What is the role of transformers in embeddings?

Transformers play a critical role in generating high-quality embeddings for natural language processing (NLP) tasks. Unlike earlier methods such as Word2Vec or GloVe, which produce static word embeddings (fixed vectors regardless of context), transformers create contextual embeddings. These embeddings dynamically adjust based on the surrounding words in a sentence, capturing nuanced meanings. For example, the word “bank” in “river bank” versus “bank account” would have different vector representations when processed by a transformer. This contextual awareness is achieved through the transformer’s self-attention mechanism, which analyzes relationships between all words in a sequence simultaneously.

The core innovation enabling this is the self-attention mechanism. Transformers process input tokens (words or subwords) in parallel, computing attention scores that determine how much each token influences the representation of others. For instance, in the sentence “The cat sat on the mat,” the embedding for “cat” would be influenced by “sat” and “mat,” but not equally—the model learns which words are most relevant. This mechanism allows transformers to capture long-range dependencies and syntactic structures that simpler models miss. Multiple layers of attention and feed-forward networks refine these representations, creating embeddings that encode both local and global context.

Transformers are the backbone of models like BERT, GPT, and T5, which generate embeddings used in tasks like text classification, translation, and question answering. For example, BERT uses bidirectional attention to build embeddings considering both left and right context, making it effective for tasks requiring sentence-level understanding. Developers can leverage pre-trained transformer models via libraries like Hugging Face Transformers, fine-tuning them for specific use cases. A practical application might involve using BERT embeddings to improve a sentiment analysis model: the embeddings capture contextual clues like negation (e.g., “not good”) that static embeddings would mishandle. This flexibility and precision make transformers indispensable for modern NLP workflows.