To improve a Sentence Transformer’s performance on domain-specific texts like legal or medical documents, focus on domain adaptation, data preprocessing, and model customization. Pre-trained models often struggle with specialized terminology and context, so adjustments are needed to align them with your domain’s requirements.
First, fine-tune the model on domain-specific data. Start by gathering a dataset of text pairs or triplets (query, positive, negative examples) from your domain. For legal documents, this could include case law excerpts paired with relevant legal summaries. Use the model’s existing training framework (e.g., MultipleNegativesRankingLoss or TripletLoss) and retrain it on this data. For example, if your medical texts contain rare abbreviations (e.g., “MI” for myocardial infarction), the model will learn to associate these with their full terms and related concepts through fine-tuning. If labeled data is scarce, use unsupervised techniques like SimCSE by generating contrastive examples from unlabeled texts through dropout or paraphrasing.
Second, preprocess your data to highlight domain-specific structures. Legal and medical texts often contain long sentences, nested clauses, or codified terms (e.g., “ICD-10 codes” in medicine). Break these into shorter segments or use entity recognition to tag key terms (e.g., “§ 1983” in legal texts) before feeding them to the model. You can also augment data by masking domain terms during training, forcing the model to infer their meaning from context. For example, replace “hypertension” with a [MASK] token in a sentence and train the model to predict it, reinforcing its understanding of surrounding medical terms.
Finally, adjust the model architecture to better handle domain nuances. If your domain uses out-of-vocabulary terms (e.g., Latin legal phrases like “res ipsa loquitur”), extend the tokenizer’s vocabulary or use a subword tokenizer trained on domain texts. Add a domain-specific embedding layer or a lightweight adapter module (like LoRA) to the transformer to capture specialized semantics without retraining the entire model. For retrieval tasks, combine the transformer with a rule-based system—for instance, prioritize documents containing exact statute references in legal search. Evaluate performance using domain-specific metrics, like matching key legal precedents or medical diagnoses, rather than generic similarity scores.