Dataset Splitting
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
Dataset Splitting has 10 facts recorded in Dontopedia across 3 references, with 2 live disagreements.
Mostly:produces(2), splits into(2), has description(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (4)
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.
demonstratesDemonstrates(1)
- Code Example 1
ex:code-example-1
followsFollows(1)
- Model Fine Tuning
ex:model-fine-tuning
phasePhase(1)
- ML Pipeline
ex:ml-pipeline
usedByUsed by(1)
- Datasets Library
ex:datasets-library
Other facts (10)
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.
| Predicate | Value | Ref |
|---|---|---|
| Produces | training-set | [2] |
| Produces | validation-set | [2] |
| Splits Into | Training Data | [3] |
| Splits Into | Test Data | [3] |
| Has Description | Splitting your dataset into training, validation, and test sets | [1] |
| Common Ratio | 80% training, 10% validation, and 10% test | [1] |
| Precedes | Model Fine Tuning | [1] |
| Requires | Datasets Library | [1] |
| Uses Function | Train Test Split | [3] |
| Sets Test Size | 0.2 | [3] |
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.
References (3)
ctx:claims/beam/69dd1448-7a7c-4adf-8f03-7a001d9bfd87- full textbeam-chunktext/plain1 KB
doc:beam/69dd1448-7a7c-4adf-8f03-7a001d9bfd87Show excerpt
- **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_…
ctx:claims/beam/f0656b10-4efe-4bd0-9005-6e894f93f6b4- full textbeam-chunktext/plain1 KB
doc:beam/f0656b10-4efe-4bd0-9005-6e894f93f6b4Show excerpt
train_dataset=train_dataset, eval_dataset=eval_dataset, tokenizer=tokenizer, data_collator=DataCollatorWithPadding(tokenizer), ) # Fine-tune the model trainer.train() # Define the feedback analysis logic def analyze_feedba…
ctx:claims/beam/34a1dce2-ecc2-4241-ad4a-235e8625b612- full textbeam-chunktext/plain1 KB
doc:beam/34a1dce2-ecc2-4241-ad4a-235e8625b612Show excerpt
retrieved_documents = rag_system.process_query(reformulated_query, context) return reformulated_query, retrieved_documents # Apply the function to each row df[['reformulated_query', 'retrieved_documents']] = df.apply( lambda ro…
See also
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