AraBERTv2+D3Tok+CE Readability Model

Model description

AraBERTv2+D3Tok+CE is a readability assessment model that was built by fine-tuning the AraBERTv2 model with cross-entropy loss (CE). For the fine-tuning, we used the D3Tok input variant from 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."

Intended uses

You can use the AraBERTv2+D3Tok+CE model as part of the transformers pipeline. You need to preprocess your text into the D3Tok input variant using the preprocessing step here.

How to use

To use the model:

from transformers import pipeline
readability = pipeline("text-classification", model="CAMeL-Lab/readability-arabertv2-d3tok-CE")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
readability_levels = [int(readability(sentences)[i]['label'][6:])+1 for i in range(len(sentences))]

Citation

@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"
}