README.md

---
license: cc-by-4.0
language:
- he
---
# 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 BERT-base model for the named-entity-recognition task.

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

Sample usage:

```python
from transformers import pipeline

oracle = pipeline('ner', model='dicta-il/dictabert-ner', aggregation_strategy='simple')

# if we set aggregation_strategy to simple, we need to define a decoder for the tokenizer. Note that the last wordpiece of a group will still be emitted
from tokenizers.decoders import WordPiece
oracle.tokenizer.backend_tokenizer.decoder = WordPiece()

sentence = 'הכי דרמטי שיש: שער של סדריק המחליף העניק לזיו אריה ניצחון שני בשלושה משחקים ועלייה מעל הקו האדום.'
oracle(sentence)
```

Output:
```json
[
  {
    "entity_group": "PER",
    "score": "0.99978834",
    "word": "סדריק",
    "start": "22",
    "end": "27"
  },
  {
    "entity_group": "PER",
    "score": "0.99994457",
    "word": "לזי",
    "start": "41",
    "end": "44"
  },
  {
    "entity_group": "PER",
    "score": "0.99993944",
    "word": "אריה",
    "start": "46",
    "end": "50"
  }
]
```

## 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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[cc-by]: http://creativecommons.org/licenses/by/4.0/
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