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.
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)
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},
}
import os
os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com"
from transformers import AutoModel, AutoTokenizer
tok = AutoTokenizer.from_pretrained("chandar-lab/NeoBERT", revision="5424c8efeea6491b151d62dee55a752165407430")
model = AutoModel.from_pretrained("chandar-lab/NeoBERT", revision="5424c8efeea6491b151d62dee55a752165407430", device_map="auto")
unsloth 
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