load_dataset
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
load_dataset has 11 facts recorded in Dontopedia across 5 references, with 2 live disagreements.
Mostly:rdf:type(4), uses function(2), parameter(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (8)
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.
calledFunctionCalled Function(1)
- Dataset
ex:dataset
hasStepHas Step(1)
- Code Execution
ex:code-execution
initializedByInitialized by(1)
- Dataset
ex:dataset
performsActionPerforms Action(1)
- Example Code
ex:example-code
precededByPreceded by(1)
- Apply Function
ex:apply-function
providesClassesProvides Classes(1)
- Datasets Library
ex:datasets-library
providesFunctionProvides Function(1)
- Datasets Library
ex:datasets-library
step1Step1(1)
- Seven Steps
ex:seven-steps
Other facts (9)
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 |
|---|---|---|
| Rdf:type | Python Function | [1] |
| Rdf:type | Function | [2] |
| Rdf:type | Operation | [3] |
| Rdf:type | Python Function | [4] |
| Uses Function | pd.read_csv | [3] |
| Uses Function | Pd Read Csv | [5] |
| Parameter | path_to_your_dataset | [1] |
| Preceded by | Import Statements | [3] |
| Imported From | Datasets Library | [4] |
Timeline
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References (5)
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/529ed2d2-aaf0-4ebb-a482-7fd789500505- full textbeam-chunktext/plain1 KB
doc:beam/529ed2d2-aaf0-4ebb-a482-7fd789500505Show excerpt
- Utilize efficient libraries and frameworks that are optimized for CPU usage, such as TensorFlow or PyTorch. ### Example Implementation Here's an example of how you can fine-tune Llama 2 13B on a CPU with these strategies: #### 1. Lo…
ctx:claims/beam/3b6a0db6-5dd7-4045-ac38-4822bbb3fa4cctx:claims/beam/04edfc72-1f93-4ce7-b6df-887c9a5f1db3- full textbeam-chunktext/plain1 KB
doc:beam/04edfc72-1f93-4ce7-b6df-887c9a5f1db3Show excerpt
from transformers import ( AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments, DataCollatorWithPadding, ) from datasets import load_dataset, DatasetDict # Load the model and tokenizer model_na…
ctx:claims/beam/c9e2838c-b8a4-4591-969b-ee77610720de- full textbeam-chunktext/plain1 KB
doc:beam/c9e2838c-b8a4-4591-969b-ee77610720deShow excerpt
1. **Hyperparameter Search**: Use grid search or random search to find the best hyperparameters. 2. **Learning Rate Scheduling**: Use learning rate schedulers like `ReduceLROnPlateau` or `CosineAnnealingLR`. ### 4. Ensemble Methods 1. **E…
See also
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