The library offers a comprehensive toolkit for understanding the relationships between variables in different contexts by including both classical and more specialized measures. It employs bootstrapping to obtain robust estimators of similarity measures between vectors, offering robustness to outliers, model-free estimation, confidence intervals, and enhanced understanding of relationships. The bootstrapping technique minimizes the influence of outliers, making the estimator more reliable, especially when the original data may contain anomalous values. The library can be used with a wide range of language models and text embedding techniques.
Key takeaways:
- The Fast Vector Similarity Library is a high-performance tool written in Rust for efficient computation of various similarity measures between vectors, useful for data analysis, machine learning, and statistics. It can be easily integrated with Python through provided bindings.
- The library implements several popular similarity measures including Spearman's Rank-Order Correlation, Kendall's Tau Rank Correlation, Approximate Distance Correlation, Jensen-Shannon Similarity, and Hoeffding's D Measure. It also supports bootstrapping for robust similarity computation.
- The library includes Python bindings and functions that allow seamless integration with Python code, enabling computation of similarity statistics and bootstrapped similarity statistics. It can also work with text embedding vectors from Language Models like LLMs.
- The bootstrapping technique in the library provides robustness to outliers, model-free estimation, construction of confidence intervals, and enhanced understanding of relationships between vectors. This adds a layer of robustness and flexibility to the computation of similarity measures.