AWD-LSTM-MoS + dynamic evaluation (WT2, 2018)
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
- Peking University,Microsoft Research Asia
- Organisation type
- Academia,Industry
- Country
- China
- Published
- 18 September 2018
- Authors
- Chengyue Gong, Di He, Xu Tan, Tao Qin, Liwei Wang, Tie-Yan 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, Text classification
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.
- Parameters
- 35M
- Training data
- 2,000,000 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
- Open (non-commercial)
code, no license: https://github.com/ChengyueGongR/Frequency-Agnostic
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
- 152
- Benchmark data
- AWD-LSTM-MoS + dynamic evaluation (WT2, 2018)
"Specifically, in language modeling and machine translation, we achieve better performance than the state-of-the-art results on PTB, WT2 and WMT14 English-German datasets."
Sources
Where this record came from and when it was last checked.
- Reference
- FRAGE: Frequency-Agnostic Word Representation
- Last updated
- 11 February 2026
What the numbers mean
Where it came from
AWD-LSTM-MoS + dynamic evaluation (WT2, 2018) was published by Peking University,Microsoft Research Asia, in China, in September 2018. The organisation is categorised as academia,Industry.
It works in Language, and is recorded as doing language modeling, Translation, Text classification.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
The training set ran to roughly 2,000,000 tokens.
Its inclusion criterion is sOTA improvement.
Answers
AWD-LSTM-MoS + dynamic evaluation (WT2, 2018) — common questions
What GPU do I need to run AWD-LSTM-MoS + dynamic evaluation (WT2, 2018)?
None. AWD-LSTM-MoS + dynamic evaluation (WT2, 2018) 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 AWD-LSTM-MoS + dynamic evaluation (WT2, 2018) open source?
No. AWD-LSTM-MoS + dynamic evaluation (WT2, 2018) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does AWD-LSTM-MoS + dynamic evaluation (WT2, 2018) have?
AWD-LSTM-MoS + dynamic evaluation (WT2, 2018) has 35M parameters. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created AWD-LSTM-MoS + dynamic evaluation (WT2, 2018)?
AWD-LSTM-MoS + dynamic evaluation (WT2, 2018) was published by Peking University,Microsoft Research Asia, based in China, categorised as academia,Industry.
When was AWD-LSTM-MoS + dynamic evaluation (WT2, 2018) released?
AWD-LSTM-MoS + dynamic evaluation (WT2, 2018) was published in September 2018. 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 AWD-LSTM-MoS + dynamic evaluation (WT2, 2018) used for?
AWD-LSTM-MoS + dynamic evaluation (WT2, 2018) works in Language, and is recorded as handling language modeling, Translation, Text classification. 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.
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.