NeoBERT
NeoBERT is a next-generation encoder model for English text representation, pre-trained from scratch on the RefinedWeb dataset. NeoBERT integrates state-of-the-art advancements in architecture, modern data, and optimized pre-training methodologies. It is designed for seamless adoption: it serves as a plug-and-play replacement for existing base models, relies on an optimal depth-to-width ratio, and leverages an extended context length of 4,096 tokens. Despite its compact 250M parameter footprint, it is the most efficient model of its kind and achieves state-of-the-art results on the massive MTEB benchmark, outperforming BERT large, RoBERTa large, NomicBERT, and ModernBERT under identical fine-tuning conditions.
Get started
Ensure you have the following dependencies installed:
pip install transformers torch xformers==0.0.28.post3
If you would like to use sequence packing (un-padding), you will need to also install flash-attention:
pip install transformers torch xformers==0.0.28.post3 flash_attn
How to use
Load the model using Hugging Face Transformers:
from transformers import AutoModel, AutoTokenizer
model_name = "chandar-lab/NeoBERT"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
# Tokenize input text
text = "NeoBERT is the most efficient model of its kind!"
inputs = tokenizer(text, return_tensors="pt")
# Generate embeddings
outputs = model(**inputs)
embedding = outputs.last_hidden_state[:, 0, :]
print(embedding.shape)
Features
| Feature | NeoBERT |
|---|---|
Depth-to-width | 28 × 768 |
Parameter count | 250M |
Activation | SwiGLU |
Positional embeddings | RoPE |
Normalization | Pre-RMSNorm |
Data Source | RefinedWeb |
Data Size | 2.8 TB |
Tokenizer | google/bert |
Context length | 4,096 |
MLM Masking Rate | 20% |
Optimizer | AdamW |
Scheduler | CosineDecay |
Training Tokens | 2.1 T |
Efficiency | FlashAttention |
License
Model weights and code repository are licensed under the permissive MIT license.
Citation
If you use this model in your research, please cite:
@misc{breton2025neobertnextgenerationbert,
title={NeoBERT: A Next-Generation BERT},
author={Lola Le Breton and Quentin Fournier and Mariam El Mezouar and Sarath Chandar},
year={2025},
eprint={2502.19587},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.19587},
}
Contact
For questions, do not hesitate to reach out and open an issue on here or on our GitHub.