Embeddings evolve during training as the model learns to adjust the vector representations based on the input data. Initially, the embeddings are typically initialized with random values or pre-trained vectors, and over time, the model fine-tunes these embeddings to minimize the loss function. For example, in a word embedding model like Word2Vec, the embeddings for each word start with random values, but as the model trains, the embeddings evolve to reflect the semantic relationships between words, such as grouping synonyms or related concepts closer together in the vector space.
During training, the embeddings are updated iteratively using backpropagation, where gradients are calculated based on the loss function and used to adjust the weights of the neural network. This process allows the embeddings to better capture the underlying structure of the data, and the model learns to generate embeddings that are useful for the downstream task, whether that’s classification, clustering, or similarity comparison.
The quality of the embeddings improves as the model is exposed to more data, and over time, the embeddings begin to encode more complex relationships within the data. At the end of training, the embeddings are typically used for downstream tasks like similarity search or other machine learning applications, with the final embeddings reflecting the learned structure of the data.