AraBERT for Arabic Question Answering (arabert-qa)

Khaaaleed5/arabert-qa is an extractive question answering model for Arabic. It is a fine-tuned version of AraBERT. Given a question and a context passage, the model predicts the span of text in the passage that answers the question.

Model Details

Intended Uses

Direct use

• Answering factual questions from a supplied Arabic passage.
• A reader component in Arabic retrieval-augmented QA pipelines.
• Research and educational demos of Arabic NLP.

Out-of-scope use

• Generating answers that are not present in the context. This is an extractive model and cannot synthesize or reason beyond the passage.
• Open-domain QA without a retriever.
• High-stakes decisions (medical, legal, financial) without human verification.

How to Use

import torch
from transformers import AutoTokenizer, AutoModelForQuestionAnswering

model_id = "Khaaaleed5/arabert-qa"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForQuestionAnswering.from_pretrained(model_id)

inputs = tokenizer(question, context, return_tensors="pt", truncation=True, max_length=384)
with torch.no_grad():
    outputs = model(**inputs)

start = outputs.start_logits.argmax()
end = outputs.end_logits.argmax() + 1
answer = tokenizer.decode(inputs["input_ids"][0][start:end], skip_special_tokens=True)
print(answer)

Training Details

Training data

• Dataset: abdoelsayed/ArabicaQA

Training hyperparameters

Training results

Framework versions

• Transformers 5.16.1
• Pytorch 2.11.0+cu128
• Datasets 4.8.5
• Tokenizers 0.23.1