AI models
Part-of-sentence tagging model
Training compute
1.5×10¹⁷ FLOP
Published
May 29, 2016
Part-of-sentence tagging model is an AI model developed by Carnegie Mellon University (CMU) (United States), first published in May 2016. It works in the language domain, on tasks such as part-of-speech tagging.
Training it took an estimated 1.5×10¹⁷ FLOP of compute (estimation method: hardware). It was trained on roughly 912.3K datapoints. Training ran on 1 NVIDIA GeForce GTX TITAN X for about 12 hours.
The reference paper has 3,193 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
- Part-of-speech tagging
- Training compute
- 1.5×10¹⁷ FLOP
- Compute estimation method
- Hardware
- Dataset size
- 912.3K
- Training hardware
- NVIDIA GeForce GTX TITAN X
- Chips used
- 1
- Training time
- 12 h
- Chip-hours
- 12
- Training power draw
- 290 W
- Numerical format
- FP32
- Citations
- 3,193
- 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.
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