TrainingArguments

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

TrainingArguments is Defined training arguments for the Trainer to control the training process.

67 facts·28 predicates·10 sources·8 in dispute

Mostly:has parameter(14), rdf:type(10), has parameter(6)

Has Parameterin dispute`has-parameter`

Output Dir^[3]sourceall time · 9500e1c6 Ed0c 41a2 Ace0 794604c62109
Num Train Epochs^[3]sourceall time · 9500e1c6 Ed0c 41a2 Ace0 794604c62109
Per Device Train Batch Size^[3]sourceall time · 9500e1c6 Ed0c 41a2 Ace0 794604c62109
Per Device Eval Batch Size^[3]sourceall time · 9500e1c6 Ed0c 41a2 Ace0 794604c62109
Warmup Steps^[3]sourceall time · 9500e1c6 Ed0c 41a2 Ace0 794604c62109
Weight Decay^[3]sourceall time · 9500e1c6 Ed0c 41a2 Ace0 794604c62109
Logging Dir^[3]sourceall time · 9500e1c6 Ed0c 41a2 Ace0 794604c62109
Logging Steps^[3]sourceall time · 9500e1c6 Ed0c 41a2 Ace0 794604c62109
Evaluation Strategy^[3]sourceall time · 9500e1c6 Ed0c 41a2 Ace0 794604c62109
Eval Steps^[3]sourceall time · 9500e1c6 Ed0c 41a2 Ace0 794604c62109

Rdf:typein dispute`rdf:type`

Python Variable^[1]all time · 69dd1448 7a7c 4adf 8f03 7a001d9bfd87
Training Arguments^[2]all time · D63b152b 34b0 4323 Aea7 F9df40b773a8
Configuration Parameters^[3]all time · 9500e1c6 Ed0c 41a2 Ace0 794604c62109
Configuration^[4]all time · 6e640b7d Dae6 4bd7 Ab64 9938ce4c792d
Configuration Object^[5]all time · B4e1fa92 87bc 4489 Ba1e 895a84d083b0
Configuration^[6]all time · 2155073f 6f86 4661 A2c4 49d7e078edee
Training Arguments^[7]sourceall time · 018e6829 A4ce 4a26 9be8 6d8ad3231779
Python Class^[8]all time · 04edfc72 1f93 4ce7 B6df 887c9a5f1db3
Configuration Object^[9]all time · 8f504244 E3b7 477b Ba46 Cb8bb984f219
Configuration Parameters^[10]all time · 0e4dede6 52a5 49ce A450 4813d1738359

Inbound mentions (16)

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.

`hasPart`Has Part(2)

Code Exampleex:code-example
Multi Language Tokenization Modelex:multi-language-tokenization-model

`uses`Uses(2)

Trainerex:trainer
Trainerex:trainer

`calledClass`Called Class(1)

Training Argsex:training_args

`configuredBy`Configured by(1)

Trainer Objectex:trainer-object

`createdFrom`Created From(1)

Training Args Instanceex:training-args-instance

`hasMember`Has Member(1)

Steps Listex:steps-list

`hasParameter`Has Parameter(1)

Trainerex:trainer

`initializedBy`Initialized by(1)

Training Argsex:training_args

`inversePrecedes`Inverse Precedes(1)

Pytorch Datasetex:pytorch-dataset

`is-configured-by`Is Configured by(1)

Trainer Classex:trainer-class

`precedes`Precedes(1)

Pytorch Datasetex:pytorch-dataset

`requires`Requires(1)

Trainerex:trainer

`takesArgument`Takes Argument(1)

Trainer Objectex:trainer-object

`uses-configuration`Uses Configuration(1)

Model Training Processex:model-training-process

Other facts (39)

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.

39 facts

Predicate	Value	Ref
Has Parameter	Num Train Epochs	[9]
Has Parameter	Per Device Train Batch Size	[9]
Has Parameter	Per Device Eval Batch Size	[9]
Has Parameter	Warmup Steps	[9]
Has Parameter	Weight Decay	[9]
Has Parameter	Logging Steps	[9]
Controls Training Behavior	Num Train Epochs	[3]
Controls Training Behavior	Per Device Train Batch Size	[3]
Controls Training Behavior	Warmup Steps	[3]
Controls Training Behavior	Weight Decay	[3]
Controls Training Behavior	Gradient Accumulation Steps	[3]
Configures	Trainer Class	[3]
Configures	Trainer	[7]
Configures	Trainer Object	[9]
Imported From	Transformers	[2]
Imported From	Transformers Library	[8]
Controls Saving	Save Steps	[3]
Controls Saving	Save Total Limit	[3]
Assigned Value	Training Arguments Definition	[1]
Controls Monitoring	Logging Steps	[3]
Controls Evaluation	Eval Steps	[3]
Controls Precision	Fp16	[3]
Description	Defined training arguments for the Trainer to control the training process	[4]
Precedes	Trainer	[4]
Used by	Trainer	[4]
Inverse Precedes	Trainer	[4]
Has Output Directory	./results	[5]
Has Number of Epochs	5	[5]
Has Per Device Train Batch Size	16	[5]
Has Per Device Eval Batch Size	16	[5]
Has Warmup Steps	500	[5]
Has Weight Decay	0.01	[5]
Has Logging Directory	./logs	[5]
Used for	Trainer	[6]
Used in	Step4	[6]
Is Used by	Trainer	[7]
Part of	Code Example	[9]
Are Defined for	Trainer	[10]
Configure	Trainer Behavior	[10]

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.

67 undated

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typebeam/69dd1448-7a7c-4adf-8f03-7a001d9bfd87

ex:PythonVariable

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assignedValuebeam/69dd1448-7a7c-4adf-8f03-7a001d9bfd87

ex:training-arguments-definition

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typebeam/d63b152b-34b0-4323-aea7-f9df40b773a8

ex:TrainingArguments

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importedFrombeam/d63b152b-34b0-4323-aea7-f9df40b773a8

ex:transformers

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labelbeam/d63b152b-34b0-4323-aea7-f9df40b773a8

TrainingArguments

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typebeam/9500e1c6-ed0c-41a2-ace0-794604c62109

ex:configuration-parameters

—