Building kannada-kasturi-embeddings

Embeddings are numerical representations of real-world objects, such as words, phrases, text, images, audio, and video. Since the real world is so complex, these representations are usually vector arrays of floating-point numbers. This helps computers process meaning, context, and semantic relationships using distances and directions between vectors. Word2Vec and FastText Embeddings are learned numerical representations. The actual values depend on the model architecture, training data, and its training objective. So the same object (say a word) gets different embeddings in Word2Vec and GPT. Think of an embedding as a model’s custom learned coordinate system for meaning. This coordinate system lets us do useful things. Find semantically similar items, power search, feed downstream ML models, or even perform arithmetic on meaning. A general example from Word2Vec is king − man + woman ≈ queen. There are mainly two types SkipGram: Where the model predicts context words given a target…