UltraBERT: Beyond the Limit of BERT Pre-Training

While decoders have rapidly scaled, encoders have remained largely unchanged since BERT. We revisit this disparity by frozen backbone evaluation via probing. Under this lens, the representations of BERT encoders become increasingly $\textit{unexploitable}$ by frozen probes, despite improved perplexity. The misalignment originates in BERT's flat design, which couples representation learning to the token reconstruction loss. We propose $\textbf{CrossBERT}$, a two-part architecture that separates the learning of high-quality encoded representations from the rigid grounding of token reconstruction. This design further enables high masking ratios ($\ge 50\%$) and gradient collection over all tokens via a $\textit{Complementary Masking Strategy}$, respectively increasing throughput by $1.5$ to $2\times$ and sample efficiency by $2\times$. Overall, CrossBERT demonstrates monotonic scaling and superior performance on MTEB(eng, v2) and frozen GLUE benchmarks.

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 Spanish-specific tasks, while establishing robust performance across specialized biomedical and legal domains. To bridge the gap between research and production, we incorporate Matryoshka Representation Learning (MRL), enabling flexible vector sizing that significantly reduces inference and storage costs. Ultimately, the MrBERT family demonstrates that modern encoder architectures can be optimized for both localized linguistic excellence and efficient, high-stakes domain specialization. We open source the complete model family on Huggingface.

The introduction of BERT established encoder-only transformer models as a foundational paradigm in natural language processing. Encoder-only models remain the standard tool for classification, tagging and retrieval, where contextual representations and low inference cost matter more than text generation, yet Turkish lacks a monolingual encoder trained from scratch with the advances consolidated in ModernBERT (rotary positional embeddings, FlashAttention, refined normalization). We introduce TabiBERT, a monolingual Turkish encoder based on the ModernBERT architecture, pretrained from scratch for one trillion tokens sampled from an 86.58B-token multi-domain corpus of web text (72%), scientific publications (19%), source code (6%) and mathematical content (0.3%). The model supports a context length of 8,192 tokens, sixteen times that of existing Turkish BERT models, and inherits the ModernBERT architecture's efficiency at long context. For rigorous and reproducible evaluation we introduce TabiBench, a benchmark of 27 datasets across eight task categories with standardized splits and evaluation protocols, summarized as a GLUE-style macro-average on a 0-100 scale. TabiBERT leads the Turkish models in five of eight categories and BERTurk, the previous best, in six of eight; the gains concentrate on question answering (+9.55 F1) and code retrieval (+2.41 NDCG@10), while the four short-text categories are near saturation. Its average of 77.28 exceeds BERTurk's 75.66; the multilingual mmBERT reaches 78.98 with twice the parameters and three times the training tokens, at 41% more tokens per Turkish input. We release model weights, training configurations and evaluation code as a transparent and reproducible foundation for future Turkish encoder research.