BERT-Large-CAS (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
- Amazon
- Organisation type
- Industry
- Country
- United States of America
- Published
- 20 April 2019
- Authors
- Chenguang Wang, Mu Li, Alexander J. Smola
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Neural Architecture Search - NAS, Language modeling
- Base model
- BERT-Large
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
- 340M
- Training data
- 103,000,000 tokens
- Epochs
- 50
Taken from Bert paper.
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 specified: https://github.com/cgraywang/gluon-nlp-1/tree/lmtransformer/scripts/language_model
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
- 139
- Benchmark data
- BERT-Large-CAS (WT103)
Sources
Where this record came from and when it was last checked.
- Reference
- Language Models with Transformers
- Last updated
- 25 May 2026
What the numbers mean
About this model
BERT-Large-CAS (WT103) was published by Amazon, in the country recorded as United States of America, during April 2019. It comes out of an organisation categorised as industry.
It works in the domain of Language, and is recorded as performing the task of neural Architecture Search - NAS, Language modeling.
Its starting point was an existing base model, BERT-Large. That is why it shares the base model's general shape and size.
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 103,000,000 tokens of text.
Answers
BERT-Large-CAS (WT103) — common questions
BERT-Large-CAS (WT103)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
BERT-Large-CAS (WT103)— how many parameters does it have?
It has a parameter count of 340M. Taken from Bert paper. 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.
BERT-Large-CAS (WT103)— who created it?
It was published by Amazon, based in United States of America, an organisation categorised as industry.
BERT-Large-CAS (WT103)— when was it released?
It was published in April 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.
BERT-Large-CAS (WT103)— what is it used for?
It works in the domain of Language, and is recorded as handling the task of neural Architecture Search - NAS, 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.
BERT-Large-CAS (WT103)— what GPU do I need to run it?
None. This 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.