Embeddings are created by training machine learning models to map input data (e.g., words, images, or users) into continuous, dense vector representations. During training, the model learns to place similar data points closer together in the embedding space and dissimilar data points farther apart. For instance, in word embeddings, a neural network model is trained on a large corpus of text to learn how words co-occur with each other. This training allows the model to generate a vector for each word such that words with similar meanings have similar vectors.
The process of creating embeddings typically involves training a model using algorithms like Word2Vec, GloVe, or BERT for text data, or using convolutional or transformer-based networks for images. The input data is passed through the model, which then outputs the corresponding embedding. The model is trained to minimize the difference between the predicted embeddings and the actual relationships present in the data.
Once the model is trained, the embeddings can be extracted and used as input features for downstream tasks like classification, clustering, or similarity search. The quality of the embeddings depends heavily on the diversity of the training data and the architecture used.