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Val Dataset

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)

Val Dataset has 19 facts recorded in Dontopedia across 4 references, with 3 live disagreements.

19 facts·12 predicates·4 sources·3 in dispute

Mostly:contains(6), uses(2), pairs(2)

Maturity scale raw canonical shape-checked rule-derived certified

Containsin disputecontains

Usesin disputeuses

Pairsin disputepairs

  • Val Inputs[4]sourceall time · 56ec773d 331c 4612 B327 318a1a96426f
  • Val Labels[4]sourceall time · 56ec773d 331c 4612 B327 318a1a96426f

Inverse ofinverseOf

  • Val Loader[3]sourceall time · 23009db1 C526 4b01 963c B2c7b2736c5b

Is Paired WithisPairedWith

  • Val Labels[3]sourceall time · 23009db1 C526 4b01 963c B2c7b2736c5b

Rdf:typerdf:type

  • Dataset[3]all time · 23009db1 C526 4b01 963c B2c7b2736c5b

Is Created UsingisCreatedUsing

Used byused_by

  • Val Loader[2]sourceall time · 212294fd 6444 48ea 90be 0ccd48cb9cc3

Is Created AsisCreatedAs

Created bycreatedBy

Is VariableisVariable

Holdsholds

  • 600[4]all time · 56ec773d 331c 4612 B327 318a1a96426f

Inbound mentions (3)

Other 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.

inverseOfInverse of(1)

usesUses(1)

usesDatasetUses Dataset(1)

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.

19 undated
—
containsbeam/9344edde-d6af-464f-9e96-394ef09895b9
ex:val_combined_inputs
—
containsbeam/212294fd-6444-48ea-90be-0ccd48cb9cc3
ex:val_combined_inputs
—
containsbeam/23009db1-c526-4b01-963c-b2c7b2736c5b
ex:val_combined_inputs
—
containsbeam/23009db1-c526-4b01-963c-b2c7b2736c5b
ex:val_labels
—
containsbeam/9344edde-d6af-464f-9e96-394ef09895b9
ex:val_labels
—
containsbeam/212294fd-6444-48ea-90be-0ccd48cb9cc3
ex:val_labels
—
createdBybeam/9344edde-d6af-464f-9e96-394ef09895b9
ex:TensorDataset
—
holdsbeam/56ec773d-331c-4612-b327-318a1a96426f
600
—
inverseOfbeam/23009db1-c526-4b01-963c-b2c7b2736c5b
ex:val_loader
—
isCreatedAsbeam/212294fd-6444-48ea-90be-0ccd48cb9cc3
ex:TensorDataset
—
isCreatedUsingbeam/23009db1-c526-4b01-963c-b2c7b2736c5b
ex:TensorDataset
—
isPairedWithbeam/23009db1-c526-4b01-963c-b2c7b2736c5b
ex:val_labels
—
isVariablebeam/9344edde-d6af-464f-9e96-394ef09895b9
ex:TensorDataset
—
pairsbeam/56ec773d-331c-4612-b327-318a1a96426f
ex:val-inputs
—
pairsbeam/56ec773d-331c-4612-b327-318a1a96426f
ex:val-labels
—
typebeam/23009db1-c526-4b01-963c-b2c7b2736c5b
ex:Dataset
—
used_bybeam/212294fd-6444-48ea-90be-0ccd48cb9cc3
ex:val_loader
—
usesbeam/9344edde-d6af-464f-9e96-394ef09895b9
ex:val_combined_inputs
—
usesbeam/9344edde-d6af-464f-9e96-394ef09895b9
ex:val_labels

References (4)

4 references
  1. [1]beam-chunk6 facts
    customctx:claims/beam/9344edde-d6af-464f-9e96-394ef09895b9
    • full textbeam-chunk
      text/plain1 KBdoc:beam/9344edde-d6af-464f-9e96-394ef09895b9
      Show excerpt
      # Concatenate existing inputs with user behavior data combined_inputs = torch.cat([inputs, user_behavior], dim=1) # Split data into training and validation sets train_size = int(0.8 * len(combined_inputs)) val_size = len(combined_inputs) -…
  2. [2]beam-chunk4 facts
    customctx:claims/beam/212294fd-6444-48ea-90be-0ccd48cb9cc3
    • full textbeam-chunk
      text/plain1 KBdoc:beam/212294fd-6444-48ea-90be-0ccd48cb9cc3
      Show excerpt
      combined_inputs = torch.cat([inputs, user_behavior], dim=1) # Split data into training and validation sets train_size = int(0.8 * len(combined_inputs)) val_size = len(combined_inputs) - train_size train_combined_inputs, val_combined_input…
  3. [3]beam-chunk6 facts
    customctx:claims/beam/23009db1-c526-4b01-963c-b2c7b2736c5b
    • full textbeam-chunk
      text/plain1 KBdoc:beam/23009db1-c526-4b01-963c-b2c7b2736c5b
      Show excerpt
      combined_inputs = torch.cat([inputs, combined_user_behavior], dim=1) # Split data into training and validation sets train_size = int(0.8 * len(combined_inputs)) val_size = len(combined_inputs) - train_size train_combined_inputs, val_combi…
  4. [4]beam-chunk3 facts
    customctx:claims/beam/56ec773d-331c-4612-b327-318a1a96426f
    • full textbeam-chunk
      text/plain1 KBdoc:beam/56ec773d-331c-4612-b327-318a1a96426f
      Show excerpt
      ```python import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset # Example data preparation inputs = torch.randn(3000, 128) # Example input data labels = torch.randn(3000, 1) …

See also

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