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license: cc-by-4.0
language:
- he
- en
---
# NeoDictaBERT-bilingual: Pushing the Frontier of BERT models in Hebrew
Following the success of [ModernBERT](https://huggingface.co/blog/modernbert) and [NeoBERT](https://huggingface.co/chandar-lab/NeoBERT), we set out to train a Hebrew version of NeoBERT.
Introducing **NeoDictaBERT-bilingual**: A Next-Generation BERT-style model trained on a mixture of Hebrew and English data, technical report coming soon.
Supported Context Length: *4,096* (~**2,700** Hebrew words)
Trained on a total of 612B tokens with a context length of 1,024, and another 122B tokens with a context length of 4,096.
This is the base model pretrained on both English and Hebrew. You can access the base model pretrained *only* on Hebrew [here](https://huggingface.co/dicta-il/neodictabert).
Sample usage:
```python
from transformers import AutoModelForMaskedLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('dicta-il/neodictabert-bilingual')
model = AutoModelForMaskedLM.from_pretrained('dicta-il/neodictabert-bilingual', trust_remote_code=True)
model.eval()
sentence = '讘砖谞转 1948 讛砖诇讬诐 讗驻专讬诐 拽讬砖讜谉 讗转 [MASK] 讘驻讬住讜诇 诪转讻转 讜讘转讜诇讚讜转 讛讗诪谞讜转 讜讛讞诇 诇驻专住诐 诪讗诪专讬诐 讛讜诪讜专讬住讟讬讬诐'
output = model(tokenizer.encode(sentence, return_tensors='pt'))
# the [MASK] is the 7th token (including [CLS])
import torch
top_2 = torch.topk(output.logits[0, 7, :], 2)[1]
print('\n'.join(tokenizer.convert_ids_to_tokens(top_2))) # should print 诇讬诪讜讚讬讜 / 讛讻砖专转讜
```
## Performance
Please see our technical report for performance metrics. The model outperforms previous SOTA models on almost all benchmarks, with a noticeable jump in the QA scores which indicate a much deeper semantic understanding.
In addition the model shows strong results on retrieval tasks, outperforming other multilingual models of similar size. See technical report [here](https://arxiv.org/abs/2510.20386) for more details.
## Citation
If you use NeoDictaBERT in your research, please cite ```NeoDictaBERT: Pushing the Frontier of BERT models for Hebrew```
**BibTeX:**
```bibtex
@misc{shmidman2025neodictabertpushingfrontierbert,
title={NeoDictaBERT: Pushing the Frontier of BERT models for Hebrew},
author={Shaltiel Shmidman and Avi Shmidman and Moshe Koppel},
year={2025},
eprint={2510.20386},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2510.20386},
}
```
## License
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This work is licensed under a
[Creative Commons Attribution 4.0 International License][cc-by].
[![CC BY 4.0][cc-by-image]][cc-by]
[cc-by]: http://creativecommons.org/licenses/by/4.0/
[cc-by-image]: https://i.creativecommons.org/l/by/4.0/88x31.png
[cc-by-shield]: https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg
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