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Test Set has 11 facts recorded in Dontopedia across 5 references, with 3 live disagreements.
Mostly:rdf:type(4), rdfs:label(3), consists of(2)
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producesProduces(4)ex:dataset_loadingex:data_splitex:data_splittingex:train_test_splitmeasuredOnMeasured on(3)ex:accuracyex:final_accuracyex:final_f1usesUses(1)ex:model_evaluationusesDatasetUses Dataset(1)ex:model_evaluationusesDataSetUses Data Set(1)ex:model_re_evaluationTimeline 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/c0a643d3-be7b-4c8f-b794-2d7d40828ff1[Turn 7444] User: I'm running a proof of concept for multi-language tokenization, testing it on 8,000 queries, and I'm hitting 89% accuracy, but I want to improve this further, can you help me optimize the code for better performance? ```py…
doc:beam/c1ca0898-d814-4ebd-a786-a3e5f69b8141# Simulate collecting new feedback new_ratings = [ {'user_id': 1, 'item_id': 10, 'rating': 4}, {'user_id': 2, 'item_id': 11, 'rating': 3}, # Add more new ratings as needed ] return new_ratings # Coll…
doc:beam/2ab0a1fa-1edb-4fa9-bdf6-d24eb14c3996- Define a function `update_model_with_feedback` to update the model with new ratings. - Convert new ratings to the Surprise format and update the model using the `update` method. 5. **Collect New Feedback**: - Define a function `…
doc:beam/0956e934-046c-45ee-94d8-496a65473dfcy_pred_cv = model.predict(X_val_cv) scores.append(accuracy_score(y_val_cv, y_pred_cv)) print(f"Cross-validation scores: {scores}") print(f"Mean CV score: {np.mean(scores):.4f}") ``` ### Explanation 1. **Data Splitting**: Split th…
doc:beam/575c6f15-a6fa-439f-9d3d-ef28e0854e79best_score = grid_search.best_score_ print(f"Best parameters: {best_params}") print(f"Best cross-validation accuracy: {best_score:.4f}") # Re-fit with best parameters pipeline.set_params(**best_params) pipeline.fit(X_train, y_train) # Fi…
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