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References (80)
[3]
mmBERT: A Modern Multilingual Encoder with Annealed Language Learning
Founder's Pitch
"MrBERT provides multilingual and domain-specific language model optimizations with open-source availability on Huggingface, targeting efficient inference and specialized performance."
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0-10 scaleHigh Potential
2/4 signals
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1/4 signals
Series A Potential
4/4 signals
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Why It Matters
Summary from abstract: We introduce MrBERT, a family of 150M-300M parameter encoders built on the ModernBERT architecture and pre-trained on 35 languages and code. Through targeted adaptation, this model family achieves state-of-the-art results on Catalan- and Sp
Product Angle
Product angle: MrBERT: Modern Multilingual Encoders via Vocabulary, Domain, and Dimensional Adaptation
Disruption
Disruption: We introduce MrBERT, a family of 150M-300M parameter encoders built on the ModernBERT architecture and pre-trained on 35 languages and code. Through targeted adaptation, this model family achieves state-of-the-art results on Catalan- and Sp
Product Opportunity
Opportunity: We introduce MrBERT, a family of 150M-300M parameter encoders built on the ModernBERT architecture and pre-trained on 35 languages and code. Through targeted adaptation, this model family achieves state-of-the-art results on Catalan- and Sp
Use Case Idea
Potential use case: We introduce MrBERT, a family of 150M-300M parameter encoders built on the ModernBERT architecture and pre-trained on 35 languages and code. Through targeted adaptation, this model family achieves state-of-the-art results on Catalan- and Sp
Science
Technical summary: We introduce MrBERT, a family of 150M-300M parameter encoders built on the ModernBERT architecture and pre-trained on 35 languages and code. Through targeted adaptation, this model family achieves state-of-the-art results on Catalan- and Sp
Method & Eval
Method and evaluation details: We introduce MrBERT, a family of 150M-300M parameter encoders built on the ModernBERT architecture and pre-trained on 35 languages and code. Through targeted adaptation, this model family achieves state-of-the-art results on Catalan- and Sp
Caveats
Caveats not specified in the abstract.
Author Intelligence
Research Author 1
University / Research Lab
author@institution.edu
Research Author 2
University / Research Lab
author@institution.edu
Research Author 3
University / Research Lab
author@institution.edu