Embeddings for unstructured data are generated through a process that converts raw data, such as text, images, or audio, into a numerical format that can be easily processed by machine learning algorithms. This transformation allows the data to be represented as vectors in a continuous vector space, where similar items are closer together. For example, in natural language processing, words or sentences are converted into fixed-length vectors that capture their meanings and relationships. Techniques like Word2Vec, GloVe, or Sentence Transformers are commonly used for text data, while Convolutional Neural Networks (CNNs) may be applied to images.
The generation of embeddings typically involves training a model on a large dataset. For text, this can be done using context-based approaches where the model learns the relationships between words based on their surrounding context in sentences. For instance, Word2Vec uses two methods—Skip-Gram and Continuous Bag of Words—to predict words based on their neighborhoods. As a result, words that appear in similar contexts will have similar vector representations. For images, a CNN can be trained on labeled data to extract features that capture important visual information, which can then be used to create embeddings.
Once the embeddings are generated, they can be used for various tasks such as classification, clustering, or recommendation systems. For instance, in a text classification task, the embeddings can serve as the input features for a classifier that predicts the category of a given piece of text. Similarly, in image recognition, the embeddings can help compare and organize images based on visual similarity. Overall, generating embeddings transforms unstructured data into a more manageable form that enhances the ability of machine learning models to learn and make predictions.