To combine multiple Sentence Transformer models or embeddings for improved task performance, you can leverage ensemble techniques that merge the strengths of different models. Here are three practical approaches:
1. Averaging or Concatenating Embeddings
The simplest method is to compute the element-wise average of embeddings from multiple models. For example, if Model A specializes in semantic similarity and Model B excels in paraphrase detection, averaging their output vectors can create a more balanced representation. Alternatively, concatenating embeddings (joining vectors end-to-end) preserves each model’s unique features but increases dimensionality. To mitigate the "curse of dimensionality," apply dimensionality reduction (e.g., PCA) or use the concatenated vector directly if computational resources allow. For instance, in a classification task, concatenating embeddings from all-mpnet-base-v2 (general-purpose) and all-distilroberta-v1 (efficient) could capture both depth and breadth of linguistic features.
2. Weighted Fusion or Meta-Learning Assign weights to embeddings based on their relevance to the task. For example, if Model A performs better on domain-specific data, give its embeddings a higher weight during averaging. Weights can be determined via grid search or validation-set performance. For more sophisticated fusion, train a meta-model (e.g., a neural network) on top of concatenated embeddings. This model learns to prioritize specific embeddings for the task. For instance, in a retrieval system, a small feedforward network could learn to combine embeddings from a legal-text model and a general-language model to improve relevance scoring.
3. Late Fusion of Model Outputs
Instead of merging embeddings directly, combine the outputs of models applied to the same task. For example, in semantic search, compute similarity scores separately using different models and average the results. This avoids compatibility issues between embedding spaces. A real-world application could involve using stsb-roberta-large for sentence-pair scoring and multi-qa-mpnet-base-dot-v1 for retrieval, then averaging their cosine similarity scores to balance precision and recall.
Key Considerations
- Ensure models are complementary (e.g., trained on different datasets or objectives).
- Validate performance on a holdout set to avoid overfitting.
- Balance computational cost: Concatenation increases inference time, while averaging is lightweight.
- For domain-specific tasks, include at least one model fine-tuned on in-domain data.
By strategically combining embeddings or outputs, you can create a more robust system that mitigates individual model weaknesses while amplifying their strengths.