Embedding models convert text into vectors by mapping words, phrases, or entire sentences to numerical representations in a high-dimensional space. This process starts with tokenization, where text is split into smaller units like words or subwords. Each token is then assigned an initial vector, often through a lookup table (like a matrix in a neural network), where every token corresponds to a unique row of numbers. These initial vectors are typically random but get refined during training. The model learns to adjust these numbers based on the context in which tokens appear, ensuring that similar words or phrases end up closer together in the vector space. For example, the word “dog” might start with random values but gradually move closer to “puppy” as the model processes examples of their usage.
The key to effective embeddings is capturing semantic and syntactic relationships. Models like Word2Vec, GloVe, or BERT achieve this through different strategies. Word2Vec, for instance, trains by predicting surrounding words (skip-gram) or using context to predict a target word (CBOW), forcing the model to learn meaningful associations. Transformer-based models like BERT go further by using attention mechanisms to weigh the importance of surrounding words dynamically. For example, in the sentence “The bank charged a fee for the loan,” BERT’s attention heads might link “bank” more strongly to “fee” and “loan” than to unrelated words. This contextual awareness allows embeddings to represent polysemous words (like “bank” as a financial institution vs. a riverbank) accurately based on their usage. The final vector is often a weighted average of these contextualized token representations or a special [CLS] token embedding that summarizes the entire input.
Developers can leverage libraries like Hugging Face’s Transformers or Sentence-Transformers to generate embeddings. For instance, using sentence-transformers/all-MiniLM-L6-v2, the input text “machine learning” might output a 384-dimensional vector like [0.23, -0.45, …, 0.72]. These vectors enable practical applications: search engines compare query and document embeddings via cosine similarity to rank results, while clustering algorithms group support tickets by embedding similarity. A key detail is dimensionality—higher dimensions (e.g., 768 in BERT) capture more nuance but increase computational cost. Pre-trained models are fine-tuned on domain-specific data (e.g., medical texts) to improve relevance. By converting text to vectors, embedding models turn unstructured language into a form that machine learning algorithms can process efficiently, bridging the gap between natural language and numerical computation.