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--- |
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license: cc-by-nc-nd-4.0 |
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task_categories: |
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- question-answering |
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task_ids: |
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- open-domain-qa |
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- extractive-qa |
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language: |
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- tr |
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tags: |
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- medical |
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pretty_name: MedTurkQuAD |
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size_categories: |
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- 1K<n<10K |
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dataset_info: |
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total_examples: 8200 |
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total_paragraphs: 875 |
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source_articles: 618 |
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source_datasets: |
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- original |
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paperswithcode_id: medturkquad-medical-turkish-question |
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--- |
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# MedTurkQuAD: Medical Turkish Question-Answering Dataset |
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MedTurkQuAD is a dataset specifically designed for question-answering (QA) tasks in the medical domain in Turkish. It contains context paragraphs derived from medical texts, paired with questions and answers related to specific diseases or medical issues. |
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For more details about the dataset, methodology, and experiments, you can refer to the corresponding [research paper](https://ieeexplore.ieee.org/abstract/document/10711128). |
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--- |
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## Dataset Overview |
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- **Number of Paragraphs**: 875 |
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- **Number of QA Pairs**: 8,200 |
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- **Sources**: 618 medical articles (110 Wikipedia, 508 Thesis in medicine) |
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- **Languages**: Turkish |
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### Dataset Structure |
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The dataset is divided into three subsets for training, validation, and testing: |
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| Split | Number of Paragraphs | Number of QA Pairs | |
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|--------------|-----------------------|---------------------| |
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| Training | 700 | 6560 | |
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| Validation | 87 | 820 | |
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| Testing | 88 | 820 | |
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--- |
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## How to Use |
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This dataset can be used with libraries such as [🤗 Datasets](https://huggingface.co/docs/datasets) or [pandas](https://pandas.pydata.org/). Below are examples of the use of the dataset: |
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```python |
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from datasets import load_dataset |
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ds = load_dataset("incidelen/MedTurkQuAD") |
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``` |
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```python |
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import pandas as pd |
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splits = {'train': 'train.json', 'validation': 'validation.json', 'test': 'test.json'} |
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df = pd.read_json("hf://datasets/incidelen/MedTurkQuAD/" + splits["train"]) |
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``` |
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--- |
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## Citation |
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If you use this dataset, please cite the following paper: |
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``` |
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@inproceedings{incidelen2024developing, |
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title={Developing Question-Answering Models in Low-Resource Languages: A Case Study on Turkish Medical Texts Using Transformer-Based Approaches}, |
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author={{\.I}ncidelen, Mert and Aydo{\u{g}}an, Murat}, |
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booktitle={2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP)}, |
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pages={1--4}, |
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year={2024}, |
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organization={IEEE} |
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} |
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``` |
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--- |
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## Acknowledgments |
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Special thanks to [maydogan](https://huggingface.co/maydogan) for their contributions and support in the development of this dataset. |
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--- |
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