---
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 = 'בשנת 1948 השלים אפרים קישון את [MASK] בפיסול מתכת ובתולדות האמנות והחל לפרסם מאמרים הומוריסטיים'

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 לימודיו / הכשרתו
```

## 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].

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