DprConfig

class lightning_ir.models.dpr.DprConfig(query_length: int = 32, doc_length: int = 512, similarity_function: Literal['cosine', 'dot'] = 'dot', normalize: bool = False, sparsification: Literal['relu', 'relu_log'] | None = None, add_marker_tokens: bool = False, query_pooling_strategy: Literal['first', 'mean', 'max', 'sum'] = 'first', doc_pooling_strategy: Literal['first', 'mean', 'max', 'sum'] = 'first', embedding_dim: int | None = None, projection: Literal['linear', 'linear_no_bias'] | None = 'linear', **kwargs)[source]

Bases: SingleVectorBiEncoderConfig

Configuration class for a DPR model.

__init__(query_length: int = 32, doc_length: int = 512, similarity_function: Literal['cosine', 'dot'] = 'dot', normalize: bool = False, sparsification: Literal['relu', 'relu_log'] | None = None, add_marker_tokens: bool = False, query_pooling_strategy: Literal['first', 'mean', 'max', 'sum'] = 'first', doc_pooling_strategy: Literal['first', 'mean', 'max', 'sum'] = 'first', embedding_dim: int | None = None, projection: Literal['linear', 'linear_no_bias'] | None = 'linear', **kwargs) → None[source]

A DPR model encodes queries and documents separately. Before computing the similarity score, the contextualized token embeddings are aggregated to obtain a single embedding using a pooling strategy. Optionally, the pooled embeddings can be projected using a linear layer.

Parameters:

  • query_length (int) – Maximum query length. Defaults to 32.

  • doc_length (int) – Maximum document length. Defaults to 512.

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

  • normalize (bool) – Whether to normalize the embeddings. Defaults to False.

  • sparsification (Literal["relu", "relu_log"] | None) – Sparsification function to apply. Defaults to None.

  • add_marker_tokens (bool) – Whether to add marker tokens to the input sequences. Defaults to False.

  • query_pooling_strategy (Literal["first", "mean", "max", "sum"]) – Pooling strategy for query embeddings. Defaults to “first”.

  • doc_pooling_strategy (Literal["first", "mean", "max", "sum"]) – Pooling strategy for document embeddings. Defaults to “first”.

  • embedding_dim (int | None) – Dimension of the final embeddings. If None, it will be set to the hidden size of the backbone model. Defaults to None.

  • projection (Literal["linear", "linear_no_bias"] | None) – Type of projection layer to apply on the pooled embeddings. If None, no projection is applied. Defaults to “linear”.

Methods

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

A DPR model encodes queries and documents separately.

Attributes

model_type

Model type for a DPR model.
backbone_model_type: str | None = None

Backbone model type for the configuration. Set by LightningIRModelClassFactory().

classmethod from_pretrained(pretrained_model_name_or_path: str | Path, *args, **kwargs) → LightningIRConfig

Loads the configuration from a pretrained model. Wraps the transformers.PretrainedConfig.from_pretrained

Parameters:

pretrained_model_name_or_path (str | Path) – Pretrained model name or path.

Returns:

Derived LightningIRConfig class.

Return type:

LightningIRConfig

Raises:

ValueError – If pretrained_model_name_or_path is not a Lightning IR model and no LightningIRConfig is passed.

get_tokenizer_kwargs(Tokenizer: Type[LightningIRTokenizer]) → Dict[str, Any]

Returns the keyword arguments for the tokenizer. This method is used to pass the configuration parameters to the tokenizer.

Parameters:

Tokenizer (Type[LightningIRTokenizer]) – Class of the tokenizer to be used.

Returns:

Keyword arguments for the tokenizer.

Return type:

Dict[str, Any]

model_type: str = 'lir-dpr'

Model type for a DPR model.

to_dict() → Dict[str, Any]

Overrides the transformers.PretrainedConfig.to_dict method to include the added arguments and the backbone model type.

Returns:

Configuration dictionary.

Return type:

Dict[str, Any]

to_diff_dict() → dict[str, Any]

Removes all attributes from the configuration that correspond to the default config attributes for better readability, while always retaining the config attribute from the class. Serializes to a Python dictionary.

Returns:

Dictionary of all the attributes that make up this configuration instance.

Return type:

dict[str, Any]