To prepare training data for fine-tuning a Sentence Transformer, you typically structure the data into sentence pairs or triples, depending on the training objective. For pairs, the format consists of two sentences and a label indicating their similarity (e.g., a numerical score between 0 and 1). For example, a pair like ("The cat sits on the mat", “A feline rests on the carpet”) might have a high similarity score (0.9). This format is used with loss functions like CosineSimilarityLoss, which trains the model to align embeddings of similar sentences. For triples, the format includes an anchor sentence, a positive (semantically similar) sentence, and a negative (dissimilar) sentence. For instance, an anchor like “How do I reset my password?” could be paired with a positive (“Steps to recover account access”) and a negative (“Best password manager apps”). This structure aligns with TripletLoss, which pushes the model to minimize the distance between the anchor and positive while maximizing it between the anchor and negative.
The data preparation process involves curating or generating relevant examples. If using existing datasets (e.g., STS Benchmark for pairs or Quora Question Pairs for triples), you’ll need to preprocess them into the required format. For custom data, you might collect sentence pairs from user queries and their corresponding support articles, or generate synthetic examples through techniques like back-translation or paraphrasing. For triples, selecting effective negatives is critical: hard negatives (semantically related but distinct) often yield better results than random ones. For example, in a FAQ retrieval task, a hard negative for “How to install the software?” might be “How to uninstall the software?” instead of an unrelated sentence like “Weather forecast today.” Data should be cleaned (removing duplicates, correcting typos) and normalized (lowercasing, standardizing punctuation) to reduce noise. Tools like pandas in Python or dedicated libraries like Hugging Face’s Datasets can streamline this process.
Here’s a concrete example: Suppose you’re fine-tuning for a customer support chatbot. For pairs, you might create a CSV file with columns text1, text2, and score, where text1 is a user query (“My order hasn’t arrived”), text2 is a support response (“Track your package here”), and score is 0.8. For triples, a JSON file could contain entries like {"anchor": "Payment failed error", "positive": "Troubleshoot payment issues", "negative": "How to request a refund"}. The model then learns to distinguish between closely related intents. Tools like sentence-transformers provide built-in dataloaders for these formats, simplifying integration into training pipelines. Properly structured data ensures the model learns robust embeddings tailored to your specific use case.