Measuring similarity between embeddings is a basic activity in machine learning (ML), natural language processing (NLP), and knowledge retrieval.
Embeddings symbolize knowledge factors (phrases, sentences, photographs, and so forth.) as dense vectors in high-dimensional house, the place semantically or functionally comparable gadgets ought to be positioned nearer collectively.
Vector embeddings are sometimes in contrast utilizing distance metrics, which quantify the distinction or similarity between two vectors.
Following are the three key similarity measures in vector:
1.Euclidean distance
2.Cosine similarity
3.Dot product
1. Euclidean Distance The Straight Line Distance:
Euclidean distance is essentially the most intuitive and generally understood similarity measure. Consider it because the…
