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Map Function has 11 facts recorded in Dontopedia across 4 references, with 2 live disagreements.
Mostly:rdf:type(2), applied to(2), applies(1)
distributesdistributesTofunctionalityrdfs:labelpartOfusedForOther 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.
areDistributedByAre Distributed by(1)ex:chunks-of-datadistributedByDistributed by(1)ex:chunks-of-datamentionsMentions(1)ex:source-textTimeline 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.
doc:beam/e04766e0-b70f-4cd4-93df-3375bb36ef45results.extend(batch_results.cpu().numpy()) return results # Parallel processing def parallel_infer(texts, num_workers=4): with ThreadPoolExecutor(max_workers=num_workers) as executor: results = list(executor.map(in…
doc:beam/8d50017f-9c68-4c07-a447-752626bebf19- The `map` function distributes the chunks of data to the worker processes, which process them in parallel. - The results are combined using `np.concatenate`. By applying these strategies, you can significantly improve the performan…
doc:beam/7ba60581-efb1-48dc-ae4e-5da742180b42queries = ["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…
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