Named Entity Recognition model
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
- Carnegie Mellon University (CMU)
- Organisation type
- Academia
- Country
- United States of America
- Published
- 4 March 2016
- Authors
- Xuezhe Ma, Eduard Hovy
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Named entity recognition (NER), Language modeling
- Numerical format
- FP32
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
- 204,567 tokens
- Epochs
- 50
Table 2. 204567 tokens
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 9.7 × 10¹⁶ FLOP
- How it was established
- Hardware
8 hours of training for NER GeForce GTX TITAN X GPU 0.33 utilization rate
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA GeForce GTX TITAN X
- Chips used
- 1
- Chip-hours
- 8
- Wall-clock time
- 8 hours
- Power draw
- 290 W
"the model training requires about 12 hours for POS tagging and 8 hours for NER"
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
- Discretionary
- Record confidence
- Confident
- Citations
- 3,100
Sources
Where this record came from and when it was last checked.
- Reference
- End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
- Last updated
- 28 November 2025
What the numbers mean
About this model
Named Entity Recognition model was published by Carnegie Mellon University (CMU), in the country recorded as United States of America, during March 2016. The publishing organisation is categorised as academia.
It works in the domain of Language, and is recorded as performing the task of named entity recognition (NER), Language modeling.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
The training run consumed about 9.7 × 10¹⁶ FLOP, on hardware recorded as NVIDIA GeForce GTX TITAN X. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Training consumed a corpus of around 204,567 tokens of text.
It is tracked in the underlying dataset for one reason in particular: discretionary.
Answers
Named Entity Recognition model — common questions
Named Entity Recognition model— when was it released?
It was published in March 2016. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Named Entity Recognition model— what is it used for?
It works in the domain of Language, and is recorded as handling the task of named entity recognition (NER), Language modeling. These are the areas it was designed around; they describe intent rather than a hard boundary.
Named Entity Recognition model— how much compute was used to train it?
Training consumed around 9.7 × 10¹⁶ FLOP, on hardware recorded as NVIDIA GeForce GTX TITAN X. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
Named Entity Recognition model— 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.
Named Entity Recognition model— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
Named Entity Recognition model— 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.
Named Entity Recognition model— who created it?
It was published by Carnegie Mellon University (CMU), based in United States of America, an organisation categorised as academia.
The other direction
Looking at it from the other side?
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