CrossEncoderRerankFunction

CrossEncoderRerankFunction is a class in milvus_model that takes a query and document as input and directly returns a similarity score instead of embeddings. This functionality uses the underlying Cross-Encoder reranking model.

pymilvus.model.reranker.CrossEncoderRerankFunction

Constructor

Constructs a CrossEncoderRerankFunction for common use cases.

CrossEncoderRerankFunction(
    model_name: str = "",
    device: str = "",
    batch_size: int = 32,
    activation_fct: Any = None,
    **kwargs,
)

Parameters:

• model_name (string)

  The name of the model to use. You can specify any of the available Cross-Encoder model names, for example, cross-encoder/ms-marco-TinyBERT-L-2-v2, cross-encoder/ms-marco-MiniLM-L-2-v2, etc. If you leave this parameter unspecified, an empty string will be used. For a list of available models, refer to Pretrained Cross-Encoders.

• device (string)

  The device to use for running the model. You can specify cpu for the CPU and cuda:n for the nth GPU device.

• batch_size (int)

  The batch size for the computation.

• activation_fct

  The activation function applied on top of logits output of model.

• ****kwargs**

  Allows additional keyword arguments to be passed to the model initialization. For more information, refer to cross_encoder.

Examples

from pymilvus.model.reranker import CrossEncoderRerankFunction

# Define the rerank function
ce_rf = CrossEncoderRerankFunction(
    model_name="cross-encoder/ms-marco-MiniLM-L-6-v2",  # Specify the model name. Defaults to an emtpy string.
    device="cpu" # Specify the device to use, e.g., 'cpu' or 'cuda:0'
)