Create README.md

README.md

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+---
+license: apache-2.0
+task_categories:
+- question-answering
+language:
+- am
+pretty_name: AmaSquad
+---
+# AmaSQuAD - Amharic Question Answering Dataset
+
+## Dataset Overview
+AmaSQuAD is a synthetic dataset created by translating the SQuAD 2.0 dataset into Amharic using a novel translation framework. The dataset addresses key challenges, including:  
+- Misalignment between translated questions and answers.  
+- Presence of multiple answers in the translated context.  
+
+Techniques such as cosine similarity (using embeddings from a fine-tuned Amharic BERT model) and Longest Common Subsequence (LCS) were used to ensure high-quality alignment between questions and answers.  
+
+## Key Features
+- **Language**: Amharic, a widely spoken Semitic language with limited NLP resources.  
+- **Data Size**: Includes training and development sets based on SQuAD 2.0, tailored for extractive machine reading comprehension.  
+- **Use Case**: Designed for training and evaluating Amharic Question Answering systems, particularly extractive QA models.  
+
+## Applications
+- Developing and benchmarking machine reading comprehension models for Amharic.  
+- Bridging the resource gap in low-resource language NLP research.  
+
+## Caveats
+- As a synthetic dataset, some translation-induced artifacts may be present.  
+- The dataset complements but does not replace the need for human-curated Amharic QA datasets.  
+
+## Citation
+If you use this dataset, please cite:  
+Hailemariam, N. D., Guda, B., & Tefferi, T. *XLM-R Based Extractive Amharic Question Answering with AmaSQuAD*. Carnegie Mellon University Africa, Rwanda.