Transformer ELMo
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
- Allen Institute for AI,University of Washington
- Organisation type
- Research collective,Academia
- Country
- United States of America
- Published
- 1 January 2019
- Authors
- ME Peters, M Neumann, L Zettlemoyer, W Yih
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.
- Parameters
- 56M
- Training data
- 2,000,000,000 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
- Unreleased
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
- 459
"Our model is the Reconciled Span Parser (RSP; Joshi et al., 2018), which, using ELMo representations, achieved state of the art performance for this task. As shown in Table 2, the LSTM based models demonstrate the best performance with a 0.2% and 1.0% improvement over the Transformer and CNN models, respectively"
Sources
Where this record came from and when it was last checked.
- Reference
- Dissecting Contextual Word Embeddings: Architecture and Representation
- Last updated
- 1 January 2026
What the numbers mean
What this model is
Transformer ELMo was published by Allen Institute for AI,University of Washington, in the country recorded as United States of America, during January 2019. The category the publisher falls under is research collective,Academia.
It works in the domain of Language, and is recorded as performing the task of language modeling.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
It was trained on a corpus of about 2,000,000,000 tokens of text.
The reason it appears in this catalogue at all: sOTA improvement.
Answers
Transformer ELMo — common questions
Transformer ELMo— when was it released?
It was published in January 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.
Transformer ELMo— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling. These are the areas it was designed around; they describe intent rather than a hard boundary.
Transformer ELMo— 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.
Transformer ELMo— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Transformer ELMo— how many parameters does it have?
It has a parameter count of 56M. 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.
Transformer ELMo— who created it?
It was published by Allen Institute for AI,University of Washington, based in United States of America, an organisation categorised as research collective,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.