Sentence Transformers differ from traditional word embedding models like Word2Vec or GloVe in three key ways: scope of representation, contextual understanding, and training methodology.
First, Sentence Transformers generate embeddings for entire sentences or phrases, while Word2Vec and GloVe produce embeddings for individual words. Traditional models assign a fixed vector to each word regardless of context, leading to issues with polysemy (e.g., "bank" as a financial institution versus a riverbank). In contrast, Sentence Transformers use transformer-based architectures like BERT to create context-aware representations. For example, the word "apple" in "I ate an apple" and "Apple released a new iPhone" would have different contextualized embeddings when processed by a Sentence Transformer, whereas Word2Vec would assign the same vector to both instances.
Second, Sentence Transformers capture semantic relationships between words in a sentence through attention mechanisms, which weigh the importance of different words dynamically. Word2Vec and GloVe rely on static, non-contextual patterns like co-occurrence statistics (e.g., Skip-gram in Word2Vec) or global matrix factorization (GloVe). For instance, traditional models might group "car" and "vehicle" based on frequent co-occurrence but fail to distinguish "not good" from "good" in sentiment analysis. Sentence Transformers, however, process entire sentences in parallel using self-attention, enabling them to model negation, syntax, and long-range dependencies more effectively.
Third, training objectives differ significantly. Sentence Transformers are often fine-tuned on tasks like semantic textual similarity (STS) or natural language inference (NLI) using triplet loss or contrastive learning, which explicitly optimize for semantic similarity between sentences. For example, they might be trained to map paraphrases like "How old are you?" and "What is your age?" to nearby vectors. Word2Vec and GloVe, in contrast, are trained on unsupervised word prediction tasks without direct optimization for sentence-level semantics. While traditional embeddings can be aggregated (e.g., averaged) for sentence representations, this approach loses nuance compared to purpose-built sentence embeddings from transformers.
In summary, Sentence Transformers provide context-sensitive, sentence-level semantics through transformer architectures and task-specific training, whereas traditional models offer static, word-level embeddings based on simpler statistical patterns.