Sentence Transformers manage varying input lengths by first tokenizing the text into subword units (like WordPiece tokens) and truncating or padding sequences to fit the model’s maximum allowed length (typically 512 tokens). For shorter texts, padding tokens are added to reach the required input size. For longer texts, truncation removes tokens beyond the limit. The model uses attention masks to ignore padding tokens during processing, ensuring they don’t contribute to the final embedding. This standardization allows batch processing while maintaining consistent input dimensions. However, truncation can discard information in very long texts, which may impact embedding quality if critical context is lost.
Sentence length can affect embeddings, but the impact depends on the content and how the model pools information. For texts within the token limit, longer sentences may encode more nuanced semantic relationships due to richer context. However, Sentence Transformers use pooling layers (like mean or max pooling) to aggregate token embeddings into a fixed-size sentence embedding. This pooling process averages or highlights key features across tokens, which can mitigate the effect of minor length variations. For truncated texts, embeddings might miss details from the removed tokens, potentially reducing their accuracy for tasks requiring full context. Shorter texts may also yield less nuanced embeddings if they lack sufficient descriptive content.
For example, a 10-word sentence and a 300-word paragraph (within the token limit) would both be converted into embeddings, but the longer text’s embedding might capture broader context, like topic or sentiment nuances. However, if the paragraph exceeds 512 tokens and is truncated, its embedding might omit critical information from the latter half. Testing often shows that embeddings for moderately long texts (e.g., 200-400 tokens) perform well in tasks like semantic similarity, while extremely short inputs (e.g., 2-3 words) may lack depth. Developers should preprocess inputs to prioritize retaining meaningful content when truncating (e.g., keeping key phrases at the start) to minimize adverse effects.