README.md

---
license: cc-by-4.0
language:
- he
inference: false
---
# DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew

State-of-the-art language model for Hebrew, released [here](https://arxiv.org/abs/2308.16687).

This is the fine-tuned model for the joint parsing of the following tasks: 

- Prefix Segmentation
- Morphological Disabmgiuation
- Lexicographical Analysis (Lemmatization)
- Syntactical Parsing (Dependency-Tree)
- Named-Entity Recognition

For the bert-base models for other tasks, see [here](https://huggingface.co/collections/dicta-il/dictabert-6588e7cc08f83845fc42a18b).

Sample usage:

```python
from transformers import AutoModel, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('dicta-il/dictabert-joint')
model = AutoModel.from_pretrained('dicta-il/dictabert-joint', trust_remote_code=True)

model.eval()

sentence = 'בשנת 1948 השלים אפרים קישון את לימודיו בפיסול מתכת ובתולדות האמנות והחל לפרסם מאמרים הומוריסטיים'
print(model.predict([sentence], tokenizer))
```

Output:
```json
TBD
```


## Citation

If you use DictaBERT in your research, please cite ```DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew```

**BibTeX:**

```bibtex
@misc{shmidman2023dictabert,
      title={DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew}, 
      author={Shaltiel Shmidman and Avi Shmidman and Moshe Koppel},
      year={2023},
      eprint={2308.16687},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

## License

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This work is licensed under a
[Creative Commons Attribution 4.0 International License][cc-by].

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