CoilConfig

class lightning_ir.models.bi_encoders.coil.CoilConfig(query_length: int | None = 32, doc_length: int | None = 512, similarity_function: 'cosine' | 'dot' = 'dot', add_marker_tokens: bool = False, token_embedding_dim: int = 32, cls_embedding_dim: int = 768, projection: 'linear' | 'linear_no_bias' = 'linear', **kwargs)[source]

Bases: MultiVectorBiEncoderConfig

Configuration class for COIL models.

__init__(query_length: int | None = 32, doc_length: int | None = 512, similarity_function: 'cosine' | 'dot' = 'dot', add_marker_tokens: bool = False, token_embedding_dim: int = 32, cls_embedding_dim: int = 768, projection: 'linear' | 'linear_no_bias' = 'linear', **kwargs) → None[source]

A COIL model encodes queries and documents separately, and computes a similarity score using the maximum similarity …

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”.

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

  • token_embedding_dim (int | None) – The output embedding dimension for tokens. Defaults to 32.

  • cls_embedding_dim (int | None) – The output embedding dimension for the [CLS] token. Defaults to 768.

  • projection (Literal["linear", "linear_no_bias"], optional) – Whether and how to project the embeddings. Defaults to “linear”.

Methods

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

A COIL model encodes queries and documents separately, and computes a similarity score using the maximum similarity ...

Attributes

model_type

Model type for COIL models.
model_type: str = 'coil'

Model type for COIL models.