---
title: "SentenceTransformersSparseTextEmbedder"
id: sentencetransformerssparsetextembedder
slug: "/sentencetransformerssparsetextembedder"
description: "Use this component to embed a simple string (such as a query) into a sparse vector using Sentence Transformers models."
---

# SentenceTransformersSparseTextEmbedder

Use this component to embed a simple string (such as a query) into a sparse vector using Sentence Transformers models.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | Before a sparse embedding [Retriever](../retrievers.mdx) in a query/RAG pipeline                                                |
| **Mandatory run variables**            | `text`: A string                                                                                                               |
| **Output variables**                   | `sparse_embedding`: A [`SparseEmbedding`](../../concepts/data-classes.mdx#sparseembedding) object                                           |
| **API reference**                      | [Embedders](/reference/embedders-api)                                                                                                 |
| **GitHub link**                        | https://github.com/deepset-ai/haystack/blob/main/haystack/components/embedders/sentence_transformers_sparse_text_embedder.py |
| **Package name** | `haystack-ai` |

</div>

For embedding lists of documents, use the [`SentenceTransformersSparseDocumentEmbedder`](sentencetransformerssparsedocumentembedder.mdx), which enriches the document with the computed sparse embedding.

## Overview

`SentenceTransformersSparseTextEmbedder` transforms a string into a sparse vector using sparse embedding models supported by the Sentence Transformers library.

When you perform sparse embedding retrieval, use this component first to transform your query into a sparse vector. Then, the Retriever will use the sparse vector to search for similar or relevant documents.

### Compatible Models

The default embedding model is [`prithivida/Splade_PP_en_v2`](https://huggingface.co/prithivida/Splade_PP_en_v2). You can specify another model with the `model` parameter when initializing this component.

Compatible models are based on SPLADE (SParse Lexical AnD Expansion), a technique for producing sparse representations for text, where each non-zero value in the embedding is the importance weight of a term in the vocabulary. This approach combines the benefits of learned sparse representations with the efficiency of traditional sparse retrieval methods. For more information, see [our docs](../retrievers.mdx#sparse-embedding-based-retrievers) that explain sparse embedding-based Retrievers further.

You can find compatible SPLADE models on the [Hugging Face Model Hub](https://huggingface.co/models?search=splade).

### Authentication

Authentication with a Hugging Face API Token is only required to access private or gated models.

The component uses an `HF_API_TOKEN` or `HF_TOKEN` environment variable, or you can pass a Hugging Face API token at initialization. See our [Secret Management](../../concepts/secret-management.mdx) page for more information.

```python
from haystack.utils import Secret
from haystack.components.embedders import SentenceTransformersSparseTextEmbedder

text_embedder = SentenceTransformersSparseTextEmbedder(
    token=Secret.from_token("<your-api-key>"),
)
```

### Backend Options

This component supports multiple backends for model execution:

- **torch** (default): Standard PyTorch backend
- **onnx**: Optimized ONNX Runtime backend for faster inference
- **openvino**: Intel OpenVINO backend for additional optimizations on Intel hardware

You can specify the backend during initialization:

```python
embedder = SentenceTransformersSparseTextEmbedder(
    model="prithivida/Splade_PP_en_v2",
    backend="onnx",
)
```

For more information on acceleration and quantization options, refer to the [Sentence Transformers documentation](https://sbert.net/docs/sentence_transformer/usage/efficiency.html).

### Prefix and Suffix

Some models may benefit from adding a prefix or suffix to the text before embedding. You can specify these during initialization:

```python
embedder = SentenceTransformersSparseTextEmbedder(
    model="prithivida/Splade_PP_en_v2",
    prefix="query: ",
    suffix="",
)
```

:::tip
If you create a Sparse Text Embedder and a Sparse Document Embedder based on the same model, Haystack takes care of using the same resource behind the scenes in order to save resources.
:::

## Usage

### On its own

```python
from haystack.components.embedders import SentenceTransformersSparseTextEmbedder

text_to_embed = "I love pizza!"

text_embedder = SentenceTransformersSparseTextEmbedder()

print(text_embedder.run(text_to_embed))

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

### In a pipeline

Currently, sparse embedding retrieval is only supported by `QdrantDocumentStore`.

First, install the required package:

```shell
pip install qdrant-haystack
```

Then, try out this pipeline:

```python
from haystack import Document, Pipeline
from haystack.components.embedders import (
    SentenceTransformersSparseDocumentEmbedder,
    SentenceTransformersSparseTextEmbedder,
)
from haystack_integrations.components.retrievers.qdrant import (
    QdrantSparseEmbeddingRetriever,
)
from haystack_integrations.document_stores.qdrant import QdrantDocumentStore

document_store = QdrantDocumentStore(
    ":memory:",
    recreate_index=True,
    use_sparse_embeddings=True,
)

documents = [
    Document(content="My name is Wolfgang and I live in Berlin"),
    Document(content="I saw a black horse running"),
    Document(content="Germany has many big cities"),
    Document(content="Sentence Transformers provides sparse embedding models."),
]

# Embed and write documents
sparse_document_embedder = SentenceTransformersSparseDocumentEmbedder(
    model="prithivida/Splade_PP_en_v2",
)
documents_with_sparse_embeddings = sparse_document_embedder.run(documents)["documents"]
document_store.write_documents(documents_with_sparse_embeddings)

# Query pipeline
query_pipeline = Pipeline()
query_pipeline.add_component(
    "sparse_text_embedder",
    SentenceTransformersSparseTextEmbedder(),
)
query_pipeline.add_component(
    "sparse_retriever",
    QdrantSparseEmbeddingRetriever(document_store=document_store),
)
query_pipeline.connect(
    "sparse_text_embedder.sparse_embedding",
    "sparse_retriever.query_sparse_embedding",
)

query = "Who provides sparse embedding models?"

result = query_pipeline.run({"sparse_text_embedder": {"text": query}})

print(result["sparse_retriever"]["documents"][0])

# Document(id=...,
# content: 'Sentence Transformers provides sparse embedding models.',
# score: 0.56...)
```
