Weight Decay

as donto believed on

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Weight Decay

Rdf:type	Hyperparameter^[4]
Rdf:type	Parameter^[3]
Affects	Model Generalization^[1]
Affects	Model Regularization^[2]
Has Value	0.01^[5]
Purpose	Regularization^[2]
Rdfs:label	weight_decay^[5]
Has Typical Range	0.01 to 0.1^[4]
Is Regularization Parameter	true^[6]
Set Value	0.01^[8]

ex:weight_decay

Weight Decay has 23 facts recorded in Dontopedia across 9 references, with 2 live disagreements.

23 facts·13 predicates·9 sources·2 in dispute

Mostly:rdf:type(5), affects(3), has value(3)

Rdf:typein dispute`rdf:type`

Hyperparameter^[4]all time · 974a068f 3f5b 4b96 B53c 9e0c612e3bee
Parameter^[3]all time · 61388ff0 B98e 4f4f B553 0328c71a6d05
Training Parameter^[8]sourceall time · 14cf4eab A053 4cf0 B374 9022e5e69c19
Training Parameter^[5]all time · 04edfc72 1f93 4ce7 B6df 887c9a5f1db3
Training Parameter^[9]all time · A287a209 7227 4d35 88d1 E63467e5486c

Affectsin dispute`affects`

Model Generalization^[1]all time · Aaa2ab69 D393 49d6 B565 40f47c0bccb9
Model Regularization^[2]all time · 09c69473 903c 475d 98c1 A87aeedbce93
Regularization^[3]all time · 61388ff0 B98e 4f4f B553 0328c71a6d05

Has Value`hasValue`

0.01^[5]sourceall time · 04edfc72 1f93 4ce7 B6df 887c9a5f1db3
0.01^[2]sourceall time · 09c69473 903c 475d 98c1 A87aeedbce93
0.01^[4]sourceall time · 974a068f 3f5b 4b96 B53c 9e0c612e3bee

Purpose`purpose`

Regularization^[2]all time · 09c69473 903c 475d 98c1 A87aeedbce93
Regularization^[8]all time · 14cf4eab A053 4cf0 B374 9022e5e69c19

Rdfs:label`rdfs:label`

weight_decay^[5]sourceall time · 04edfc72 1f93 4ce7 B6df 887c9a5f1db3
weight_decay^[3]all time · 61388ff0 B98e 4f4f B553 0328c71a6d05

Has Typical Range`hasTypicalRange`

0.01 to 0.1^[4]all time · 974a068f 3f5b 4b96 B53c 9e0c612e3bee

Is Regularization Parameter`isRegularizationParameter`

true^[6]all time · Befe5288 0889 4495 85bd A24c2feddb5d

Set Value`setValue`

0.01^[8]sourceall time · 14cf4eab A053 4cf0 B374 9022e5e69c19

Is Parameter of`isParameterOf`

Training Arguments^[3]all time · 61388ff0 B98e 4f4f B553 0328c71a6d05

Has Suggested Value`hasSuggestedValue`

0.01^[3]all time · 61388ff0 B98e 4f4f B553 0328c71a6d05

Prevents`prevents`

Overfitting^[7]all time · 2d4011b7 Fd19 414d 88f5 084c1fba93b1

Type`type`

regularization^[2]all time · 09c69473 903c 475d 98c1 A87aeedbce93

Inbound mentions (5)

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.

`hasParameter`Has Parameter(3)

Training Argsex:training_args
Training Argumentsex:training_arguments
Training Argumentsex:TrainingArguments

`calledWith`Called With(1)

Training Argumentsex:TrainingArguments

`usesParameter`Uses Parameter(1)

Example Configurationex:example_configuration

Other facts (1)

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.

1 facts

Predicate	Value	Ref
Inverse Affects	Model Generalization	[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.

23 undated

—

affectsbeam/aaa2ab69-d393-49d6-b565-40f47c0bccb9

ex:model_generalization

—

affectsbeam/09c69473-903c-475d-98c1-a87aeedbce93

ex:model_regularization

—

affectsbeam/61388ff0-b98e-4f4f-b553-0328c71a6d05

ex:regularization

—

hasSuggestedValuebeam/61388ff0-b98e-4f4f-b553-0328c71a6d05

0.01

—

hasTypicalRangebeam/974a068f-3f5b-4b96-b53c-9e0c612e3bee

ex:0.01_to_0.1

—

hasValuebeam/04edfc72-1f93-4ce7-b6df-887c9a5f1db3

0.01

—

hasValuebeam/09c69473-903c-475d-98c1-a87aeedbce93

0.01

—

hasValuebeam/974a068f-3f5b-4b96-b53c-9e0c612e3bee

0.01

—

inverseAffectsbeam/09c69473-903c-475d-98c1-a87aeedbce93

ex:model_generalization

—

isParameterOfbeam/61388ff0-b98e-4f4f-b553-0328c71a6d05

ex:TrainingArguments

—

isRegularizationParameterbeam/befe5288-0889-4495-85bd-a24c2feddb5d

true

—

preventsbeam/2d4011b7-fd19-414d-88f5-084c1fba93b1

ex:Overfitting

—

purposebeam/09c69473-903c-475d-98c1-a87aeedbce93

ex:regularization

—

purposebeam/14cf4eab-a053-4cf0-b374-9022e5e69c19

ex:regularization

—

labelbeam/04edfc72-1f93-4ce7-b6df-887c9a5f1db3

weight_decay

—

labelbeam/61388ff0-b98e-4f4f-b553-0328c71a6d05

weight_decay

—

typebeam/974a068f-3f5b-4b96-b53c-9e0c612e3bee

ex:Hyperparameter

—

typebeam/61388ff0-b98e-4f4f-b553-0328c71a6d05

ex:Parameter

—

typebeam/14cf4eab-a053-4cf0-b374-9022e5e69c19

ex:training_parameter

—

typebeam/04edfc72-1f93-4ce7-b6df-887c9a5f1db3

ex:TrainingParameter

—

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

ex:TrainingParameter

—

setValuebeam/14cf4eab-a053-4cf0-b374-9022e5e69c19

0.01

—

typebeam/09c69473-903c-475d-98c1-a87aeedbce93

regularization

References (9)

9 references

[1]beam-chunk1 fact
customctx:claims/beam/aaa2ab69-d393-49d6-b565-40f47c0bccb9
- full textbeam-chunk
  text/plain1 KBdoc:beam/aaa2ab69-d393-49d6-b565-40f47c0bccb9
  Show excerpt
  errors.append(doc) return errors errors = analyze_tokenization_errors(documents, tokenizer) print(f"Tokenization Errors: {errors}") # Fine-tune the model on your specific dataset # This involves preparing a labeled dataset…
[2]beam-chunk5 facts
customctx:claims/beam/09c69473-903c-475d-98c1-a87aeedbce93
- full textbeam-chunk
  text/plain1 KBdoc:beam/09c69473-903c-475d-98c1-a87aeedbce93
  Show excerpt
  output_dir='./results', num_train_epochs=3, per_device_train_batch_size=8, per_device_eval_batch_size=8, warmup_steps=500, weight_decay=0.01, logging_dir='./logs', logging_steps=10, evaluation_strategy="s…
[3]61388ff0 B98e 4f4f B553 0328c71a6d055 facts
customctx:claims/beam/61388ff0-b98e-4f4f-b553-0328c71a6d05
[4]beam-chunk3 facts
customctx:claims/beam/974a068f-3f5b-4b96-b53c-9e0c612e3bee
- full textbeam-chunk
  text/plain1 KBdoc:beam/974a068f-3f5b-4b96-b53c-9e0c612e3bee
  Show excerpt
  test_encodings = tokenize_data(tokenizer, test_df['query']) # Create datasets train_dataset = QueryDataset(train_encodings, train_df['label'].tolist()) test_dataset = QueryDataset(test_encodings, test_df['label'].tolist()) …
[5]beam-chunk3 facts
customctx:claims/beam/04edfc72-1f93-4ce7-b6df-887c9a5f1db3
- full textbeam-chunk
  text/plain1 KBdoc:beam/04edfc72-1f93-4ce7-b6df-887c9a5f1db3
  Show excerpt
  from transformers import ( AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments, DataCollatorWithPadding, ) from datasets import load_dataset, DatasetDict # Load the model and tokenizer model_na…
[6]beam-chunk1 fact
customctx:claims/beam/befe5288-0889-4495-85bd-a24c2feddb5d
- full textbeam-chunk
  text/plain1 KBdoc:beam/befe5288-0889-4495-85bd-a24c2feddb5d
  Show excerpt
  # Define training arguments training_args = TrainingArguments( output_dir=f'./results/{model_name}', num_train_epochs=3, per_device_train_batch_size=16, per_device_eval_batch_size=16, warmup_s…
[7]beam-chunk1 fact
customctx:claims/beam/2d4011b7-fd19-414d-88f5-084c1fba93b1
- full textbeam-chunk
  text/plain1 KBdoc:beam/2d4011b7-fd19-414d-88f5-084c1fba93b1
  Show excerpt
  training_args = TrainingArguments( output_dir='./results', num_train_epochs=3, per_device_train_batch_size=16, per_device_eval_batch_size=16, warmup_steps=500, weight_decay=0.01, logging_dir='./logs', logging…
[8]beam-chunk3 facts
customctx:claims/beam/14cf4eab-a053-4cf0-b374-9022e5e69c19
- full textbeam-chunk
  text/plain1 KBdoc:beam/14cf4eab-a053-4cf0-b374-9022e5e69c19
  Show excerpt
  model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(df['label'].unique())) tokenizer = AutoTokenizer.from_pretrained(model_name) # Tokenize the data train_encodings = tokenizer(train_df['query'].tolist(), …
[9]beam-chunk1 fact
customctx:claims/beam/a287a209-7227-4d35-88d1-e63467e5486c
- full textbeam-chunk
  text/plain1 KBdoc:beam/a287a209-7227-4d35-88d1-e63467e5486c
  Show excerpt
  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_…

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Weight Decay

Rdf:typein disputerdf:type

Affectsin disputeaffects

Has ValuehasValue

Purposepurpose

Rdfs:labelrdfs:label

Has Typical RangehasTypicalRange

Is Regularization ParameterisRegularizationParameter

Set ValuesetValue

Is Parameter ofisParameterOf

Has Suggested ValuehasSuggestedValue

Preventsprevents

Typetype

Inbound mentions (5)

hasParameterHas Parameter(3)

calledWithCalled With(1)

usesParameterUses Parameter(1)

Other facts (1)

Timeline

References (9)

See also

Keep researching

Rdf:typein dispute`rdf:type`

Affectsin dispute`affects`

Has Value`hasValue`

Purpose`purpose`

Rdfs:label`rdfs:label`

Has Typical Range`hasTypicalRange`

Is Regularization Parameter`isRegularizationParameter`

Set Value`setValue`

Is Parameter of`isParameterOf`

Has Suggested Value`hasSuggestedValue`

Prevents`prevents`

Type`type`

`hasParameter`Has Parameter(3)

`calledWith`Called With(1)

`usesParameter`Uses Parameter(1)