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

<a id="camel.embeddings.vlm_embedding._feature_output_to_tensor"></a>

## _feature_output_to_tensor

```python
def _feature_output_to_tensor(outputs: Any):
```

Return the embedding tensor from CLIP-like `get_*_features` results.

Transformers v5 returns :class:`~transformers.modeling_outputs.
BaseModelOutputWithPooling` with projected features in ``pooler_output``;
older versions returned a raw tensor.

<a id="camel.embeddings.vlm_embedding.VisionLanguageEmbedding"></a>

## VisionLanguageEmbedding

```python
class VisionLanguageEmbedding:
```

Provides image embedding functionalities using multimodal model.

**Parameters:**

- **model_name**: The model type to be used for generating embeddings. And the default value is: obj:`openai/clip-vit-base-patch32`.

<a id="camel.embeddings.vlm_embedding.VisionLanguageEmbedding.__init__"></a>

### __init__

```python
def __init__(self, model_name: str = 'openai/clip-vit-base-patch32'):
```

Initializes the: obj: `VisionLanguageEmbedding` class with a
specified model and return the dimension of embeddings.

**Parameters:**

- **model_name** (str, optional): The version name of the model to use. (default: :obj:`openai/clip-vit-base-patch32`)

<a id="camel.embeddings.vlm_embedding.VisionLanguageEmbedding.embed_list"></a>

### embed_list

```python
def embed_list(self, objs: List[Union[Image.Image, str]], **kwargs: Any):
```

Generates embeddings for the given images or texts.

**Parameters:**

- **objs** (List[Image.Image|str]): The list of images or texts for which to generate the embeddings.
- **image_processor_kwargs**: Extra kwargs passed to the image processor.
- **tokenizer_kwargs**: Extra kwargs passed to the text tokenizer (processor).
- **model_kwargs**: Extra kwargs passed to the main model.

**Returns:**

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

<a id="camel.embeddings.vlm_embedding.VisionLanguageEmbedding.get_output_dim"></a>

### get_output_dim

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

**Returns:**

  int: The dimensionality of the embedding for the current model.
