This model is a 55.86% smaller version of jhu-clsp/mmBERT-base optimized for Uzbek language via vocabulary size reduction using the trimming method.
This trimmed model should perform similarly to the original model with only 32,768 tokens and a much smaller memory footprint. However, it may not perform well for other languages as tokens not commonly used in the selected languages were removed from the vocabulary.

Model Statistics

Metric	Original	Trimmed	Reduction
Vocabulary size	256,000 tokens	32,768 tokens	87.20%
Model size	306,939,648 params	135,497,472 params	55.86%

Mining Dataset Statistics

Number of texts used for mining: 200,000 texts
Dataset: Lumberjackk/fineweb-2-trimming

Usage

python

from transformers import AutoModel, AutoTokenizer

model_name = "Lumberjackk/mmBERT-base-uzs-32768"
model = AutoModel.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

Citation

mmBERT

@misc{marone2025mmbertmodernmultilingualencoder,
      title={mmBERT: A Modern Multilingual Encoder with Annealed Language Learning}, 
      author={Marc Marone and Orion Weller and William Fleshman and Eugene Yang and Dawn Lawrie and Benjamin Van Durme},
      year={2025},
      eprint={2509.06888},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.06888}, 
}

alphaedge-ai/mmBERT-base-uzs-16384

mmBERT-base-uzs-32768

Model Statistics

Mining Dataset Statistics

Usage

Citation

mmBERT

alphaedge-ai/
mmBERT-base-uzs-16384