Yes, embeddings can be learned for custom data by training a model on the specific dataset you want to represent. For example, if you have a dataset of product descriptions, you can train a model to generate embeddings that represent the semantic features of the products. In this case, the model would learn to map similar products (based on their descriptions) to similar embedding vectors.
The process for learning embeddings from custom data is similar to training embeddings for general data types like text or images. You would choose a suitable model architecture (e.g., a neural network, transformer, or convolutional network), train it on your custom dataset, and extract the learned embeddings for use in downstream tasks. You can also use pre-trained models as starting points and fine-tune them with your custom data to save computational resources and improve performance.
One key advantage of learning embeddings for custom data is that the model can be specialized to your domain, capturing nuances and relationships that general-purpose embeddings might miss. For instance, embeddings trained on medical data would capture the specific relationships between medical terms, which would be different from embeddings trained on general text.