---
license: mit
---

# CAMeLBERT-UD-parser

## Model description
The **CAMeLBERT-UD-parser** is a neural dependency parsing model for Arabic text, specifically designed for the Universal Dependencies (UD) dependency formalism. 
It is based on the Biaffine Attention Dependency Parsing model introduced by [Dozat and Manning (2017)](https://arxiv.org/pdf/1611.01734.pdf) and implemented in 
[SuPar](https://github.com/yzhangcs/parser), which has been shown to be very effective for dependency parsing in many languages. 
The model is trained on the NUDAR (NYUAD Universal Dependency for Arabic) train set, which is a large Arabic corpus annotated with UD dependency labels. 
The model uses a CamelBERT-MSA word embedding layer, which is a pre-trained language model that has been trained on a massive dataset of Arabic text. 
The model was introduced in our paper "CamelParser2.0: A State-of-the-Art Dependency Parser for Arabic". 
The paper describes the model in detail and evaluates its performance on various Arabic dependency parsing tasks.


## Intended uses
The CAMeLBERT-UD-parser is shipped with the [CAMeLParser](https://github.com/CAMeL-Lab/camel_parser) as one of the default parsing models, 
and can be selected when parsing texts using the UD formalism.

## Citation
```bibtex
@inproceedings{Elshabrawy:2023:camelparser,
    title = "{CamelParser2.0: A State-of-the-Art Dependency Parser for Arabic}",
    author = {Ahmed Elshabrawy and
              Muhammed AbuOdeh and
              Go Inoue and
              Nizar Habash} ,
    booktitle = {Proceedings of The First Arabic Natural Language Processing Conference (ArabicNLP 2023)},
    year = "2023"
}
```