Dontopedia

Hybrid Retrieval Function

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Hybrid Retrieval Function has 12 facts recorded in Dontopedia across 1 reference, with 3 live disagreements.

12 facts·8 predicates·1 sources·3 in dispute

Mostly:has parameter(3), calls function(2), combines methods(2)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (2)

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demonstratesDemonstrates(1)

explainsExplains(1)

Other facts (12)

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12 facts
PredicateValueRef
Has Parameterquery[1]
Has Parameterdocuments[1]
Has Parameteralpha[1]
Calls FunctionBm25 Retrieval[1]
Calls FunctionDense Retrieval[1]
Combines MethodsSparse Retrieval[1]
Combines MethodsDense Retrieval[1]
Rdf:typeFunction[1]
Function Namehybrid_sparse_dense_retrieval[1]
Parameter Default0.5[1]
Computescombined-scores[1]
Returnscombined_scores[1]

Timeline

Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.

typebeam/07b00e3a-dd0e-40bb-a9be-bbdf1ac254da
ex:Function
functionNamebeam/07b00e3a-dd0e-40bb-a9be-bbdf1ac254da
hybrid_sparse_dense_retrieval
hasParameterbeam/07b00e3a-dd0e-40bb-a9be-bbdf1ac254da
query
hasParameterbeam/07b00e3a-dd0e-40bb-a9be-bbdf1ac254da
documents
hasParameterbeam/07b00e3a-dd0e-40bb-a9be-bbdf1ac254da
alpha
parameterDefaultbeam/07b00e3a-dd0e-40bb-a9be-bbdf1ac254da
0.5
callsFunctionbeam/07b00e3a-dd0e-40bb-a9be-bbdf1ac254da
ex:bm25-retrieval
callsFunctionbeam/07b00e3a-dd0e-40bb-a9be-bbdf1ac254da
ex:dense-retrieval
computesbeam/07b00e3a-dd0e-40bb-a9be-bbdf1ac254da
combined-scores
returnsbeam/07b00e3a-dd0e-40bb-a9be-bbdf1ac254da
combined_scores
combinesMethodsbeam/07b00e3a-dd0e-40bb-a9be-bbdf1ac254da
ex:sparse-retrieval
combinesMethodsbeam/07b00e3a-dd0e-40bb-a9be-bbdf1ac254da
ex:dense-retrieval

References (1)

1 references
  1. ctx:claims/beam/07b00e3a-dd0e-40bb-a9be-bbdf1ac254da
    • full textbeam-chunk
      text/plain1 KBdoc:beam/07b00e3a-dd0e-40bb-a9be-bbdf1ac254da
      Show excerpt
      with torch.no_grad(): doc_outputs = model(**doc_inputs) query_outputs = model(**query_inputs) doc_embeddings = doc_outputs.last_hidden_state.mean(dim=1) query_embedding = query_outputs.last_hidden_state.mean(dim

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