rerank_results
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)
rerank_results has 24 facts recorded in Dontopedia across 3 references, with 3 live disagreements.
Mostly:has parameter(4), produces(2), consumes(2)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (5)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
containsContains(2)
- Code Section
ex:code-section - Function Scope
ex:function-scope
includesIncludes(1)
- Module Level Functions
ex:module-level-functions
producedByProduced by(1)
- Reranked Results
ex:reranked-results
usableByUsable by(1)
- Model Instance
ex:model-instance
Other facts (22)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Has Parameter | model | [1] |
| Has Parameter | results | [1] |
| Has Parameter | Model | [3] |
| Has Parameter | Results | [3] |
| Produces | Reranked Results | [1] |
| Produces | Ranked Order | [3] |
| Consumes | Model | [1] |
| Consumes | Results | [1] |
| Rdf:type | Function | [2] |
| Rdf:type | Function | [3] |
| Is Function | true | [1] |
| Uses Context | No Grad | [1] |
| Computes | Scores | [1] |
| Sorts | Indices | [1] |
| Execution Order | 4 | [1] |
| Calls | Preprocess Input | [1] |
| Purpose | Result Sorting | [1] |
| Optimizes | Computational Efficiency | [1] |
| Returns | Sorted Indices | [3] |
| Uses Context Manager | Torch No Grad | [3] |
| Defined in | Main Module | [3] |
| Parameter | Results | [3] |
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.
References (3)
ctx:claims/beam/b729dc6d-53ff-42db-95a2-0b4b64111a65- full textbeam-chunktext/plain1 KB
doc:beam/b729dc6d-53ff-42db-95a2-0b4b64111a65Show excerpt
self.fc3 = nn.Linear(32, 1) self.dropout = nn.Dropout(0.5) def forward(self, x): x = torch.relu(self.fc1(x)) x = self.dropout(x) x = torch.relu(self.fc2(x)) x = self.dropout(x) x …
ctx:claims/beam/6fee7420-d7a9-4f8e-bc28-9cd1591ad95d- full textbeam-chunktext/plain1 KB
doc:beam/6fee7420-d7a9-4f8e-bc28-9cd1591ad95dShow excerpt
avg_val_loss = total_val_loss / len(val_loader) print(f"Validation Loss: {avg_val_loss:.4f}") return model ``` ### Example Usage Here's how you can use the above components to integrate your reranking logi…
ctx:claims/beam/fa097ab4-7c54-4d7c-bce6-50883cbc7667
See also
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