Embedding models are trained to convert high-dimensional data, like text or images, into dense, lower-dimensional vectors that capture meaningful patterns. The core idea is to teach the model to represent items in a way that similar items (e.g., words with related meanings or images of the same category) are closer together in the vector space. For text, common approaches include methods like Word2Vec, GloVe, or transformer-based models like BERT. These models learn by analyzing the context in which words or phrases appear. For example, Word2Vec uses a shallow neural network to predict surrounding words (skip-gram) or a target word based on its context (CBOW), adjusting word vectors to reflect how often they co-occur. Transformer-based models, like BERT, go further by training on masked language modeling tasks—hiding parts of the input text and learning to predict them using bidirectional context.
The training process typically involves three key steps: data preparation, model architecture setup, and optimization. First, large datasets (e.g., Wikipedia articles or web-crawled text) are preprocessed—tokenized into words or subwords, and structured into input-output pairs. For instance, in Word2Vec, a sentence like "The quick brown fox" would generate pairs like ("quick", "brown") for skip-gram. The model architecture then maps tokens to vectors (embeddings) and processes them through layers. In transformer models, self-attention mechanisms weigh the importance of different tokens in a sequence. During training, the model minimizes a loss function, such as cross-entropy for prediction tasks or contrastive loss for similarity tasks. Optimization techniques like stochastic gradient descent adjust the embeddings iteratively. For example, if the model incorrectly predicts a context word, the embeddings for the target and context words are nudged closer in the vector space.
Post-training, embeddings are often fine-tuned for specific tasks or domains. For example, a pre-trained BERT model might be adapted for sentiment analysis by adding a classification layer and training on labeled movie reviews. Evaluation metrics depend on the use case: semantic similarity might use benchmarks like GLUE or STS-B, while retrieval tasks measure recall@k (how often relevant items appear in the top k results). Developers can also reduce dimensionality using techniques like PCA or t-SNE for visualization. Practical considerations include balancing embedding size (too small loses information; too large causes overfitting) and choosing context windows (smaller windows capture syntactic patterns, larger ones capture topic-level relationships). For instance, in a recommendation system, user-item interaction data might train embeddings where similar users and items cluster, enabling efficient nearest-neighbor searches.