How to Benchmark Embedding Models on Your Own Data

Understanding Embedding Models

• 💡 Embedding models convert text into numerical representations called dense vectors, capturing the meaning of the text.
• 🎯 These vectors enable applications like recommendation systems and retrieval-augmented generation (RAG).
• 🚀 The size of the dense vector (embedding dimension) involves a trade-off between capturing subtle meanings and computational resources.
• 📊 Models like all-MiniLM produce smaller embeddings (384 dimensions), while Quen-embedding-8B produces larger ones (4,096 dimensions).

Text Extraction and Chunking

• 📄 Vision Language Models (VLMs) are crucial for accurately extracting text from PDFs, preserving structure and handling scanned documents, unlike traditional Python libraries.
• 🧩 Text is divided into coherent chunks that focus on a single idea to maintain context, often using LLMs for this process.
• 🛠️ Tools like llama.cpp and libraries such as ranx are used for local model execution and performance metrics.

Benchmarking and Evaluation

• 📈 Public benchmarks like MT-AB can be a starting point, but custom benchmarks on private data are essential for accurate model selection.
• 🧪 Key metrics for evaluating retrieval models include Mean Reciprocal Rank (MRR), Recall@K, and NDCG, which assess the relevance and position of retrieved documents.
• 🔬 Statistical tests (e.g., paired t-test, Fisher's randomization test) are vital to determine if performance differences between models are statistically significant or due to chance.
• 📊 The ranx library facilitates calculating these metrics and performing statistical tests, providing win/loss/tie tables and p-values for robust comparison.

Multilingual Models and Data Expansion

• 🌐 Multilingual embedding models learn language-agnostic meanings, allowing vectors for the same concept in different languages (e.g., English and Arabic) to map to the same spot in the vector space.
• ⚠️ Performance can drop when merging multiple languages into a single index due to potential noise and interference.
• 🔍 When selecting models, it's crucial to verify if they were trained on the target language; models not trained on a specific language (e.g., all-MiniLM on Arabic) perform poorly.
• 📊 Custom benchmarks on domain-specific data are recommended to find models that best suit niche use cases, rather than solely relying on general leaderboards.

Practical Implementation

• 💻 The course provides notebooks for using proprietary models (Gemini, OpenAI) and open-source models (Quen, all-MiniLM, AIO) for text extraction, chunking, embedding generation, and benchmarking.
• 💰 Embedding models are generally cost-effective, with large-scale usage costing very little.
• 🚀 The ultimate goal is to build a reliable evaluation framework to select the best embedding model for specific applications, ensuring performance and accuracy.