSoft + 4 layer QRNN + dynamic evaluation (WT103) used for?
AdvSoft + 4 layer QRNN + dynamic evaluation (WT103) works in Language, and is recorded as handling language modeling, Translation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run AdvSoft + 4 layer QRNN + dynamic evaluation (WT103)?
None. AdvSoft + 4 layer QRNN + dynamic evaluation (WT103) 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 AdvSoft + 4 layer QRNN + dynamic evaluation (WT103) open source?
No. AdvSoft + 4 layer QRNN + dynamic evaluation (WT103) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does AdvSoft + 4 layer QRNN + dynamic evaluation (WT103) have?
No parameter count has been published for AdvSoft + 4 layer QRNN + dynamic evaluation (WT103), which is why no memory or speed figure appears on this page.
Who created AdvSoft + 4 layer QRNN + dynamic evaluation (WT103)?
AdvSoft + 4 layer QRNN + dynamic evaluation (WT103) was published by University of Texas at Austin, based in United States of America, categorised as academia.
When was AdvSoft + 4 layer QRNN + dynamic evaluation (WT103) released?
AdvSoft + 4 layer QRNN + dynamic evaluation (WT103) was published in June 2019. 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.