Update README.md

README.md

@@ -12,5 +12,68 @@ tags:
 pretty_name: MedTurkQuAD
 size_categories:
 - 1K<n<10K
+dataset_info:
+  total_examples: 8200
+  total_paragraphs: 875
+  source_articles: 618
+source_datasets:
+- original
+paperswithcode_id: medturkquad-medical-turkish-question
+---
+# MedTurkQuAD: Medical Turkish Question-Answering Dataset
+
+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.
+For more details about the dataset, methodology, and experiments, you can refer to the corresponding [research paper](https://ieeexplore.ieee.org/abstract/document/10711128).
+
+---
+## Dataset Overview
+- **Number of Paragraphs**: 875
+- **Number of QA Pairs**: 8,200
+- **Sources**: 618 medical articles (110 Wikipedia, 508 Thesis in medicine)
+- **Languages**: Turkish
+
+### Dataset Structure
+The dataset is divided into three subsets for training, validation, and testing:
+
+| Split        | Number of Paragraphs | Number of QA Pairs |
+|--------------|-----------------------|---------------------|
+| Training     | 700                   | 6560                |
+| Validation   | 87                    | 820                 |
+| Testing      | 88                    | 820                 |
+
+---
+## How to Use
+
+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:
 
----
+```python
+from datasets import load_dataset
+
+ds = load_dataset("incidelen/MedTurkQuAD")
+```
+```python
+import pandas as pd
+
+splits = {'train': 'train.json', 'validation': 'validation.json', 'test': 'test.json'}
+df = pd.read_json("hf://datasets/incidelen/MedTurkQuAD/" + splits["train"])
+```
+---
+## Citation
+If you use this dataset, please cite the following paper:
+
+```
+@inproceedings{incidelen2024developing,
+  title={Developing Question-Answering Models in Low-Resource Languages: A Case Study on Turkish Medical Texts Using Transformer-Based Approaches},
+  author={{\.I}ncidelen, Mert and Aydo{\u{g}}an, Murat},
+  booktitle={2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP)},
+  pages={1--4},
+  year={2024},
+  organization={IEEE}
+}
+```
+---
+## Acknowledgments
+
+Special thanks to [maydogan](https://huggingface.co/maydogan) for their contributions and support in the development of this dataset.
+
+---