---
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 lemmatization task.  

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

## General guidelines for how the lemmatizer works:

Given an input text in Hebrew, it attempts to match up each word with the correct lexeme in its vocabulary. 

- If the token is split up into multiple wordpieces it doesn't cause a problem, we still predict the lexeme with a high accuracy.

- If the lexeme of a given token doesn't appear in the vocabulary, the model will attempt to predict a special token `[BLANK]`. In that case, the word is usually a name of a person or a city, and the lexeme is probably the word after removing prefixes which can be done with the [dictabert-seg](https://huggingface.co/dicta-il/dictabert-seg) tool.

- For verbs the lexeme is the 3rd person past singular form. 

This method is purely neural-based, so sometimes the predicted lexeme may not match exactly and can be in a similar semantic space. For more accurate results, one can implement rules on top of the prediction to look at the top K matches and choose using a specific set of rules.

Sample usage:

```python
from transformers import AutoModel, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('dicta-il/dictabert-lex')
model = AutoModel.from_pretrained('dicta-il/dictabert-lex', 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
[
  [
    [
      "בשנת",
      "שנה"
    ],
    [
      "1948",
      "1948"
    ],
    [
      "השלים",
      "השלים"
    ],
    [
      "אפרים",
      "אפרים"
    ],
    [
      "קישון",
      "קישון"
    ],
    [
      "את",
      "את"
    ],
    [
      "לימודיו",
      "לימוד"
    ],
    [
      "בפיסול",
      "פיסול"
    ],
    [
      "מתכת",
      "מתכת"
    ],
    [
      "ובתולדות",
      "תולדה"
    ],
    [
      "האמנות",
      "אומנות"
    ],
    [
      "והחל",
      "החל"
    ],
    [
      "לפרסם",
      "פרסם"
    ],
    [
      "מאמרים",
      "מאמר"
    ],
    [
      "הומוריסטיים",
      "הומוריסטי"
    ]
  ]
]
```


## 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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