What is the role of transformers in generating embeddings?

Transformers play a central role in generating embeddings by processing input data through self-attention mechanisms and layered neural networks to create context-aware representations of text. Unlike earlier methods like Word2Vec or GloVe, which produce static embeddings (fixed vectors for each word regardless of context), transformers generate dynamic embeddings that adapt to the surrounding words. For example, the word “bank” in “river bank” versus “bank account” would receive different embeddings based on the sentence’s context. This contextual understanding is achieved by analyzing relationships between all words in a sequence simultaneously, allowing the model to weigh the importance of each word relative to others.

The architecture of transformers, particularly the encoder stack in models like BERT or the decoder in GPT, enables this process. Each transformer layer consists of self-attention and feed-forward sublayers. Self-attention computes a weighted sum of all input tokens, where the weights reflect how much each token influences the current one. For instance, in the sentence “The cat sat on the mat,” the embedding for “cat” would be influenced by “sat” and “mat” but adjusted based on their relevance. As data passes through multiple layers, embeddings become increasingly refined, capturing higher-level syntactic and semantic patterns. Developers can extract these embeddings from intermediate layers (e.g., the 12th layer in BERT-base) or combine them for specific tasks.

Practically, transformer-based embeddings are used in applications like semantic search, sentiment analysis, and machine translation. For example, in a search engine, a query like “affordable electric cars” can be matched with documents containing “cheap EVs” by comparing embedding similarities. Libraries like Hugging Face’s Transformers simplify access to pretrained models, allowing developers to generate embeddings with minimal code. However, computational costs are a consideration—larger models require GPUs for efficient inference. Trade-offs between model size (e.g., BERT-base vs. BERT-large) and task performance often guide implementation choices. By leveraging transformers, embeddings become powerful tools for representing nuanced language structures in machine learning pipelines.