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References (80)

[1]
SMEC: Rethinking Matryoshka Representation Learning for Retrieval Embedding Compression
2025Biao Zhang, Lixing Chen et al.
[2]
EmbeddingGemma: Powerful and Lightweight Text Representations
2025Henrique Schechter Vera, Sahil Dua et al.
[3]
mmBERT: A Modern Multilingual Encoder with Annealed Language Learning
2025Marc Marone, Orion Weller et al.
[4]
PyLate: Flexible Training and Retrieval for Late Interaction Models
2025Antoine Chaffin, Raphaël Sourty
[5]
Seq vs Seq: An Open Suite of Paired Encoders and Decoders
2025Orion Weller, Kathryn Ricci et al.
[6]
ThinkingViT: Matryoshka Thinking Vision Transformer for Elastic Inference
2025Ali Hojjat, Janek Haberer et al.
[7]
FineWeb2: One Pipeline to Scale Them All - Adapting Pre-Training Data Processing to Every Language
2025Guilherme Penedo, Hynek Kydlícek et al.
[8]
Biomed-Enriched: A Biomedical Dataset Enriched with LLMs for Pretraining and Extracting Rare and Hidden Content
2025Rian Touchent, Nathan Godey et al.
[9]
BioClinical ModernBERT: A State-of-the-Art Long-Context Encoder for Biomedical and Clinical NLP
2025Thomas Sounack, Joshua Davis et al.
[10]
Causal Head Gating: A Framework for Interpreting Roles of Attention Heads in Transformers
2025Andrew Nam, Henry Conklin et al.
[11]
Qwen3 Technical Report
2025An Yang, Anfeng Li et al.
[12]
Enhancing Multilingual LLM Pretraining with Model-Based Data Selection
2025Bettina Messmer, Vinko Sabolcec et al.
[13]
SmolLM2: When Smol Goes Big - Data-Centric Training of a Small Language Model
2025Loubna Ben Allal, Anton Lozhkov et al.
[14]
Mass-Editing Memory with Attention in Transformers: A cross-lingual exploration of knowledge
2025D. Mela, Aitor Gonzalez-Agirre et al.
[15]
3CEL: A corpus of legal Spanish contract clauses
2025Nuria Aldama-García, Patricia Marsa Morales et al.
[16]
Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference
2024Benjamin Warner, Antoine Chaffin et al.
[17]
HydraViT: Stacking Heads for a Scalable ViT
2024Janek Haberer, Ali Hojjat et al.
[18]
mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval
2024Xin Zhang, Yanzhao Zhang et al.
[19]
Flextron: Many-in-One Flexible Large Language Model
2024Ruisi Cai, Saurav Muralidharan et al.
[20]
Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations
2024Alexander Hägele, Elie Bakouch et al.

Showing 20 of 80 references

Founder's Pitch

"MrBERT provides multilingual and domain-specific language model optimizations with open-source availability on Huggingface, targeting efficient inference and specialized performance."

Multilingual NLPScore: 7View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

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5

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1/4 signals

2.5

Series A Potential

4/4 signals

10

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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