Adversarial + AWD-LSTM-MoS + partial shuffled
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
- Translation, Language modeling
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
- Epochs
- 450
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
- Open (non-commercial)
code, no clear license: 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
- Adversarial + AWD-LSTM-MoS + partial shuffled
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
About this model
Adversarial + AWD-LSTM-MoS + partial shuffled was published by University of Texas at Austin, in United States of America, in June 2019. The organisation is categorised as academia.
It works in Language, and is recorded as doing translation, Language modeling.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
Adversarial + AWD-LSTM-MoS + partial shuffled — common questions
When was Adversarial + AWD-LSTM-MoS + partial shuffled released?
Adversarial + AWD-LSTM-MoS + partial shuffled 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.
What is Adversarial + AWD-LSTM-MoS + partial shuffled used for?
Adversarial + AWD-LSTM-MoS + partial shuffled works in Language, and is recorded as handling translation, Language modeling. 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 Adversarial + AWD-LSTM-MoS + partial shuffled?
None. Adversarial + AWD-LSTM-MoS + partial shuffled 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 Adversarial + AWD-LSTM-MoS + partial shuffled open source?
No. Adversarial + AWD-LSTM-MoS + partial shuffled has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Adversarial + AWD-LSTM-MoS + partial shuffled have?
No parameter count has been published for Adversarial + AWD-LSTM-MoS + partial shuffled, which is why no memory or speed figure appears on this page.
Who created Adversarial + AWD-LSTM-MoS + partial shuffled?
Adversarial + AWD-LSTM-MoS + partial shuffled was published by University of Texas at Austin, based in United States of America, categorised as academia.
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.