AdamW

as donto believed on

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)

AdamW has 23 facts recorded in Dontopedia across 10 references, with 3 live disagreements.

23 facts·15 predicates·10 sources·3 in dispute

Mostly:rdf:type(5), action kills(2), constructor takes(2)

Inbound mentions (13)

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.

`rdf:type`Rdf:type(3)

Optimizerex:optimizer
Optimizerex:optimizer
Optimizerex:optimizer

`usesOptimizer`Uses Optimizer(3)

Current Configex:current-config
Optimizer Configex:optimizer-config
Trainingex:training

`adjustedBy`Adjusted by(1)

Learning Rateex:learning_rate

`exports`Exports(1)

Transformersex:transformers

`hasKeyDistinctionFrom`Has Key Distinction From(1)

Rotational Adam Wex:RotationalAdamW

`hasType`Has Type(1)

Optimizerex:optimizer

`imports`Imports(1)

Transformers Importex:transformers-import

`isInstance`Is Instance(1)

Adamw Optimizerex:adamw-optimizer

`isInstanceof`Is Instanceof(1)

Optimizer Adamwex:optimizer_adamw

Other facts (21)

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.

21 facts

Predicate	Value	Ref
Rdf:type	Optimizer Algorithm	[3]
Rdf:type	Python Optimizer	[5]
Rdf:type	Optimizer Type	[7]
Rdf:type	Optimizer Algorithm	[8]
Rdf:type	Gradient Descent Optimizer	[10]
Action Kills	Phase Dynamics	[4]
Action Kills	Coupling	[4]
Constructor Takes	Model Parameters	[9]
Constructor Takes	learningRate	[9]
Module Origin	Torch.optim	[1]
Learning Rate	0.00001	[1]
Is Optimizer	Torch Optimizer	[2]
Applies Weight Decay on	Sphere	[4]
Shrinks	Norms	[4]
Destroys	Synchronization	[4]
Module	transformers.optimization	[6]
Sub Class of	Optimizer	[6]
Used for	Optimizer	[7]
Adjusts	Learning Rate	[7]
Full Form	AdamW optimizer	[7]
Imported From	Transformers	[8]

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

—

moduleOriginbeam/ab8baaaa-135d-4a15-8914-a9becb6bfdcd

ex:torch.optim

—

learningRatebeam/ab8baaaa-135d-4a15-8914-a9becb6bfdcd

0.00001

—

isOptimizerbeam/51a366c4-36ad-4c73-a8a6-a8071a33c62a

ex:torch-optimizer

—

typebeam/4b8ea4b0-f383-42eb-81ec-520f3a41cb29

ex:OptimizerAlgorithm

—

labelblah/watt-activation/212

AdamW

—

appliesWeightDecayOnblah/watt-activation/212

ex:sphere

—

actionKillsblah/watt-activation/212

ex:phase-dynamics

—

shrinksblah/watt-activation/212

ex:norms

—

actionKillsblah/watt-activation/212

ex:coupling

—

destroysblah/watt-activation/212

ex:synchronization

—

typebeam/c3f449b6-692f-4686-9fd2-1ddb94bd4d4d

ex:PythonOptimizer

—

labelbeam/5a00c51f-dd1e-428b-b79b-370b9163f60f

AdamW

—

modulebeam/5a00c51f-dd1e-428b-b79b-370b9163f60f

transformers.optimization

—

subClassOfbeam/5a00c51f-dd1e-428b-b79b-370b9163f60f

ex:Optimizer

—

typebeam/de26bd5a-a2da-49d1-b64f-c8f7fe98d1f8

ex:OptimizerType

—

usedForbeam/de26bd5a-a2da-49d1-b64f-c8f7fe98d1f8

ex:optimizer

—

adjustsbeam/de26bd5a-a2da-49d1-b64f-c8f7fe98d1f8

ex:learning_rate

—

fullFormbeam/de26bd5a-a2da-49d1-b64f-c8f7fe98d1f8

AdamW optimizer

—

typebeam/864c2d75-2f47-4635-8d2e-4fe6efdd0312

ex:OptimizerAlgorithm

—

importedFrombeam/864c2d75-2f47-4635-8d2e-4fe6efdd0312

ex:transformers

—

constructorTakesbeam/e3f0a373-bd18-4169-94d6-399b3e607bf3

ex:model_parameters

—

constructorTakesbeam/e3f0a373-bd18-4169-94d6-399b3e607bf3

learningRate

—

typebeam/503d566f-4b98-4b5e-a567-8579fbcf1e30

ex:GradientDescentOptimizer

References (10)

10 references

[1]Ab8baaaa 135d 4a15 8914 A9becb6bfdcd2 facts
ctx:claims/beam/ab8baaaa-135d-4a15-8914-a9becb6bfdcd
- full textbeam-chunk
  text/plain1 KBdoc:beam/ab8baaaa-135d-4a15-8914-a9becb6bfdcd
  Show excerpt
  dataloader = DataLoader(dataset, batch_size=32) model_name = "bert-base-uncased" model = AutoModel.from_pretrained(model_name).to(device) optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5) train_model(model, o…
[2]51a366c4 36ad 4c73 A8a6 A8071a33c62a1 fact
ctx:claims/beam/51a366c4-36ad-4c73-a8a6-a8071a33c62a
- full textbeam-chunk
  text/plain1 KBdoc:beam/51a366c4-36ad-4c73-a8a6-a8071a33c62a
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  scaler.update() optimizer.zero_grad() # Example usage: train_model_with_amp(model, optimizer, dataloader, device, gradient_accumulation_steps=4) ``` 4. **Data Loading Efficiency:** - Use effici…
[3]4b8ea4b0 F383 42eb 81ec 520f3a41cb291 fact
ctx:claims/beam/4b8ea4b0-f383-42eb-81ec-520f3a41cb29
- full textbeam-chunk
  text/plain1 KBdoc:beam/4b8ea4b0-f383-42eb-81ec-520f3a41cb29
  Show excerpt
  optimizer = AdamW(model.parameters(), lr=1e-5) texts = ["This is an example sentence."] * 1000 # Example dataset dataset = TextDataset(texts, tokenizer) dataloader = DataLoader(dataset, batch_size=32, num_workers=4) train_model_with_amp(…
[4]2126 facts
ctx:discord/blah/watt-activation/212
- full textwatt-activation-212
  text/plain3 KBdoc:agent/watt-activation-212/6835fc9f-e8f3-4cfe-b6ab-3f16b5dbc7d2
  Show excerpt
  [2026-03-11 04:12] xenonfun: ``` ⏺ The sidecar data is very revealing! Let me respond to the designer message while the run finishes. --- On Omega's optimizer question: RotationalAdamW is exactly the geometry-aware rotation optimizer d…
[5]C3f449b6 692f 4686 9fd2 1ddb94bd4d4d1 fact
ctx:claims/beam/c3f449b6-692f-4686-9fd2-1ddb94bd4d4d
- full textbeam-chunk
  text/plain1 KBdoc:beam/c3f449b6-692f-4686-9fd2-1ddb94bd4d4d
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  Here's a complete example to get you started: ```python import torch from torch.utils.data import DataLoader, Dataset from transformers import AutoModelForSequenceClassification, AutoTokenizer, AdamW, get_linear_schedule_with_warmup # Loa…
[6]5a00c51f Dd1e 428b B79b 370b9163f60f3 facts
ctx:claims/beam/5a00c51f-dd1e-428b-b79b-370b9163f60f
[7]De26bd5a A2da 49d1 B64f C8f7fe98d1f84 facts
ctx:claims/beam/de26bd5a-a2da-49d1-b64f-c8f7fe98d1f8
- full textbeam-chunk
  text/plain1 KBdoc:beam/de26bd5a-a2da-49d1-b64f-c8f7fe98d1f8
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  outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels) loss = outputs.loss loss.backward() optimizer.step() scheduler.step() total_loss += loss.it…
[8]864c2d75 2f47 4635 8d2e 4fe6efdd03122 facts
ctx:claims/beam/864c2d75-2f47-4635-8d2e-4fe6efdd0312
- full textbeam-chunk
  text/plain1 KBdoc:beam/864c2d75-2f47-4635-8d2e-4fe6efdd0312
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  - **Margin**: Adjust the margin in contrastive loss functions to penalize incorrect predictions more heavily. ### 5. **Evaluation Metrics** - **Precision@k**: Monitor Precision@k metrics during training to ensure the model is improvi…
[9]E3f0a373 Bd18 4169 94d6 399b3e607bf32 facts
ctx:claims/beam/e3f0a373-bd18-4169-94d6-399b3e607bf3
- full textbeam-chunk
  text/plain1 KBdoc:beam/e3f0a373-bd18-4169-94d6-399b3e607bf3
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  dataset = DenseRetrievalDataset(queries, passages, tokenizer) data_loader = DataLoader(dataset, batch_size=32, shuffle=True) # Define optimizer and learning rate scheduler optimizer = AdamW(model.parameters(), lr=1e-5) scheduler = torch.op…
[10]503d566f 4b98 4b5e A567 8579fbcf1e301 fact
ctx:claims/beam/503d566f-4b98-4b5e-a567-8579fbcf1e30
- full textbeam-chunk
  text/plain1 KBdoc:beam/503d566f-4b98-4b5e-a567-8579fbcf1e30
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  truncation=True, return_attention_mask=True, return_tensors='pt' ) return { 'query': query_encoding, 'passage': passage_encoding } def __len__(self): …

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AdamW

Inbound mentions (13)

rdf:typeRdf:type(3)

usesOptimizerUses Optimizer(3)

adjustedByAdjusted by(1)

exportsExports(1)

hasKeyDistinctionFromHas Key Distinction From(1)

hasTypeHas Type(1)

importsImports(1)

isInstanceIs Instance(1)

isInstanceofIs Instanceof(1)

Other facts (21)

Timeline

References (10)

See also

Keep researching

`rdf:type`Rdf:type(3)

`usesOptimizer`Uses Optimizer(3)

`adjustedBy`Adjusted by(1)

`exports`Exports(1)

`hasKeyDistinctionFrom`Has Key Distinction From(1)

`hasType`Has Type(1)

`imports`Imports(1)

`isInstance`Is Instance(1)

`isInstanceof`Is Instanceof(1)