attention_mask

as donto believed on

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

attention_mask has 26 facts recorded in Dontopedia across 4 references, with 6 live disagreements.

26 facts·17 predicates·4 sources·6 in dispute

Mostly:rdf:type(3), created by(2), has shape(2)

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.

`contains`Contains(1)

Code Snippetex:code-snippet

`createsTensor`Creates Tensor(1)

Test Caseex:test-case

`derivedFrom`Derived From(1)

Optimized Attention Maskex:optimized-attention-mask

`hasArgument`Has Argument(1)

Handler Callex:handler-call

`is-used-to-create`Is Used to Create(1)

Torch Tensorex:torch-tensor

Other facts (24)

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.

24 facts

Predicate	Value	Ref
Rdf:type	Torch Tensor	[2]
Rdf:type	Py Torch Tensor	[3]
Rdf:type	Tensor	[4]
Created by	Torch Tensor	[2]
Created by	Torch Randint	[4]
Has Shape	2x3	[2]
Has Shape	Shape 2x3	[3]
Mask Value	0	[2]
Mask Value	1	[2]
Semantic Meaning	zero-indicates-padding	[2]
Semantic Meaning	one-indicates-active-token	[2]
Contains Element	Binary Zero	[3]
Contains Element	Binary One	[3]
Is a	Torch Tensor	[1]
Encodes	Token Validity Mask	[1]
Has Shape	[2,3]	[1]
Corresponds to	Input Ids Tensor	[1]
Has Value	[[0, 0, 1], [1, 0, 0]]	[2]
Used in	Handler Call	[2]
Tensor Value	[[0, 0, 1], [1, 0, 0]]	[3]
Has Element Type	Binary Type	[3]
Shape	Batch Size Sequence Length	[4]
Value Range	0 to 2	[4]
Created With	Torch Randint	[4]

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.

26 undated

—

is-abeam/537fbc2b-7909-4faa-acb8-7dc925078999

ex:torch-tensor

—

encodesbeam/537fbc2b-7909-4faa-acb8-7dc925078999

ex:token-validity-mask

—

has-shapebeam/537fbc2b-7909-4faa-acb8-7dc925078999

[2,3]

—

corresponds-tobeam/537fbc2b-7909-4faa-acb8-7dc925078999

ex:input-ids-tensor

—

typebeam/5e8a169a-b4c0-41ba-8477-6cb9d783868b

ex:TorchTensor

—

createdBybeam/5e8a169a-b4c0-41ba-8477-6cb9d783868b

ex:torch-tensor

—

hasValuebeam/5e8a169a-b4c0-41ba-8477-6cb9d783868b

[[0, 0, 1], [1, 0, 0]]

—

hasShapebeam/5e8a169a-b4c0-41ba-8477-6cb9d783868b

2x3

—

maskValuebeam/5e8a169a-b4c0-41ba-8477-6cb9d783868b

—

maskValuebeam/5e8a169a-b4c0-41ba-8477-6cb9d783868b

—

semanticMeaningbeam/5e8a169a-b4c0-41ba-8477-6cb9d783868b

zero-indicates-padding

—

semanticMeaningbeam/5e8a169a-b4c0-41ba-8477-6cb9d783868b

one-indicates-active-token

—

usedInbeam/5e8a169a-b4c0-41ba-8477-6cb9d783868b

ex:handler-call

—

typebeam/f1f8f635-6c4d-4009-a459-c40f4e5e49a5

ex:PyTorchTensor

—

labelbeam/f1f8f635-6c4d-4009-a459-c40f4e5e49a5

attention_mask

—

tensorValuebeam/f1f8f635-6c4d-4009-a459-c40f4e5e49a5

[[0, 0, 1], [1, 0, 0]]

—

hasShapebeam/f1f8f635-6c4d-4009-a459-c40f4e5e49a5

ex:shape-2x3

—

hasElementTypebeam/f1f8f635-6c4d-4009-a459-c40f4e5e49a5

ex:binary-type

—

containsElementbeam/f1f8f635-6c4d-4009-a459-c40f4e5e49a5

ex:binary-zero

—

containsElementbeam/f1f8f635-6c4d-4009-a459-c40f4e5e49a5

ex:binary-one

—

typebeam/77f26145-94db-4cae-9f14-ffd10b5837d7

ex:Tensor

—

labelbeam/77f26145-94db-4cae-9f14-ffd10b5837d7

attention_mask

—

createdBybeam/77f26145-94db-4cae-9f14-ffd10b5837d7

ex:torch-randint

—

shapebeam/77f26145-94db-4cae-9f14-ffd10b5837d7

ex:batch-size-sequence-length

—

valueRangebeam/77f26145-94db-4cae-9f14-ffd10b5837d7

ex:0-to-2

—

createdWithbeam/77f26145-94db-4cae-9f14-ffd10b5837d7

ex:torch-randint

References (4)

4 references

[1]537fbc2b 7909 4faa Acb8 7dc9250789994 facts
ctx:claims/beam/537fbc2b-7909-4faa-acb8-7dc925078999
- full textbeam-chunk
  text/plain1 KBdoc:beam/537fbc2b-7909-4faa-acb8-7dc925078999
  Show excerpt
  I've been using the Hugging Face Transformers library, and I'm impressed by its performance, but I need to ensure that my embedding dimensions are correctly configured. Here's a snippet of my current code: ``` import torch from transformers…
[2]5e8a169a B4c0 41ba 8477 6cb9d783868b9 facts
ctx:claims/beam/5e8a169a-b4c0-41ba-8477-6cb9d783868b
- full textbeam-chunk
  text/plain1 KBdoc:beam/5e8a169a-b4c0-41ba-8477-6cb9d783868b
  Show excerpt
  input_ids = torch.tensor([[1, 2, 3], [4, 5, 6]]) attention_mask = torch.tensor([[0, 0, 1], [1, 0, 0]]) input_ids, attention_mask = handler(input_ids, attention_mask) print(input_ids) print(attention_mask) ``` ### Explanation 1. **Check fo…
[3]F1f8f635 6c4d 4009 A459 C40f4e5e49a57 facts
ctx:claims/beam/f1f8f635-6c4d-4009-a459-c40f4e5e49a5
- full textbeam-chunk
  text/plain1 KBdoc:beam/f1f8f635-6c4d-4009-a459-c40f4e5e49a5
  Show excerpt
  optimized_input_ids = self.optimize_input_ids(input_ids) optimized_attention_mask = self.optimize_attention_mask(attention_mask) return optimized_input_ids, optimized_attention_mask def optimize_inp…
[4]77f26145 94db 4cae 9f14 Ffd10b5837d76 facts
ctx:claims/beam/77f26145-94db-4cae-9f14-ffd10b5837d7

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attention_mask

Inbound mentions (5)

containsContains(1)

createsTensorCreates Tensor(1)

derivedFromDerived From(1)

hasArgumentHas Argument(1)

is-used-to-createIs Used to Create(1)

Other facts (24)

Timeline

References (4)

See also

Keep researching

`contains`Contains(1)

`createsTensor`Creates Tensor(1)

`derivedFrom`Derived From(1)

`hasArgument`Has Argument(1)

`is-used-to-create`Is Used to Create(1)