From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
Eval Results has 13 facts recorded in Dontopedia across 4 references, with 1 live disagreement.
Mostly:rdf:type(3), input to(1), rdfs:label(1)
isAssignedcontainsMetricrequiresderivedFromisPrintedisResultOfOther 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.
producesProduces(1)ex:trainerTimeline 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/6c3b0310-9572-42f3-a33f-3f41bc304470logging_steps=10, evaluation_strategy='epoch', save_total_limit=2, ) # Define the trainer trainer = Trainer( model=model, args=training_args, train_dataset=dataset['train'], eval_dataset=dataset['test'], dat…
doc:beam/09c69473-903c-475d-98c1-a87aeedbce93output_dir='./results', num_train_epochs=3, per_device_train_batch_size=8, per_device_eval_batch_size=8, warmup_steps=500, weight_decay=0.01, logging_dir='./logs', logging_steps=10, evaluation_strategy="s…
doc:beam/974a068f-3f5b-4b96-b53c-9e0c612e3beetest_encodings = tokenize_data(tokenizer, test_df['query']) # Create datasets train_dataset = QueryDataset(train_encodings, train_df['label'].tolist()) test_dataset = QueryDataset(test_encodings, test_df['label'].tolist()) …
doc:beam/befe5288-0889-4495-85bd-a24c2feddb5d# Define training arguments training_args = TrainingArguments( output_dir=f'./results/{model_name}', num_train_epochs=3, per_device_train_batch_size=16, per_device_eval_batch_size=16, warmup_s…
Dontopedia is in a read-only public launch. Follow the references and disputed branches now; contributions will open after durable identity and moderation are in place.