Dontopedia

Load and Split

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)

Load and Split has 5 facts recorded in Dontopedia across 2 references.

5 facts·5 predicates·2 sources

Mostly:rdf:type(1), precedes(1), is step number(1)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (3)

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containsStepContains Step(2)

consistsOfConsists of(1)

Other facts (5)

The 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.

5 facts
PredicateValueRef
Rdf:typeData Preparation Step[1]
PrecedesTokenize Data[2]
Is Step Number1[2]
EnablesTokenize Data[2]
EnsuresData Separation[2]

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.

typebeam/ba4ebe5f-d07c-449d-a419-da14a14caa93
ex:DataPreparationStep
precedesbeam/0e4dede6-52a5-49ce-a450-4813d1738359
ex:tokenize-data
isStepNumberbeam/0e4dede6-52a5-49ce-a450-4813d1738359
1
enablesbeam/0e4dede6-52a5-49ce-a450-4813d1738359
ex:tokenize-data
ensuresbeam/0e4dede6-52a5-49ce-a450-4813d1738359
ex:data-separation

References (2)

2 references
  1. ctx:claims/beam/ba4ebe5f-d07c-449d-a419-da14a14caa93
    • full textbeam-chunk
      text/plain1 KBdoc:beam/ba4ebe5f-d07c-449d-a419-da14a14caa93
      Show excerpt
      from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score # Load dataset and split into training and testing sets X_train, X_test, y_train, y_test =
  2. ctx:claims/beam/0e4dede6-52a5-49ce-a450-4813d1738359
    • full textbeam-chunk
      text/plain990 Bdoc:beam/0e4dede6-52a5-49ce-a450-4813d1738359
      Show excerpt
      - Load and split the dataset into training and testing sets. - Tokenize the data using the tokenizer. 2. **Model Fine-Tuning**: - Define a custom dataset class to handle the tokenized data. - Set up training arguments and defin

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