AI models
GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (WT2)
Training compute
4.6×10¹⁷ FLOP
Parameters
38M
Published
Aug 29, 2017
GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (WT2) is an AI model developed by Ben-Gurion University of the Negev (Israel), first published in August 2017. It works in the language domain, on tasks such as language modeling.
Training it took an estimated 4.6×10¹⁷ FLOP of compute (estimation method: operation counting). The model has 38,000,000 parameters. It was trained on roughly 2M datapoints.
Access: Unreleased. Its weights are not openly released. The reference paper has 4 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Ben-Gurion University of the Negev
- Country of organization
- Israel
- Domain
- Language
- Task
- Language modeling
- Training compute
- 4.6×10¹⁷ FLOP
- Compute estimation method
- Operation counting
- Parameters
- 38,000,000
- Dataset size
- 2M
- Model accessibility
- Unreleased
- Open weights
- No
- Citations
- 4
- Epoch confidence
- Confident
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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