Normalize

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From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)

Normalize is Ensure that both sparse (BM25) and dense scores are normalized to the same scale.

18 facts·10 predicates·8 sources·5 in dispute

Mostly:rdf:type(3), precedes(3), purpose(2)

Inbound mentions (13)

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`hasStep`Has Step(2)

Score Fusion Pipelineex:score-fusion-pipeline
Search Retrieve Processex:search-retrieve-process

`addressedBy`Addressed by(1)

Score Domination Preventionex:score-domination-prevention

`consistsOf`Consists of(1)

Full Pipelineex:full-pipeline

`correspondsTo`Corresponds to(1)

Section Normalizationex:section-normalization

`follows`Follows(1)

Weighting Stepex:weighting-step

`hasOrderedStep`Has Ordered Step(1)

Step Sequenceex:step-sequence

`includes`Includes(1)

Query Processingex:query-processing

`locatedAfter`Located After(1)

Integration Step 4 5ex:integration-step-4-5

`modified-by`Modified by(1)

Loss Variableex:loss-variable

`preprocessed-by`Preprocessed by(1)

Query Vectorex:query-vector

`rdf:type`Rdf:type(1)

Step 5ex:step-5

`resultOf`Result of(1)

Normalized Scoresex:normalized-scores

Other facts (16)

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.

16 facts

Predicate	Value	Ref
Rdf:type	Preprocessing Step	[1]
Rdf:type	Step	[6]
Rdf:type	Processing Step	[8]
Precedes	Index Creation Step	[3]
Precedes	Weighting Step	[6]
Precedes	Fusion Step	[7]
Purpose	Cosine Compatibility	[2]
Purpose	prevent-one-method-from-dominating	[6]
Requires	Sparse Scores	[6]
Requires	Dense Scores	[6]
Has Operation Description	4 sq + sum + sqrt + 4 div	[5]
Has Calculated Flo Ps	14	[5]
Has Approximate Flo Ps	true	[5]
Step Number	1	[6]
Description	Ensure that both sparse (BM25) and dense scores are normalized to the same scale	[6]
Enables	Weighting Step	[6]

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.

18 undated

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typebeam/efd9e47b-8b3a-4eab-a817-a886c4565864

ex:PreprocessingStep

—

purposebeam/cd357396-3d15-4187-a06d-464838aefe07

ex:cosine-compatibility

—

precedesbeam/9c3d6c77-2b58-4a3b-9618-59e705c00dfd

ex:index-creation-step

—

labelblah/watt-activation/370

normalize step

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labelblah/watt-activation/461

Normalize

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hasOperationDescriptionblah/watt-activation/461

4 sq + sum + sqrt + 4 div

—

hasCalculatedFLOPsblah/watt-activation/461

—

hasApproximateFLOPsblah/watt-activation/461

true

—

typebeam/2b9cc40e-4d45-444b-b775-a81c9b036d4a

ex:Step

—

stepNumberbeam/2b9cc40e-4d45-444b-b775-a81c9b036d4a

—

descriptionbeam/2b9cc40e-4d45-444b-b775-a81c9b036d4a

Ensure that both sparse (BM25) and dense scores are normalized to the same scale

—

requiresbeam/2b9cc40e-4d45-444b-b775-a81c9b036d4a

ex:sparse-scores

—

requiresbeam/2b9cc40e-4d45-444b-b775-a81c9b036d4a

ex:dense-scores

—

purposebeam/2b9cc40e-4d45-444b-b775-a81c9b036d4a

prevent-one-method-from-dominating

—

precedesbeam/2b9cc40e-4d45-444b-b775-a81c9b036d4a

ex:weighting-step

—

enablesbeam/2b9cc40e-4d45-444b-b775-a81c9b036d4a

ex:weighting-step

—

precedesbeam/c2cfce3c-ef3d-4bc1-8ac6-e059a3dd9fbb

ex:fusion-step

—

typebeam/cbc9db46-35a4-41fe-a106-fc2f984bd354

ex:ProcessingStep

References (8)

8 references

[1]Efd9e47b 8b3a 4eab A817 A886c45658641 fact
ctx:claims/beam/efd9e47b-8b3a-4eab-a817-a886c4565864
- full textbeam-chunk
  text/plain1 KBdoc:beam/efd9e47b-8b3a-4eab-a817-a886c4565864
  Show excerpt
  #### Step 7: Search and Retrieve ```python query = "Query in a rare language" query_language = detect_language(query) if query_language == 'rare_language': query_embedding = language_specific_model.encode(query, convert_to_tensor=True…
[2]Cd357396 3d15 4187 A06d 464838aefe071 fact
ctx:claims/beam/cd357396-3d15-4187-a06d-464838aefe07
- full textbeam-chunk
  text/plain1 KBdoc:beam/cd357396-3d15-4187-a06d-464838aefe07
  Show excerpt
  ### Using Quantization for Efficiency Quantization can further reduce the memory footprint and speed up the search process. FAISS supports various quantization techniques, such as PQ (Product Quantization). Here's an example using PQ: ``…
[3]9c3d6c77 2b58 4a3b 9618 59e705c00dfd1 fact
ctx:claims/beam/9c3d6c77-2b58-4a3b-9618-59e705c00dfd
- full textbeam-chunk
  text/plain1 KBdoc:beam/9c3d6c77-2b58-4a3b-9618-59e705c00dfd
  Show excerpt
  # Normalize the vectors for cosine similarity faiss.normalize_L2(vectors) # Create an IVFPQ index nlist = 100 # Number of clusters m = 8 # Number of subquantizers index = faiss.IndexIVFPQ(faiss.IndexFlatL2(128), 128, nlist, m, 8) # 8 is…
[4]3701 fact
ctx:discord/blah/watt-activation/370
- full textwatt-activation-370
  text/plain3 KBdoc:agent/watt-activation-370/319ffc75-f6e3-490e-bf32-ddb97e36692c
  Show excerpt
  [2026-03-18 17:29] xenonfun: ⏺ Here's the status: Implementation complete and pushed: - AnchorBind module in harmonic_mlx/antenna.py — 32 soft anchors on S^{d_bind-1}, softmax assignment, residual fusion - Wired into AntennaHarmonicB…
[5]4614 facts
ctx:discord/blah/watt-activation/461
- full textwatt-activation-461
  text/plain3 KBdoc:agent/watt-activation-461/3e06edea-629f-46f3-bd14-9e5cf4a8936a
  Show excerpt
  [2026-03-21 17:03] xenonfun: ``` FLOPs per token (forward): ┌────────────────────────────────────────┬──────────────────────────┐ │ Operation │ FLOPs │ ├──────────────────────────────…
[6]2b9cc40e 4d45 444b B775 A81c9b036d4a8 facts
ctx:claims/beam/2b9cc40e-4d45-444b-b775-a81c9b036d4a
- full textbeam-chunk
  text/plain1 KBdoc:beam/2b9cc40e-4d45-444b-b775-a81c9b036d4a
  Show excerpt
  [Turn 6413] Assistant: Great to hear that you've found a weighting scheme that provides an 18% relevance lift for 4,000 searches. Applying this to a larger dataset of 25,000 hybrid queries should be straightforward, given that the underlyin…
[7]C2cfce3c Ef3d 4bc1 8ac6 E059a3dd9fbb1 fact
ctx:claims/beam/c2cfce3c-ef3d-4bc1-8ac6-e059a3dd9fbb
- full textbeam-chunk
  text/plain1 KBdoc:beam/c2cfce3c-ef3d-4bc1-8ac6-e059a3dd9fbb
  Show excerpt
  #### 2. Normalization Normalize the scores to ensure they are on the same scale. #### 3. Advanced Fusion Techniques Consider using a weighted sum with normalization. ### Example Code ```python import numpy as np from sklearn.model_select…
[8]Cbc9db46 35a4 41fe A106 Fc2f984bd3541 fact
ctx:claims/beam/cbc9db46-35a4-41fe-a106-fc2f984bd354
- full textbeam-chunk
  text/plain1 KBdoc:beam/cbc9db46-35a4-41fe-a106-fc2f984bd354
  Show excerpt
  1. **Weighted Metrics**: Apply different weights to different metrics based on their importance. 2. **Normalized Metrics**: Normalize the metrics to a common scale, such as a 0-1 range. 3. **Aggregated Metrics**: Aggregate metrics using sta…

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Normalize

Inbound mentions (13)

hasStepHas Step(2)

addressedByAddressed by(1)

consistsOfConsists of(1)

correspondsToCorresponds to(1)

followsFollows(1)

hasOrderedStepHas Ordered Step(1)

includesIncludes(1)

locatedAfterLocated After(1)

modified-byModified by(1)

preprocessed-byPreprocessed by(1)

rdf:typeRdf:type(1)

resultOfResult of(1)

Other facts (16)

Timeline

References (8)

See also

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`hasStep`Has Step(2)

`addressedBy`Addressed by(1)

`consistsOf`Consists of(1)

`correspondsTo`Corresponds to(1)

`follows`Follows(1)

`hasOrderedStep`Has Ordered Step(1)

`includes`Includes(1)

`locatedAfter`Located After(1)

`modified-by`Modified by(1)

`preprocessed-by`Preprocessed by(1)

`rdf:type`Rdf:type(1)

`resultOf`Result of(1)