SetEncoderModel

class lightning_ir.models.cross_encoders.set_encoder.SetEncoderModel(config: SetEncoderConfig, *args, **kwargs)[source]

Bases: MonoModel

SetEncoder model. See SetEncoderConfig for configuration options.

__init__(config: SetEncoderConfig, *args, **kwargs)[source]

Initializes a SetEncoder model give a SetEncoderConfig.

Parameters:

config (SetEncoderConfig) – Configuration for the SetEncoder model.

Methods

__init__(config, *args, **kwargs)

Initializes a SetEncoder model give a SetEncoderConfig.

attention_forward(_self, self, ...)

Performs the attention forward pass for the SetEncoder model.

cat_other_doc_hidden_states(hidden_states, ...)

Concatenates the hidden states of other documents to the hidden states of the query and documents.

forward(encoding)

Computes contextualized embeddings for the joint query-document input sequence and computes a relevance score.

get_extended_attention_mask(attention_mask, ...)

Extends the attention mask to account for the number of documents per query.

Attributes

ALLOW_SUB_BATCHING

Flag to allow mini batches of documents for a single query.

self_attention_pattern

training

ALLOW_SUB_BATCHING = False

Flag to allow mini batches of documents for a single query. Set to false for listwise models to ensure correctness.

static attention_forward(_self, self: Module, hidden_states: Tensor, attention_mask: FloatTensor | None, *args, num_docs: Sequence[int], **kwargs) → Tuple[Tensor][source]

Performs the attention forward pass for the SetEncoder model.

Parameters:

  • _self (SetEncoderModel) – Reference to the SetEncoder instance.

  • self (torch.nn.Module) – Reference to the attention module.

  • hidden_states (torch.Tensor) – Hidden states from the previous layer.

  • attention_mask (torch.FloatTensor | None) – Attention mask for the input sequence.

  • num_docs (Sequence[int]) – Specifies how many documents are passed per query. If a sequence of integers, len(num_doc) should be equal to the number of queries and sum(num_docs) equal to the number of documents, i.e., the sequence contains one value per query specifying the number of documents for that query. If an integer, assumes an equal number of documents per query. If None, tries to infer the number of documents by dividing the number of documents by the number of queries.

Returns:

Contextualized embeddings.

Return type:

Tuple[torch.Tensor]

cat_other_doc_hidden_states(hidden_states: Tensor, num_docs: Sequence[int]) → Tensor[source]

Concatenates the hidden states of other documents to the hidden states of the query and documents.

Parameters:

  • hidden_states (torch.Tensor) – Hidden states of the query and documents.

  • num_docs (Sequence[int]) – Specifies how many documents are passed per query. If a sequence of integers, len(num_doc) should be equal to the number of queries and sum(num_docs) equal to the number of documents, i.e., the sequence contains one value per query specifying the number of documents for that query. If an integer, assumes an equal number of documents per query. If None, tries to infer the number of documents by dividing the number of documents by the number of queries.

Returns:

Concatenated hidden states of the query and documents.

Return type:

torch.Tensor

config_class

alias of SetEncoderConfig

forward(encoding: BatchEncoding) → CrossEncoderOutput[source]

Computes contextualized embeddings for the joint query-document input sequence and computes a relevance score.

Parameters:

encoding (BatchEncoding) – Tokenizer encoding for the joint query-document input sequence.

Returns:

Output of the model.

Return type:

CrossEncoderOutput

get_extended_attention_mask(attention_mask: Tensor, input_shape: Tuple[int, ...], device: device | None = None, dtype: dtype | None = None, num_docs: Sequence[int] | None = None) → Tensor[source]

Extends the attention mask to account for the number of documents per query.

Parameters:

  • attention_mask (torch.Tensor) – Attention mask for the input sequence.

  • input_shape (Tuple[int, ...]) – Shape of the input sequence.

  • device (torch.device | None) – Device to move the attention mask to. Defaults to None.

  • dtype (torch.dtype | None) – Data type of the attention mask. Defaults to None.

  • num_docs (Sequence[int] | None) – Specifies how many documents are passed per query. If a sequence of integers, len(num_doc) should be equal to the number of queries and sum(num_docs) equal to the number of documents, i.e., the sequence contains one value per query specifying the number of documents for that query. If an integer, assumes an equal number of documents per query. If None, tries to infer the number of documents by dividing the number of documents by the number of queries. Defaults to None.

  • Returns – torch.Tensor: Extended attention mask.