---
library_name: transformers
tags: []
---

# araelectra-base-discriminator-89540-pretrain

# Quran Passage Retrieval Model

This is a **fine-tuned model** on Arabic passage retrieval datasets, used for **Quran QA 2023 Task A**.

## Model Description

This model was fine-tuned to perform text classification on an Arabic dataset. The task involves identifying relevant passages from the Quran in response to specific questions, focusing on retrieval quality.

- **Base model**: Pretrained transformer-based model (e.g., AraBERT, CAMeLBERT, AraELECTRA).
- **Task**: Passage retrieval (text classification).
- **Dataset**: Fine-tuned on the Quran QA 2023 dataset.

## Intended Use

- **Language**: Arabic
- **Task**: Passage retrieval for Quran QA
- **Usage**: Use this model for ranking and retrieving relevant passages from a corpus of Arabic text, primarily for question answering tasks.

## Evaluation Results
- reported in the paper

## How to Use

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("mohammed-elkomy/quran-qa")
tokenizer = AutoTokenizer.from_pretrained("mohammed-elkomy/quran-qa")

inputs = tokenizer("Your input text", return_tensors="pt")
outputs = model(**inputs)

## Citation
    If you use this model, please cite the following:

```
@inproceedings{elkomy2023quran,
title={TCE at Qur’an QA 2023 Shared Task: Low Resource Enhanced Transformer-based Ensemble Approach for Qur’anic QA},
author={Mohammed ElKomy and Amany Sarhan},
year={2023},
url={https://github.com/mohammed-elkomy/quran-qa/},
}
```


```
@inproceedings{elkomy2022quran,
title={TCE at Qur'an QA 2022: Arabic Language Question Answering Over Holy Qur'an Using a Post-Processed Ensemble of BERT-based Models},
author={Mohammed ElKomy and Amany Sarhan},
year={2022},
url={https://github.com/mohammed-elkomy/quran-qa/},
}
```