(ensemble): AWD-LSTM-DOC (fin) × 5 (PTB)
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
- NTT Communication Science Laboratories,Tohoku University
- Organisation type
- Industry,Academia
- Country
- Japan
- Published
- 30 August 2018
- Authors
- Sho Takase, Jun Suzuki, Masaaki Nagata
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- 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
- 300
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
code and weights, MIT: https://github.com/nttcslab-nlp/doc_lm?tab=readme-ov-file
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
- 37
- Benchmark data
- (ensemble): AWD-LSTM-DOC (fin) × 5 (PTB)
Sources
Where this record came from and when it was last checked.
- Reference
- Direct Output Connection for a High-Rank Language Model
- Last updated
- 11 February 2026
What the numbers mean
Where it came from
(ensemble): AWD-LSTM-DOC (fin) × 5 (PTB) was published by NTT Communication Science Laboratories,Tohoku University, in the country recorded as Japan, during August 2018. It comes out of an organisation categorised as industry,Academia.
It works in the domain of Language, and is recorded as performing the task of language modeling.
Published weights mean the model runs on your machine rather than someone else's, which is what makes the hardware question below answerable at all.
Answers
(ensemble): AWD-LSTM-DOC (fin) × 5 (PTB) — common questions
(ensemble): AWD-LSTM-DOC (fin) × 5 (PTB)— when was it released?
It was published in August 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.
(ensemble): AWD-LSTM-DOC (fin) × 5 (PTB)— what is it used for?
It works in the domain of Language, and is recorded as handling the task of 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.
(ensemble): AWD-LSTM-DOC (fin) × 5 (PTB)— 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.
(ensemble): AWD-LSTM-DOC (fin) × 5 (PTB)— 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.
(ensemble): AWD-LSTM-DOC (fin) × 5 (PTB)— 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.
(ensemble): AWD-LSTM-DOC (fin) × 5 (PTB)— 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.
(ensemble): AWD-LSTM-DOC (fin) × 5 (PTB)— who created it?
It was published by NTT Communication Science Laboratories,Tohoku University, based in Japan, an organisation categorised as industry,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.