This project demonstrates various applications of text embeddings and semantic analysis using Python. Here's a breakdown of the key components and techniques demonstrated:

1. Initial Vector Space Demonstration

   • Creates a handcrafted vector space for concepts like "queen", "king", "cat", "dog", "lion_king", and "castle"
   • Each concept is represented by 6 dimensions: kingdom, male, is_human, is_animal, is_fictional, and size
   • Visualizes these vectors in 2D and 3D space using PCA (Principal Component Analysis)

2. Real-world Application

   • Uses the SentenceTransformer model 'all-mpnet-base-v2' for generating text embeddings
   • Works with a dataset of animal names stored in 'animals.parquet'
   • Demonstrates several key applications:

3. Key Features:

   • Similarity Search: Shows how to find semantically similar terms using cosine similarity
   • Clustering: Uses K-means clustering to group similar concepts
   • Dimensionality Reduction: Applies PCA to visualize high-dimensional embeddings in 2D/3D space
   • Semantic Analysis: Analyzes relationships between words and concepts
   • Abstract Concept Analysis: Works with a dataset of abstract nouns to find semantic relationships

4. Notable Examples:

   • Demonstrates finding similar animals to "birds"
   • Shows clustering of animal concepts
   • Analyzes semantic aspects of text (like laptop review analysis)
   • Maps relationships between concrete and abstract concepts

The project effectively shows how modern NLP techniques can be used to understand and analyze semantic relationships between words and concepts quantitatively, making it useful for applications like semantic search, content recommendation, and text analysis.

Let me know if you'd like me to elaborate on any of these aspects or explain specific parts of the implementation in more detail.

I'm happy to provide more information or answer any questions you may have.