Sentence Transformers handle varying input text lengths through a combination of truncation, padding, and attention masks, ensuring consistent embedding dimensions regardless of input size. These models, built on architectures like BERT, have a maximum sequence length (typically 512 tokens). When text exceeds this limit, it is truncated to fit. Shorter texts are padded with placeholder tokens (e.g., zeros) to reach the model’s fixed input size. Crucially, attention masks are used to tell the model which tokens are real (to process) and which are padding (to ignore). This allows the model to process batches of texts with different lengths efficiently without losing structural integrity.
The embedding generation process involves pooling layers (e.g., mean, max, or CLS token pooling) that aggregate token-level embeddings into a single fixed-size sentence embedding. For example, mean pooling averages all token embeddings in the sequence, while the CLS token uses a dedicated token’s embedding as the sentence representation. These methods ensure the final embedding dimension remains constant, even if input lengths vary. Importantly, the attention mechanism ensures padding tokens don’t influence the output, so shorter texts aren’t skewed by irrelevant padding. The model’s ability to focus on meaningful tokens means embeddings primarily reflect content, not length.
Sentence length can indirectly affect embeddings in two scenarios. First, if a text is truncated due to exceeding the model’s maximum length, information loss may occur, altering the embedding. For example, a 600-token document cut to 512 tokens might lose nuances from the removed portion. Second, longer texts within the limit may produce embeddings that capture more detailed context, as the model processes more tokens. However, pooling layers mitigate drastic differences: averaging 100 tokens versus 500 tokens may dilute individual token impacts but preserves overall semantic meaning. Testing shows that embeddings for paraphrased sentences of varying lengths (e.g., “The quick brown fox” vs. a detailed 50-word description of the same scene) remain semantically close, demonstrating robustness to length variations. Thus, while length can influence embeddings in edge cases, the architecture minimizes its impact for most practical purposes.