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# nemoguardrails.embeddings.providers.sentence_transformers

## Module Contents

### Classes

| Name                                                                                                                                | Description                                  |
| ----------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------- |
| [`SentenceTransformerEmbeddingModel`](#nemoguardrails-embeddings-providers-sentence_transformers-SentenceTransformerEmbeddingModel) | Embedding model using sentence-transformers. |

### Functions

| Name                                                                                      | Description |
| ----------------------------------------------------------------------------------------- | ----------- |
| [`get_executor`](#nemoguardrails-embeddings-providers-sentence_transformers-get_executor) | -           |

### API

```python
class nemoguardrails.embeddings.providers.sentence_transformers.SentenceTransformerEmbeddingModel(
    embedding_model: str,
    kwargs = {}
)
```

**Bases:** [EmbeddingModel](/guardrails-python-sdk/nemoguardrails/embeddings/providers/base#nemoguardrails-embeddings-providers-base-EmbeddingModel)

Embedding model using sentence-transformers.

This class represents an embedding model that utilizes the sentence-transformers library
for generating sentence embeddings.

**Parameters:**

The name or path of the pre-trained sentence-transformers model.

```python
nemoguardrails.embeddings.providers.sentence_transformers.SentenceTransformerEmbeddingModel.encode(
    documents: typing.List[str]
) -> typing.List[typing.List[float]]
```

Encode a list of documents into their corresponding sentence embeddings.

**Parameters:**

The list of documents to be encoded.

**Returns:** `List[List[float]]`

List\[List\[float]]: The list of sentence embeddings, where each embedding is a list of floats.

```python
nemoguardrails.embeddings.providers.sentence_transformers.SentenceTransformerEmbeddingModel.encode_async(
    documents: typing.List[str]
) -> typing.List[typing.List[float]]
```

async

Encode a list of documents into their corresponding sentence embeddings.

**Parameters:**

The list of documents to be encoded.

**Returns:** `List[List[float]]`

List\[List\[float]]: The list of sentence embeddings, where each embedding is a list of floats.

```python
nemoguardrails.embeddings.providers.sentence_transformers.get_executor()
```