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Tokenized Datasets has 22 facts recorded in Dontopedia across 3 references, with 3 live disagreements.
Mostly:has key(4), rdf:type(3), rdfs:label(2)
rdfs:labelresultOfMapOperationusedAsremovesColumnsusesBatchedcreatedByMapstructureaccessMethodusesSyntaxOther 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.
inverseHasPartInverse Has Part(3)ex:tokenized_datasets_testex:tokenized_datasets_trainex:tokenized_datasets_validationisPartOfIs Part of(3)ex:tokenized_datasets_testex:tokenized_datasets_trainex:tokenized_datasets_validationresultsInResults in(1)ex:tokenization-operationtrainedWithTrained With(1)ex:trainerThe 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 |
|---|---|---|
| Coordinates | Data Preparation Pipeline | [1] |
| Preprocessed by | Tokenizer | [1] |
| Results From | Tokenization Operation | [2] |
| Assigned Value | Tokenization Operation | [2] |
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.
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/69dd1448-7a7c-4adf-8f03-7a001d9bfd87- **Splitting**: Split your dataset into training, validation, and test sets. A common split ratio is 80% training, 10% validation, and 10% test. ```python from datasets import load_dataset, DatasetDict # Load your dataset dataset = load_…
doc:beam/2e15bda3-1327-4a52-84cc-730203563e58labels = tokenizer(examples['reformulated'], max_length=512, padding='max_length', truncation=True, return_tensors='pt')['input_ids'] model_inputs['labels'] = labels return model_inputs tokenized_datasets = dataset.map(preproce…
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