Load Dataset

as donto believed on

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

Load Dataset has 6 facts recorded in Dontopedia across 4 references, with 2 live disagreements.

6 facts·3 predicates·4 sources·2 in dispute

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.

`describesAction`Describes Action(1)

Step 1ex:step_1

`hasSubAction`Has Sub Action(1)

Step 1ex:step_1

`usesFunction`Uses Function(1)

Dataset Loadingex:dataset-loading

Other facts (6)

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.

6 facts

Predicate	Value	Ref
Rdf:type	Hugging Face Function	[1]
Rdf:type	Dataset Loading Function	[3]
Rdf:type	Data Operation	[4]
Has Parameter	csv	[1]
Has Parameter	Data Files Param	[1]
Function of	datasets_library	[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.

6 undated

—

typebeam/b4e1fa92-87bc-4489-ba1e-895a84d083b0

ex:HuggingFaceFunction

—

hasParameterbeam/b4e1fa92-87bc-4489-ba1e-895a84d083b0

csv

—

hasParameterbeam/b4e1fa92-87bc-4489-ba1e-895a84d083b0

ex:data-files-param

—

functionOfbeam/6c3b0310-9572-42f3-a33f-3f41bc304470

datasets_library

—

typebeam/a287a209-7227-4d35-88d1-e63467e5486c

ex:DatasetLoadingFunction

—

typebeam/e90baac4-24b6-4abb-89e2-a81f7d246e29

ex:DataOperation

References (4)

4 references

[1]B4e1fa92 87bc 4489 Ba1e 895a84d083b03 facts
ctx:claims/beam/b4e1fa92-87bc-4489-ba1e-895a84d083b0
- full textbeam-chunk
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  6. **Ensemble Methods**: Combine multiple models to improve overall accuracy. ### Enhanced Code Example Here's an enhanced version of your code that incorporates these strategies: ```python import torch from transformers import AutoModel…
[2]6c3b0310 9572 42f3 A33f 3f41bc3044701 fact
ctx:claims/beam/6c3b0310-9572-42f3-a33f-3f41bc304470
- full textbeam-chunk
  text/plain1 KBdoc:beam/6c3b0310-9572-42f3-a33f-3f41bc304470
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  logging_steps=10, evaluation_strategy='epoch', save_total_limit=2, ) # Define the trainer trainer = Trainer( model=model, args=training_args, train_dataset=dataset['train'], eval_dataset=dataset['test'], dat…
[3]A287a209 7227 4d35 88d1 E63467e5486c1 fact
ctx:claims/beam/a287a209-7227-4d35-88d1-e63467e5486c
- full textbeam-chunk
  text/plain1 KBdoc:beam/a287a209-7227-4d35-88d1-e63467e5486c
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  Here's the complete example: ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments from datasets import load_dataset import torch # Load your dataset dataset = load_dataset("your_…
[4]E90baac4 24b6 4abb 89e2 A81f7d246e291 fact
ctx:claims/beam/e90baac4-24b6-4abb-89e2-a81f7d246e29
- full textbeam-chunk
  text/plain1 KBdoc:beam/e90baac4-24b6-4abb-89e2-a81f7d246e29
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  accuracy = accuracy_score(test_df['label'], predicted_labels) print(f"Accuracy for {model_name}: {accuracy:.2f}") return accuracy # List of models to experiment with models_to_test = [ "bert-base-uncased", "roberta-bas…

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Load Dataset

Inbound mentions (3)

describesActionDescribes Action(1)

hasSubActionHas Sub Action(1)

usesFunctionUses Function(1)