Meet mmBERT: An Encoder-only Language Model Pretrained on 3T Tokens of Multilingual Text in over 1800 Languages and 2–4× Faster than Previous Models

Why was a brand new multilingual encoder wanted?

XLM-RoBERTa (XLM-R) has dominated multilingual NLP for extra than 5 years, an unusually lengthy reign in AI analysis. While encoder-only fashions like BERT and RoBERTa have been central to early progress, most analysis power shifted towards decoder-based generative fashions. Encoders, nevertheless, stay extra environment friendly and usually outperform decoders on embedding, retrieval, and classification duties. Despite this, multilingual encoder improvement stalled.

A group of researchers from Johns Hopkins University suggest mmBERT that addresses this hole by delivering a contemporary encoder, surpassesing XLM-R and rivals latest large-scale fashions resembling OpenAI’s o3 and Google’s Gemini 2.5 Pro.

Understanding the structure of mmBERT

mmBERT comes in two primary configurations:

• Base mannequin: 22 transformer layers, 1152 hidden dimension, ~307M parameters (110M non-embedding).
• Small mannequin: ~140M parameters (42M non-embedding).

It adopts the Gemma 2 tokenizer with a 256k vocabulary, rotary place embeddings (RoPE), and FlashAttention2 for effectivity. Sequence size is prolonged from 1024 to 8192 tokens, utilizing unpadded embeddings and sliding-window consideration. This permits mmBERT to course of contexts practically an order of magnitude longer than XLM-R whereas sustaining quicker inference.

What coaching information and phases have been used?

mmBERT was skilled on 3 trillion tokens spanning 1,833 languages. Data sources embody FineWeb2, Dolma, MegaWika v2, ProLengthy, StarCoder, and others. English makes up solely ~10–34% of the corpus relying on the part.

Training was carried out in three phases:

1. Pre-training: 2.3T tokens throughout 60 languages and code.
2. Mid-training: 600B tokens throughout 110 languages, targeted on higher-quality sources.
3. Decay part: 100B tokens protecting 1,833 languages, emphasizing low-resource adaptation.

What new coaching methods have been launched?

Three primary improvements drive mmBERT’s efficiency:

• Annealed Language Learning (ALL): Languages are launched progressively (60 → 110 → 1833). Sampling distributions are annealed from high-resource to uniform, guaranteeing low-resource languages achieve affect throughout later phases with out overfitting restricted information.
• Inverse Masking Schedule: The masking ratio begins at 30% and decays to five%, encouraging coarse-grained studying early and fine-grained refinements later.
• Model Merging Across Decay Variants: Multiple decay-phase fashions (English-heavy, 110-language, and 1833-language) are mixed by way of TIES merging, leveraging complementary strengths with out retraining from scratch.

How does mmBERT carry out on benchmarks?

• English NLU (GLUE): mmBERT base achieves 86.3, surpassing XLM-R (83.3) and practically matching ModernBERT (87.4), regardless of allocating >75% of coaching to non-English information.
• Multilingual NLU (XTREME): mmBERT base scores 72.8 vs. XLM-R’s 70.4, with good points in classification and QA duties.
• Embedding duties (MTEB v2): mmBERT base ties ModernBERT in English (53.9 vs. 53.8) and leads in multilingual (54.1 vs. 52.4 for XLM-R).
• Code retrieval (CoIR): mmBERT outperforms XLM-R by ~9 factors, although EuroBERT stays stronger on proprietary information.

How does mmBERT deal with low-resource languages?

The annealed studying schedule ensures that low-resource languages profit throughout later coaching. On benchmarks like Faroese FoQA and Tigrinya TiQuAD, mmBERT considerably outperforms each o3 and Gemini 2.5 Pro. These outcomes exhibit that encoder fashions, if skilled rigorously, can generalize successfully even in excessive low-resource eventualities.

What effectivity good points does mmBERT obtain?

mmBERT is 2–4× quicker than XLM-R and MiniLM whereas supporting 8192-token inputs. Notably, it stays quicker at 8192 tokens than older encoders have been at 512 tokens. This pace increase derives from the ModernBERT coaching recipe, environment friendly consideration mechanisms, and optimized embeddings.

Summary

mmBERT comes because the long-overdue alternative for XLM-R, redefining what a multilingual encoder can ship. It runs 2–4× quicker, handles sequences as much as 8K tokens, and outperforms prior fashions on each high-resource benchmarks and low-resource languages that have been underserved in the previous. Its coaching recipe—3 trillion tokens paired with annealed language studying, inverse masking, and mannequin merging—reveals how cautious design can unlock broad generalization with out extreme redundancy. The result’s an open, environment friendly, and scalable encoder that not solely fills the six-year hole since XLM-R but in addition supplies a sturdy basis for the following era of multilingual NLP methods.

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