SingleVectorBiEncoderConfig

class lightning_ir.bi_encoder.bi_encoder_config.SingleVectorBiEncoderConfig(query_length: int | None = 32, doc_length: int | None = 512, similarity_function: 'cosine' | 'dot' = 'dot', normalization: 'l2' | None = None, sparsification: 'relu' | 'relu_log' | 'relu_2xlog' | None = None, add_marker_tokens: bool = False, query_pooling_strategy: 'first' | 'mean' | 'max' | 'sum' = 'mean', doc_pooling_strategy: 'first' | 'mean' | 'max' | 'sum' = 'mean', **kwargs)[source]

Bases: BiEncoderConfig

Configuration class for a single-vector bi-encoder model.

__init__(query_length: int | None = 32, doc_length: int | None = 512, similarity_function: 'cosine' | 'dot' = 'dot', normalization: 'l2' | None = None, sparsification: 'relu' | 'relu_log' | 'relu_2xlog' | None = None, add_marker_tokens: bool = False, query_pooling_strategy: 'first' | 'mean' | 'max' | 'sum' = 'mean', doc_pooling_strategy: 'first' | 'mean' | 'max' | 'sum' = 'mean', **kwargs)[source]

Configuration class for a single-vector bi-encoder model. A single-vector bi-encoder model pools the representations of queries and documents into a single vector before computing a similarity score.

Parameters:

  • query_length (int | None) – Maximum number of tokens per query. If None does not truncate. Defaults to 32.

  • doc_length (int | None) – Maximum number of tokens per document. If None does not truncate. Defaults to 512.

  • similarity_function (Literal['cosine', 'dot']) – Similarity function to compute scores between query and document embeddings. Defaults to “dot”.

  • normalization (Literal['l2'] | None) – Whether to normalize query and document embeddings. Defaults to None.

  • sparsification (Literal['relu', 'relu_log', 'relu_2xlog'] | None) – Whether and which sparsification function to apply. Defaults to None.

  • add_marker_tokens (bool) – Whether to prepend extra marker tokens [Q] / [D] to queries / documents. Defaults to False.

  • query_pooling_strategy (Literal['first', 'mean', 'max', 'sum'] | str) – How to pool the query token embeddings. Defaults to “mean”.

  • doc_pooling_strategy (Literal['first', 'mean', 'max', 'sum'] | str) – How to pool document token embeddings. Defaults to “mean”.

Methods

__init__([query_length, doc_length, ...])

Configuration class for a single-vector bi-encoder model.

Attributes

model_type

Model type for single-vector bi-encoder models.
model_type: str = 'single-vector-bi-encoder'

Model type for single-vector bi-encoder models.