This guide explains how to train or fine-tune Sentence Transformers models, which map variable-length text to fixed-size embeddings. While the original post is outdated, the concepts remain relevant. Here's what you need to know:
How Sentence Transformers Work
Sentence Transformers consist of two main layers: a pre-trained Transformer model (e.g., DistilRoBERTa) that generates contextualized word embeddings, followed by a pooling layer that condenses them into a single fixed-length embedding. This design significantly reduces computation compared to using a Transformer alone for semantic search.
Preparing Your Dataset
Your dataset must indicate sentence similarity. Labels can be explicit or derived from document structure (e.g., two sentences from the same paragraph are similar). Common formats include pairs with similarity scores, triplets (anchor, positive, negative), or unlabeled pairs.
Choosing a Loss Function
Select a loss function based on your data:
- ContrastiveLoss: For labeled pairs (similar vs. dissimilar)
- TripletLoss: For triplets (anchor, positive, negative)
- MultipleNegativesRankingLoss: For pairs without explicit labels, often used with natural language inference data
Training or Fine-Tuning
You can build a model from scratch using SentenceTransformer(models=[...]) or load a pre-trained model from Hugging Face Hub. Then, call model.fit() with your prepared dataset. For updated training, see the new SentenceTransformerTrainer API.
Limitations
Sentence Transformers may not be ideal for tasks requiring deep semantic understanding or very long documents. For those cases, consider cross-encoders or other architectures.
For up-to-date tutorials, refer to the official guides on embedding models, rerankers, sparse embeddings, and multimodal models.