metadata
license: mit
task_categories:
- feature-extraction
tags:
- pretraining
- encoder
- multilingual
- fill-mask
mmBERT Training Data (Ready-to-Use)
Complete Training Dataset: Pre-randomized and ready-to-use multilingual training data (3T tokens) for encoder model pre-training.
This dataset is part of the complete, pre-shuffled training data used to train the mmBERT encoder models. Unlike the individual phase datasets, this version is ready for immediate use but the mixture cannot be modified easily. The data is provided in decompressed MDS format ready for use with ModernBERT's Composer and the ModernBERT training repository.
Sample Usage (of models trained with this data)
Here are a few quick examples showing how to use the models trained with this dataset for various tasks.
Small Model for Fast Inference (Feature Extraction)
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/mmbert-small")
model = AutoModel.from_pretrained("jhu-clsp/mmbert-small")
# Example: Get multilingual embeddings
inputs = tokenizer("Hello world! 你好世界! Bonjour le monde!", return_tensors="pt")
outputs = model(**inputs)
embeddings = outputs.last_hidden_state.mean(dim=1)
Base Model for Classification
from transformers import AutoTokenizer, AutoModelForMaskedLM
import torch
tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/mmbert-base")
model = AutoModelForMaskedLM.from_pretrained("jhu-clsp/mmbert-base")
# Example: Multilingual masked language modeling
text = "The capital of [MASK] is Paris."
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
# Get predictions for [MASK] tokens
mask_indices = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)
predictions = outputs.logits[mask_indices]
top_tokens = torch.topk(predictions, 5, dim=-1)
predicted_words = [tokenizer.decode(token) for token in top_tokens.indices[0]]
print(f"Predictions: {predicted_words}")
Licensing & Attribution
This dataset aggregates multiple open-source datasets under permissive licenses. See individual source datasets for specific attribution requirements.
Related Resources
- Models: mmBERT Model Suite
- Individual Phases: Pre-training | Mid-training | Decay
- Checkpoints: Training Checkpoints
- Paper: Arxiv link
- Code: GitHub Repository
Citation
@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},
}