CAMeL-Lab / readability-arabertv2-d3tok-reg

AraBERTv2+D3Tok+Reg Readability Model

Model description

AraBERTv2+D3Tok+Reg is a readability assessment model that was built by fine-tuning the AraBERTv2 model with Mean Squared Error loss ( Reg ). 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+Reg 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-reg")
with open("/PATH/TO/preprocessed_d3tok", "r") as f:
    sentences = f.read().split("\n")
results = readability(sentences, function_to_apply="none")
readability_levels = [max(round(result['score']+0.5),1) for result in results]

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"
}