To continue training a Sentence Transformer model with new data without restarting from scratch, you load the existing pre-trained or fine-tuned model and perform additional training steps with the new dataset. This approach leverages the learned representations from prior training while adapting them to new data. Here’s how it works in practice:
Step 1: Load the Existing Model
Start by loading the saved model using frameworks like Hugging Face’s transformers or sentence-transformers library. For example, SentenceTransformer.from_pretrained("your/saved_model") loads the model architecture and weights. This preserves the embeddings and other learned parameters from prior training. Ensure the new data aligns with the original training data’s structure (e.g., pairs/triplets for contrastive loss) to maintain consistency.
Step 2: Adjust Training Parameters When resuming training, use a smaller learning rate than the initial training phase. For instance, if the original learning rate was 2e-5, try 1e-5 or 5e-6 to avoid overwriting previously learned patterns. You can also freeze specific layers (e.g., early transformer layers) to focus updates on task-specific layers. Combine the new data with a subset of the original data if possible to prevent catastrophic forgetting—a scenario where the model loses prior knowledge.
Step 3: Train and Validate
Use the same loss function (e.g., MultipleNegativesRankingLoss, TripletLoss) as the original training to maintain objective consistency. For example, if the model was trained with contrastive learning on sentence pairs, apply the same setup to the new data. Monitor validation metrics (e.g., evaluation on a downstream task like semantic textual similarity) to detect overfitting. Training for fewer epochs than the initial phase is common, as the model only needs incremental adjustments.
Example Workflow Suppose you have a model fine-tuned on product descriptions and want to add support for technical documentation. Load the model, mix 20% of the original product data with the new technical docs, and train for 3-5 epochs with a reduced learning rate. This balances retaining product-related knowledge while adapting to the new domain. Tools like Weights & Biases or TensorBoard can track loss and embedding quality during training.
Key considerations include data compatibility, hyperparameter tuning, and validation. Avoid overloading the model with data that conflicts with prior patterns, and always test updated embeddings on representative tasks.