> ## Documentation Index
> Fetch the complete documentation index at: https://docs.camel-ai.org/llms.txt
> Use this file to discover all available pages before exploring further.

# Camel.embeddings.sentence transformers embeddings

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

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

## SentenceTransformerEncoder

```python theme={"system"}
class SentenceTransformerEncoder:
```

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

References:
[https://www.sbert.net/](https://www.sbert.net/)

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

### **init**

```python theme={"system"}
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" />

### embed\_list

```python theme={"system"}
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" />

### get\_output\_dim

```python theme={"system"}
def get_output_dim(self):
```

**Returns:**

int: The dimensionality of the embeddings.