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CLIPEncoder Objects

class CLIPEncoder(DenseEncoder)
Multi-modal dense encoder for text and images using CLIP-type models via HuggingFace. Arguments:
  • name (str): The name of the model to use.
  • tokenizer_kwargs (Dict): Keyword arguments for the tokenizer.
  • processor_kwargs (Dict): Keyword arguments for the processor.
  • model_kwargs (Dict): Keyword arguments for the model.
  • device (Optional[str]): The device to use for the model.
  • str0 (str1): The tokenizer for the model.
  • str2 (str1): The processor for the model.
  • str4 (str1): The model.
  • str6 (str1): The torch library.
  • str8 (str1): The PIL library.

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__init__

def __init__(**data)
Initialize the CLIPEncoder. Arguments:
  • **data (Dict): Keyword arguments for the encoder.

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__call__

def __call__(docs: List[Any],
             batch_size: int = 32,
             normalize_embeddings: bool = True) -> List[List[float]]
Encode a list of documents. Can handle both text and images. Arguments:
  • docs (List[Any]): The documents to encode.
  • batch_size (int): The batch size for the encoding.
  • normalize_embeddings (bool): Whether to normalize the embeddings.
Returns: List[List[float]]: A list of embeddings.