---
features:
  - |
    Introduce `SentenceTransformersSparseTextEmbedder` and `SentenceTransformersSparseDocumentEmbedder` components.
    These components embed text and documents using sparse embedding models compatible with
    Sentence Transformers.
    Sparse embeddings are interpretable, efficient when used with inverted indexes, combine classic information
    retrieval with neural models, and are complementary to dense embeddings.
    Currently, the produced `SparseEmbedding` objects are compatible with the `QdrantDocumentStore`.

    Usage example:
    ```python
    from haystack.components.embedders import SentenceTransformersSparseTextEmbedder
    ```
    text_embedder = SentenceTransformersSparseTextEmbedder()
    text_embedder.warm_up()

    print(text_embedder.run("I love pizza!"))

    # {'sparse_embedding': SparseEmbedding(indices=[999, 1045, ...], values=[0.918, 0.867, ...])}
    ```
