Add Tigrinya-SQuAD Data Card

README.md

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+---
+viewer: false
+pretty_name: "Tigrinya-SQuAD: Machine-Translated Training Dataset"
+language:
+  - ti
+multilinguality:
+  - monolingual
+task_categories:
+  - question-answering
+size_categories:
+  - 10K<n<100K
+dataset_size: ~10MB
+download_size: ~6MB
+license: cc-by-sa-4.0
+tags:
+  - tigrinya
+  - question-answering
+  - mrc
+  - reading-comprehension
+  - low-resource
+  - african-languages
+  - machine-translation
+  - silver-standard
+splits:
+  - name: train
+    num_examples: 46737
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: "data/Tigrinya-SQuAD-v1-train-*"
+---
+
+# Tigrinya-SQuAD: Machine-Translated Training Dataset
+
+Tigrinya-SQuAD is a machine-translated and filtered version of the English SQuAD 1.1 training dataset, automatically converted to Tigrinya for training question-answering models in low-resource settings.
+
+This silver dataset serves as training data for Tigrinya question-answering systems. **For evaluation and benchmarking, please use the gold-standard [TiQuAD](https://huggingface.co/datasets/fgaim/tiquad) dataset, which contains human-annotated validation and test sets.**
+
+**Published with the paper:** [Question-Answering in a Low-resourced Language: Benchmark Dataset and Models for Tigrinya](https://aclanthology.org/2023.acl-long.661/) (ACL 2023)
+
+**Related repositories:**
+
+- [TiQuAD (gold dataset)](https://huggingface.co/datasets/fgaim/tiquad)
+- The paper's [GitHub repository](https://github.com/fgaim/TiQuAD)
+
+## Dataset Overview
+
+Tigrinya-SQuAD is designed as training data for extractive question answering in Tigrinya, a low-resource Semitic language primarily spoken in Eritrea and Ethiopia. The dataset features:
+
+- **Source data**: English SQuAD 1.1 training part, which is based on Wikipedia articles
+- **Machine-translated**: Automatically translated from English SQuAD 1.1 using neural machine translation
+- **Filtered**: Post-processed with heuristic filtering to improve quality and discarded low-quality samples
+- **Training-only**: Contains only training split; use TiQuAD for validation/testing
+- **SQuAD format**: Maintains compatibility with standard QA frameworks
+- Not human verified, to be used for training but not for final evaluation
+
+| **Split** | **Articles** | **Paragraphs** | **Questions** | **Answers** |
+|-----------|--------------|----------------|---------------|-------------|
+| Train     | 442          | 17,391         | 46,737        | 46,737      |
+
+## How to Load Tigrinya-SQuAD
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+tigrinya_squad = load_dataset("fgaim/tigrinya-squad", trust_remote_code=True)
+print(tigrinya_squad)
+```
+
+```python
+DatasetDict({
+    train: Dataset({
+        features: ['id', 'title', 'context', 'question', 'answers'],
+        num_rows: 46737
+    })
+})
+```
+
+### Data Fields
+
+- **`id`**: Unique identifier for each question-answer pair
+- **`title`**: Title of the source article (translated from English)
+- **`context`**: The paragraph containing the answer (in Tigrinya)
+- **`question`**: The question to be answered (in Tigrinya)  
+- **`answers`**: Dictionary containing:
+  - `text`: List with single answer string (training data has one answer per question)
+  - `answer_start`: List with position where answer begins in the context
+
+## Evaluation and Benchmarking
+
+This dataset contains only training data, for proper evaluation of Tigrinya question-answering models use [TiQuAD](https://huggingface.co/datasets/fgaim/tiquad), which provides multireference, human-annotated validation/test splits. Both datasets can be combined during training for best results as reported in the paper.
+
+## Citation
+
+If you use this dataset in your work, please cite the original TiQuAD paper:
+
+```bibtex
+@inproceedings{gaim-etal-2023-tiquad,
+    title = "Question-Answering in a Low-resourced Language: Benchmark Dataset and Models for {T}igrinya",
+    author = "Fitsum Gaim and Wonsuk Yang and Hancheol Park and Jong C. Park",
+    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = jul,
+    year = "2023",
+    address = "Toronto, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.acl-long.661",
+    pages = "11857--11870",
+}
+```
+
+## Data Quality and Limitations
+
+As a machine-translated dataset, Tigrinya-SQuAD has inherent limitations:
+
+- **Translation errors**: Some questions/answers may have translation artifacts
+- **Cultural adaptation**: Context may not perfectly align with Tigrinya cultural references
+- Not suitable for model evaluation or human performance comparison but for training purpose only.
+
+If you identify any issues with the dataset, please contact us at <fitsum.gaim@kaist.ac.kr>.
+
+## Acknowledgments
+
+This dataset builds upon the foundational work of the Stanford Question Answering Dataset (SQuAD) and the human-annotated TiQuAD dataset. We thank the original SQuAD creators for making their data freely available.
+
+## License
+
+This work is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/).
+
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://licensebuttons.net/l/by-sa/4.0/88x31.png" /></a>