metadata
library_name: transformers
tags:
- readability
license: mit
base_model:
- aubmindlab/bert-base-arabertv2
pipeline_tag: text-classification
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"
}