Training and Finetuning Sparse Embedding Models with Sentence Transformers v5

Sentence Transformers v5.0 introduces sparse embedding model training capabilities, supporting finetuning of encoders like SPLADE and CSR models. Key components include model architecture, datasets, loss functions, training arguments, evaluators, and the trainer class. Sparse models offer advantages in hybrid search scenarios and can be integrated with vector databases like Qdrant. This update allows customization of sparse models for specific domains or languages, enhancing performance in semantic search and retrieval tasks.