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SemanticRanking[{text1, …},ref]

sorts textual items texti by semantic similarity to the reference string ref.

SemanticRanking[list,ref,prop]

returns the specified property prop.

Details
Details and Options Details and Options
Examples  
Basic Examples  
Scope  
Options  
Method  
Properties & Relations  
Possible Issues  
See Also
Related Guides
History
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SemanticRanking

SemanticRanking[{text1, …},ref]

sorts textual items texti by semantic similarity to the reference string ref.

SemanticRanking[list,ref,prop]

returns the specified property prop.

Details

  • SemanticRanking reorders the input textual list according to semantic similarity to a provided reference string.
  • SemanticRanking[{text1, text2, text3,… }, ref] sorts texti from the most to least similar.
  • Possible values for prop include:
  • "Ordering"similarity rank for each item
    "Item"items in the original order
    "Rank"item position in the ordered list
    "RankedItem"items reordered from most to least similar (default)
    "Relevance"numerical similarity between each item and the reference
    {prop1,…}a list of properties
    Alla Tabular with all the properties
  • The following options can be given:
  • Method Automaticsemantic ranking method
  • Possible values for Method include:
  • Automaticdefault local model
    Tiny, Small, Medium, Largepredefined model sizes
    {"string",size}a named model of the specified size
    fa custom ranking function
  • A custom ranker f must operate on the reference string and a list of strings to produce a numerical vector of the same length. The higher the number, the more similar the pair is.
  • Specific ranking models include:
  • "MiniLM"default (Medium) "MiniLM" ranker
    {"MiniLM",Tiny}MiniLM-V2 L-2
    {"MiniLM",Small}MiniLM-V2 L-4
    {"MiniLM",Medium}MiniLM-V2 L-6
    {"MiniLM",Large}MiniLM-V2 L-12

Examples

open all close all

Basic Examples  (2)

Rerank single-word items according to similarity:

Retrieve the relevance of each item:

Rerank sentences according to similarity to a query:

Scope  (3)

Retrieve items in order of similarity from a reference text:

Return a selected property of each item:

Retrieve multiple properties:

Get all properties:

Options  (1)

Method  (1)

Specify a reranking function for species designations using a custom text extractor:

Use the extractor in the function:

Rank the text:

Check the ordering:

Properties & Relations  (1)

SemanticSearch automatically reranks the results using SemanticRanking:

This is equivalent to retrieving the "MinItems" for RerankingMethod without actually doing it:

Then taking the first five results after using SemanticRanking:

Possible Issues  (2)

Create several paragraphs of text:

Reranking such a large number of items is slow:

Instead create a SemanticSearchIndex:

Use SemanticSearch to get a shortlist of the best matches:

Then rerank the shortlist:

The closest result is identical:

Words without context may not be ordered as expected:

A larger ranking model may improve results:

Alternatively, add more context to make the results more reliable:

Wolfram Research (2025), SemanticRanking, Wolfram Language function, https://reference.wolfram.com/language/ref/SemanticRanking.html.

Text

Wolfram Research (2025), SemanticRanking, Wolfram Language function, https://reference.wolfram.com/language/ref/SemanticRanking.html.

CMS

Wolfram Language. 2025. "SemanticRanking." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/SemanticRanking.html.

APA

Wolfram Language. (2025). SemanticRanking. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/SemanticRanking.html

BibTeX

@misc{reference.wolfram_2025_semanticranking, author="Wolfram Research", title="{SemanticRanking}", year="2025", howpublished="\url{https://reference.wolfram.com/language/ref/SemanticRanking.html}", note=[Accessed: 25-August-2025]}

BibLaTeX

@online{reference.wolfram_2025_semanticranking, organization={Wolfram Research}, title={SemanticRanking}, year={2025}, url={https://reference.wolfram.com/language/ref/SemanticRanking.html}, note=[Accessed: 25-August-2025]}