Embedding dimensionality refers to the number of dimensions (or features) in the embedding vector. The choice of dimensionality is an important factor in balancing the trade-off between capturing enough information and maintaining computational efficiency. Higher-dimensional embeddings can capture more detailed relationships within the data, but they also require more memory and computational power.
Typically, dimensionality is chosen based on experimentation. For text embeddings, dimensions between 100 and 1000 are commonly used, but the ideal size depends on factors such as the complexity of the data, the size of the dataset, and the computational resources available. For example, large pre-trained models like BERT generate embeddings with 768 dimensions. Increasing the dimensionality can improve the model's ability to capture nuanced relationships in the data, but beyond a certain point, the benefit diminishes.
In practice, it's often beneficial to start with a default or commonly used dimensionality and then adjust based on the task at hand. Dimensionality reduction techniques (like PCA or t-SNE) can be used afterward to reduce the size of embeddings while retaining the important features. Balancing dimensionality is key to achieving good performance while managing computational efficiency.