To improve Sentence Transformers on domain-specific text, focus on adapting the model to your domain through fine-tuning, adjusting preprocessing, and leveraging domain knowledge. Here's a structured approach:
1. Fine-Tune with Domain-Specific Data
The most effective way to improve performance is to fine-tune the model on your domain data. Start by curating a dataset of text pairs (e.g., sentences, paragraphs) from your domain. For tasks like semantic similarity, generate pairs with labels indicating similarity (e.g., legal clauses from related cases or medical notes describing similar conditions). Use contrastive learning objectives like MultipleNegativesRankingLoss if labeled pairs are scarce—this works well for unsupervised scenarios by treating naturally occurring pairs (e.g., a query and its matching document) as positives. If labeled data exists, CosineSimilarityLoss or triplet loss can help. For example, fine-tuning on a dataset of legal contract clauses with semantically similar/non-similar labels helps the model learn domain-specific phrasing and relationships.
2. Optimize Tokenization and Preprocessing Domain-specific jargon, abbreviations (e.g., "HGB" for hemoglobin in medical text), or compound terms (e.g., "force majeure" in legal documents) can confuse tokenizers trained on general text. Address this by:
- Extending the tokenizer's vocabulary with frequent domain terms using
tokenizer.add_tokens(). - Adjusting preprocessing to retain domain-specific structures (e.g., keeping "§ 1983" intact in legal text instead of splitting it).
- Using a domain-adapted tokenizer if available (e.g., BioBERT's tokenizer for medical text). If the base model struggles with rare tokens, consider initializing a new embedding layer for added terms or training a custom tokenizer on your corpus.
3. Use Domain-Pretrained Models and Task-Specific Tuning Replace the base Sentence Transformer with a model pretrained on domain data. For example:
- Legal: Use
legal-bertornlpaueb/legal-bert-small-uncasedas a starting point. - Medical: Try
BioBERTormicrosoft/BiomedNLP-PubMedBERT. Fine-tune these models on your task-specific data with a smaller learning rate (e.g., 2e-5) to retain domain knowledge while adapting to your use case. Additionally, adjust pooling strategies (e.g., usingcls_tokenpooling for longer documents) and validate with domain-relevant evaluation tasks (e.g., measuring accuracy in retrieving relevant case law sections rather than relying solely on generic benchmarks like STS). If performance plateaus, combine domain-adaptive pretraining (continued pretraining on your corpus) with task-specific fine-tuning in a two-stage process.
For example, a medical implementation might:
- Start with
PubMedBERT, extend its tokenizer with EHR acronyms. - Continue pretraining on hospital discharge summaries.
- Fine-tune with triplet loss on patient note pairs to emphasize clinical similarity. This approach aligns the model’s embeddings with domain-specific semantics.