A new tutorial has emerged that focuses on enhancing the performance of Sentence-Transformers embedding models. This process uses a technique called Matryoshka Representation Learning (MRL). The goal is to ensure that the initial dimensions of the vector contain the most valuable semantic information.
The tutorial outlines how to train the model with MatryoshkaLoss using triplet data. After training, it tests the model’s effectiveness by checking how well it performs with truncated embeddings of different sizes: 64, 128, and 256 dimensions. This approach allows users to assess the model’s retrieval quality efficiently.
The process begins with installing necessary libraries and importing essential modules for training and evaluation. A consistent seed is set to ensure that the training process is repeatable. The tutorial emphasizes the importance of aligning random number generators for both PyTorch and CUDA when using a GPU.
Next, the tutorial presents a method for evaluating retrieval performance. It encodes queries and documents, calculates cosine similarity, and reports metrics like Mean Reciprocal Rank (MRR) and Recall. The embeddings are normalized after truncation to maintain comparability in cosine space. This makes it easier to see the differences before and after training.
The dataset used for training is the MS MARCO triplet dataset, which is streamed to build a training set of queries, positive examples, and negative examples. This setup helps in making retrieval meaningful. The tutorial highlights the importance of stopping the data collection early to keep the process manageable while still demonstrating the effects of truncation.
A strong embedding model is then loaded, and its full embedding dimensions are recorded. The tutorial runs a baseline evaluation across different dimensions to understand how truncation affects performance before any training takes place. The results are printed for comparison later.
The training phase involves creating a MultipleNegativesRankingLoss and wrapping it with MatryoshkaLoss, targeting a descending list of dimensions. After fine-tuning the model, the same evaluation benchmarks are run again to check for improvements in retrieval performance.
The conclusion of the tutorial notes that the fine-tuned model maintains strong retrieval capabilities, even when the vectors are truncated to smaller sizes, such as 64 dimensions. The results show an improvement in performance compared to the baseline. The model is saved and can be loaded with a smaller dimension setting for quicker and more efficient vector searches.
This tutorial not only provides a practical guide for enhancing embedding models but also offers a clear workflow for creating smaller, faster vector indexes while retaining the option to use full-dimensional embeddings for re-ranking when necessary. For those interested in the technical details, the full code is available online.