What are common mistakes that could lead to poor results when using Sentence Transformer embeddings for semantic similarity tasks?

When using Sentence Transformers for semantic similarity tasks, three common mistakes lead to poor results: inadequate text preprocessing, incorrect model selection, and improper handling of similarity metrics. These issues often undermine the effectiveness of embeddings despite the inherent quality of the models.

First, poor text preprocessing significantly impacts embedding quality. Sentence Transformers expect clean, normalized text, but developers often skip steps like removing irrelevant markup (e.g., HTML tags), handling typos, or standardizing whitespace. For example, a sentence like “The quick brown fox…” (with non-breaking spaces) might be treated differently than its clean counterpart. Similarly, failing to truncate or split long texts to match the model’s maximum token limit (e.g., 512 tokens for BERT-based models) can cause silent truncation, discarding critical context. A developer analyzing product reviews might miss key details if a 600-word review is truncated mid-sentence, leading to unreliable similarity scores.

Second, using the wrong model or configuration is a frequent error. Sentence Transformers offer specialized models (e.g., all-MiniLM-L6-v2 for general use, paraphrase-multilingual-MiniLM-L12-v2 for multilingual tasks), but developers often default to a generic model without evaluating its suitability. For instance, using a model trained on short news headlines to compare technical documents will underperform. Additionally, neglecting to configure the model’s pooling method (e.g., mean pooling vs. CLS token) or failing to normalize embeddings (required for cosine similarity) can distort results. A developer might assume model.encode() handles normalization automatically, but some implementations require manual L2-normalization post-processing.

Third, misusing similarity metrics or thresholds invalidates comparisons. Cosine similarity is standard, but developers might incorrectly apply it to unnormalized embeddings or use Euclidean distance without scaling. For example, two embeddings with magnitudes of 10 and 20 might have a high cosine similarity (angle close to 0°) but appear distant in Euclidean terms, leading to misinterpretations. Overlooking task-specific thresholds is another pitfall: a cosine score of 0.7 might indicate strong similarity in legal documents but weak similarity in social media posts. Developers often hardcode thresholds without validating them against domain-specific data, resulting in false positives or missed matches.

Avoiding these mistakes requires validating preprocessing pipelines, testing multiple models, and rigorously evaluating similarity thresholds against real-world data. For instance, preprocessing scripts should explicitly handle encoding issues, and model choice should align with both the task (e.g., retrieval vs. clustering) and text domain (e.g., medical vs. casual language). By addressing these areas systematically, developers can reliably leverage Sentence Transformers for accurate semantic comparisons.