input_ids

as donto believed on

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

input_ids has 35 facts recorded in Dontopedia across 5 references, with 5 live disagreements.

35 facts·18 predicates·5 sources·5 in dispute

Mostly:contains token id(6), contains element(6), rdf:type(4)

Inbound mentions (7)

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

`corresponds-to`Corresponds to(1)

Attention Mask Tensorex:attention-mask-tensor

`createsTensor`Creates Tensor(1)

Test Caseex:test-case

`derivedFrom`Derived From(1)

Optimized Input Idsex:optimized-input-ids

`hasArgument`Has Argument(1)

Handler Callex:handler-call

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

Torch Tensorex:torch-tensor

`uses`Uses(1)

Testing Scenarioex:testing-scenario

Other facts (33)

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.

33 facts

Predicate	Value	Ref
Contains Token Id	1	[2]
Contains Token Id	2	[2]
Contains Token Id	3	[2]
Contains Token Id	4	[2]
Contains Token Id	5	[2]
Contains Token Id	6	[2]
Contains Element	Value 1	[4]
Contains Element	Value 2	[4]
Contains Element	Value 3	[4]
Contains Element	Value 4	[4]
Contains Element	Value 5	[4]
Contains Element	Value 6	[4]
Rdf:type	Torch Tensor	[2]
Rdf:type	Tensor	[3]
Rdf:type	Py Torch Tensor	[4]
Rdf:type	Tensor	[5]
Created by	Torch Tensor	[2]
Created by	Torch Randint	[5]
Has Shape	2x3	[2]
Has Shape	Shape 2x3	[4]
Is a	Torch Tensor	[1]
Has Shape	[2,3]	[1]
Represents	Tokenized Input	[1]
Has Value	[[1, 2, 3], [4, 5, 6]]	[2]
Used in	Handler Call	[2]
Purpose	Simulation	[3]
Contains	Two Sequences	[3]
Property	Different Lengths	[3]
Tensor Value	[[1, 2, 3], [4, 5, 6]]	[4]
Has Element Type	Integer Type	[4]
Shape	Batch Size Sequence Length	[5]
Value Range	0 to 1000	[5]
Created With	Torch Randint	[5]

Timeline

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.

35 undated

—

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

ex:torch-tensor

—

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

[2,3]

—

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

ex:tokenized-input

—

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

ex:TorchTensor

—

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

ex:torch-tensor

—

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

[[1, 2, 3], [4, 5, 6]]

—

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

2x3

—

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

1

—

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

2

—

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

3

—

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

4

—

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

5

—

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

6

—

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

ex:handler-call

—

typebeam/897b7b85-132e-45ab-a5df-34500775a74a

ex:Tensor

—

purposebeam/897b7b85-132e-45ab-a5df-34500775a74a

ex:simulation

—

containsbeam/897b7b85-132e-45ab-a5df-34500775a74a

ex:two-sequences

—

propertybeam/897b7b85-132e-45ab-a5df-34500775a74a

ex:different-lengths

—

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

ex:PyTorchTensor

—

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

input_ids

—

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

[[1, 2, 3], [4, 5, 6]]

—

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

ex:shape-2x3

—

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

ex:integer-type

—

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

ex:value-1

—

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

ex:value-2

—

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

ex:value-3

—

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

ex:value-4

—

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

ex:value-5

—

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

ex:value-6

—

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

ex:Tensor

—

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

input_ids

—

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-1000

—

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

ex:torch-randint

References (5)

5 references

[1]537fbc2b 7909 4faa Acb8 7dc9250789993 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 6cb9d783868b11 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]897b7b85 132e 45ab A5df 34500775a74a4 facts
ctx:claims/beam/897b7b85-132e-45ab-a5df-34500775a74a
- full textbeam-chunk
  text/plain1 KBdoc:beam/897b7b85-132e-45ab-a5df-34500775a74a
  Show excerpt
  3. **Extract Context Window**: Define a lambda layer to extract the context window around each token. The context size is calculated dynamically based on the query length. 4. **Flatten Context Window**: Flatten the context window tensor to …
[4]F1f8f635 6c4d 4009 A459 C40f4e5e49a511 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…
[5]77f26145 94db 4cae 9f14 Ffd10b5837d76 facts
ctx:claims/beam/77f26145-94db-4cae-9f14-ffd10b5837d7

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