README.md

---
library_name: transformers
tags:
- readability
license: mit
base_model:
- CAMeL-Lab/bert-base-arabic-camelbert-msa
pipeline_tag: text-classification
---
# CAMeLBERT+Word+CE Readability Model

## Model description
**CAMeLBERT+Word+CE** is a readability assessment model that was built by fine-tuning the **CAMeLBERT-msa** model with cross-entropy loss (**CE**).
For the fine-tuning, we used the **Word** input variant from [BAREC-Corpus-v1.0](https://huggingface.co/datasets/CAMeL-Lab/BAREC-Corpus-v1.0).
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"[A Large and Balanced Corpus for Fine-grained Arabic Readability Assessment](https://arxiv.org/abs/2502.13520)."*

## Intended uses
You can use the CAMeLBERT+Word+CE model as part of the transformers pipeline.

## How to use
To use the model with a transformers pipeline:

```python
>>> from transformers import pipeline
>>> readability = pipeline("text-classification", model="CAMeL-Lab/readability-camelbert-word-CE")
>>> text = 'و قال له انه يحب اكل الطعام بكثره'
>>> readability_level = int(readability(text)[0]['label'][6:])+1
>>> print("readability level: {}".format(readability_level))
readability level: 10
```

## Citation
```bibtex
@inproceedings{elmadani-etal-2025-readability,
    title = "A Large and Balanced Corpus for Fine-grained Arabic Readability Assessment",
    author = "Elmadani, Khalid N.  and
      Habash, Nizar  and
      Taha-Thomure, Hanada",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics"
}
```