<a id="camel.embeddings.sentence_transformers_embeddings"></a>

<a id="camel.embeddings.sentence_transformers_embeddings.SentenceTransformerEncoder"></a>

## SentenceTransformerEncoder

```python
class SentenceTransformerEncoder:
```

This class provides functionalities to generate text
embeddings using `Sentence Transformers`.

References:
https://www.sbert.net/

<a id="camel.embeddings.sentence_transformers_embeddings.SentenceTransformerEncoder.__init__"></a>

### __init__

```python
def __init__(self, model_name: str = 'intfloat/e5-large-v2', **kwargs):
```

Initializes the: obj: `SentenceTransformerEmbedding` class
with the specified transformer model.

**Parameters:**

- **model_name** (str, optional): The name of the model to use. (default: :obj:`intfloat/e5-large-v2`) **kwargs (optional): Additional arguments of :class:`SentenceTransformer`, such as :obj:`prompts` etc.

<a id="camel.embeddings.sentence_transformers_embeddings.SentenceTransformerEncoder.embed_list"></a>

### embed_list

```python
def embed_list(self, objs: list[str], **kwargs: Any):
```

Generates embeddings for the given texts using the model.

**Parameters:**

- **objs** (list[str]): The texts for which to generate the embeddings.

**Returns:**

  list[list[float]]: A list that represents the generated embedding
as a list of floating-point numbers.

<a id="camel.embeddings.sentence_transformers_embeddings.SentenceTransformerEncoder.get_output_dim"></a>

### get_output_dim

```python
def get_output_dim(self):
```

**Returns:**

  int: The dimensionality of the embeddings.
