AI models
Named Entity Recognition model
Training compute
9.7×10¹⁶ FLOP
Published
Mar 4, 2016
Named Entity Recognition model is an AI model developed by Carnegie Mellon University (CMU) (United States), first published in March 2016. It works in the language domain, on tasks such as named entity recognition (ner) and language modeling.
Training it took an estimated 9.7×10¹⁶ FLOP of compute (estimation method: hardware). It was trained on roughly 204.6K datapoints. Training ran on 1 NVIDIA GeForce GTX TITAN X for about 8 hours.
The reference paper has 3,100 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Carnegie Mellon University (CMU)
- Country of organization
- United States
- Domain
- Language
- Task
- Named entity recognition (NER), Language modeling
- Training compute
- 9.7×10¹⁶ FLOP
- Compute estimation method
- Hardware
- Dataset size
- 204.6K
- Training hardware
- NVIDIA GeForce GTX TITAN X
- Chips used
- 1
- Training time
- 8 h
- Chip-hours
- 8
- Training power draw
- 290 W
- Numerical format
- FP32
- Citations
- 3,100
- Epoch confidence
- Confident
More from Carnegie Mellon University (CMU)
SourceEpoch AI, 'AI Models'. Published online at epoch.ai. Retrieved 2026-07-29 from https://epoch.ai/data/ai-models. Licensed under CC BY 4.0.
← All ai models