AI models
Contriever
Training compute
1.6×10²⁰ FLOP
Parameters
110M
Published
Dec 16, 2021
Contriever is an AI model developed by Meta AI, University College London (UCL), PSL University and Université Grenoble Alpes (United States, United Kingdom and France), first published in December 2021. It works in the language domain, on tasks such as language modeling.
Training it took an estimated 1.6×10²⁰ FLOP of compute (estimation method: operation counting). The model has 110,000,000 parameters. It was trained on roughly 262.1B datapoints.
Access: Open weights (non-commercial). Its weights are openly available. It is built on top of BERT-Large. The reference paper has 1,477 citations. Epoch AI rates the confidence of this record as likely.
Full record
- Organization
- Meta AI, University College London (UCL), PSL University, Université Grenoble Alpes
- Country of organization
- United States, United Kingdom, France
- Domain
- Language
- Task
- Language modeling
- Training compute
- 1.6×10²⁰ FLOP
- Compute estimation method
- Operation counting
- Parameters
- 110,000,000
- Dataset size
- 262.1B
- Model accessibility
- Open weights (non-commercial)
- Open weights
- Yes
- Base model
- BERT-Large
- Citations
- 1,477
- Epoch confidence
- Likely
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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