Our multilingual model differs from our English language embed model, which was trained using dot product calculations. By utilizing dot products, we obtain a similarity score that is not normalized, capturing the magnitude of the compared vectors. This incorporation of dimensionality enables the multilingual embeddings to outperform the standard ones.
Incorporating AI into your products has never been more effortless. LLMRails's models enable dynamic chat functionalities, generate compelling text for product descriptions, blog posts, and articles, and comprehend the essence of text for search purposes, content moderation, and intent identification.