Semantic Dataset Selection

# Semantic Dataset Selection ## Description This project explores how incorporating semantic similarity metrics into hierarchical dataset selection can enhance the contextual relevance and quality of selected data subsets. ## Research Hypothesis Incorporating semantic similarity metrics into hierarchical dataset selection can enhance the contextual relevance and quality of selected data subsets. ## Implementation Approach We will develop an enhanced version of the hierarchical dataset selection algorithm incorporating semantic similarity metrics, such as word embeddings or ontology-based methods. The performance of this method will be evaluated against the original using accuracy and relevance of model predictions in NLP and image classification domains. ## Setup Instructions 1. Clone the repository: `git clone ` 2. Navigate to the project directory: `cd semantic_dataset_selection` 3. Install the required packages: `pip install -r requirements.txt` ## Usage Examples - Run training: `python src/train.py` - Evaluate results: `python src/evaluate.py` ## Expected Results We expect the enhanced algorithm to improve the contextual and thematic coherence of selected datasets, leading to more effective machine learning models. ## References - [Hierarchical Dataset Selection for High-Quality Data Sharing](http://arxiv.org/abs/2512.10952v1)