:.4f
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
:.4f has 11 facts recorded in Dontopedia across 5 references, with 3 live disagreements.
Mostly:rdf:type(3), used in(2), specifies(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedOther facts (9)
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 |
|---|---|---|
| Rdf:type | Format Specifier | [2] |
| Rdf:type | Format Specification | [3] |
| Rdf:type | Output Specification | [4] |
| Used in | Print Statement 1 | [2] |
| Used in | Print Statement 2 | [2] |
| Specifies | Two Decimal Precision | [1] |
| Specifies Precision | 4 | [3] |
| Controls | two decimal places | [4] |
| Applies to | Loss Value | [5] |
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 (5)
ctx:claims/beam/401284ac-4b49-4678-a3e2-aa44c5ceacbb- full textbeam-chunktext/plain1 KB
doc:beam/401284ac-4b49-4678-a3e2-aa44c5ceacbbShow excerpt
print(f"Adjusted nprobe search time: {end_time - start_time:.2f} seconds") ``` By systematically adjusting these parameters, you can find the optimal configuration that balances search speed and accuracy for your application. [Turn 1978] …
ctx:claims/beam/59323be7-0344-48af-a986-55126680111bctx:claims/beam/489950f5-8a6b-41bc-89ca-958506c8e179ctx:claims/beam/7ba60581-efb1-48dc-ae4e-5da742180b42- full textbeam-chunktext/plain1 KB
doc:beam/7ba60581-efb1-48dc-ae4e-5da742180b42Show excerpt
queries = ["example query"] * 6000 # Measure the latency of processing multiple queries in parallel start_time = time.time() results = process_queries(queries) end_time = time.time() latency = end_time - start_time print(f"Total latency fo…
ctx:claims/beam/d37ddcd2-e87b-45fe-94fd-23a99f3a695e- full textbeam-chunktext/plain1 KB
doc:beam/d37ddcd2-e87b-45fe-94fd-23a99f3a695eShow excerpt
# Calculate average loss for the epoch avg_loss = running_loss / len(data_loader) print(f'Epoch [{epoch + 1}/100], Loss: {avg_loss:.4f}, LR: {optimizer.param_groups[0]["lr"]}') # Step the scheduler s…
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