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QCRI/AraDiCE-BoolQ

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Hugging Face2024-11-03 更新2025-04-12 收录
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--- license: cc-by-nc-sa-4.0 task_categories: - question-answering language: - ar tags: - reading-comprehension pretty_name: 'AraDiCE -- BoolQ' dataset_info: - config_name: BoolQ-msa splits: - name: test num_examples: 984 - config_name: BoolQ-lev splits: - name: test num_examples: 984 - config_name: BoolQ-egy splits: - name: test num_examples: 984 - config_name: BoolQ-eng splits: - name: test num_examples: 984 configs: - config_name: BoolQ-msa data_files: - split: test path: BoolQ_msa/validation.json - config_name: BoolQ-lev data_files: - split: test path: BoolQ_lev/validation.json - config_name: BoolQ-egy data_files: - split: test path: BoolQ_egy/validation.json - config_name: BoolQ-eng data_files: - split: test path: BoolQ_eng/validation.json --- # AraDiCE: Benchmarks for Dialectal and Cultural Capabilities in LLMs ## Overview The **AraDiCE** dataset is designed to evaluate dialectal and cultural capabilities in large language models (LLMs). The dataset consists of post-edited versions of various benchmark datasets, curated for validation in cultural and dialectal contexts relevant to Arabic. In this repository, we present the BoolQ split of the data. <!-- ## File/Directory TO DO: - **licenses_by-nc-sa_4.0_legalcode.txt** License information. - **README.md** This file. --> ## Evaluation We have used [lm-harness](https://github.com/EleutherAI/lm-evaluation-harness) eval framework to for the benchmarking. We will soon release them. Stay tuned!! ## License The dataset is distributed under the **Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)**. The full license text can be found in the accompanying `licenses_by-nc-sa_4.0_legalcode.txt` file. ## Citation Please find the paper <a href="https://arxiv.org/pdf/2409.11404" target="_blank" style="margin-right: 15px; margin-left: 10px">here.</a> ``` @article{mousi2024aradicebenchmarksdialectalcultural, title={{AraDiCE}: Benchmarks for Dialectal and Cultural Capabilities in LLMs}, author={Basel Mousi and Nadir Durrani and Fatema Ahmad and Md. Arid Hasan and Maram Hasanain and Tameem Kabbani and Fahim Dalvi and Shammur Absar Chowdhury and Firoj Alam}, year={2024}, publisher={arXiv:2409.11404}, url={https://arxiv.org/abs/2409.11404}, } ```
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