orionweller/mmBERT-pretrain-p2-fineweb2-remaining
Updated • 34.6k
How to use agentlans/mmBERT-small-safetensors with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("fill-mask", model="agentlans/mmBERT-small-safetensors") # Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("agentlans/mmBERT-small-safetensors")
model = AutoModel.from_pretrained("agentlans/mmBERT-small-safetensors", device_map="auto")This repository provides an unofficial Safetensors-converted version of jhu-clsp/mmBERT-small. Converting the weights to Safetensors improves loading security and allows you to use the model with recent versions of the transformers library without requiring trust_remote_code=True.
mmBERT-small is a state-of-the-art multilingual encoder trained on 3T+ tokens across 1,800+ languages, utilizing novel techniques for learning low-resource languages during the annealing/decay phase.
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("agentlans/mmBERT-small-safetensors")
model = AutoModel.from_pretrained("agentlans/mmBERT-small-safetensors")
If you use mmBERT in your research, please cite the original authors:
@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},
}
Base model
jhu-clsp/mmBERT-small