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.
1from transformers import AutoModelForSequenceClassification, AutoTokenizer
2
3model = AutoModelForSequenceClassification.from_pretrained("mohammed-elkomy/quran-qa")
4tokenizer = AutoTokenizer.from_pretrained("mohammed-elkomy/quran-qa")
5
6inputs = tokenizer("Your input text", return_tensors="pt")
7outputs = model(**inputs)
8
9## Citation
10 If you use this model, please cite the following:
11
@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/},
}
