library_name: transformers
tags: []
araelectra-base-discriminator-2703-arabic_tydiqa
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
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鈥檃n QA 2023 Shared Task: Low Resource Enhanced Transformer-based Ensemble Approach for Qur鈥檃nic 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/}, }