How do you continue training (or fine-tune further) a Sentence Transformer with new data without starting the training from scratch?

To continue training a Sentence Transformer model with new data without starting from scratch, you can leverage techniques that build upon the existing pre-trained model while incorporating incremental updates. Here’s a step-by-step explanation tailored for developers:

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1. Load the Pre-Trained Model and Prepare New Data

Start by loading your existing Sentence Transformer model (e.g., all-mpnet-base-v2) using frameworks like Hugging Face’s transformers or the sentence-transformers library. For example:

from sentence_transformers import SentenceTransformer, InputExample
model = SentenceTransformer('existing_model_path')

Prepare your new data in a format compatible with the model’s input requirements. This typically involves creating pairs or triplets (anchor, positive, negative) for contrastive learning. For instance:

new_examples = [InputExample(texts=["anchor text", "positive example", "negative example"])]

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2. Adjust Training Parameters to Avoid Overfitting

When resuming training, use a smaller learning rate to prevent overwriting the model’s existing knowledge. For example:

from sentence_transformers import losses
train_dataloader = DataLoader(new_examples, batch_size=32)
loss = losses.TripletLoss(model)
# Use a reduced learning rate (e.g., 1e-5 instead of 2e-5)
model.fit(
 train_objectives=[(train_dataloader, loss)],
 epochs=3,
 optimizer_params={'lr': 1e-5}
)

Freezing specific layers (e.g., the first 6 transformer layers) can also help preserve pre-trained features. This is done via:

for param in model._first_module().auto_model.encoder.layer[:6].parameters():
 param.requires_grad = False

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3. Combine Old and New Data for Balanced Training

To prevent catastrophic forgetting, mix a subset of the original training data with the new data. For example, allocate 20% of the batch to old data and 80% to new data. Additionally, apply data augmentation (e.g., synonym replacement or back-translation) to the new dataset to enhance generalization.

After training, validate performance on both old and new tasks using metrics like cosine similarity or retrieval accuracy. Save the updated model separately to retain the original version:

model.save('updated_model_path')

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Key Considerations:

• Use checkpointing to save intermediate models during training.
• Monitor loss curves to detect overfitting or unstable learning.
• Experiment with layer-specific learning rates (e.g., higher rates for the top layers).

By following this approach, you efficiently adapt the model to new data while preserving its foundational capabilities.