AntonAntonov/NLPTemplateEngine
(1.0.3)
current version: 1.0.6 »
Examples
Basic Examples (2)
Here we generate classifier creation code:
Here we generate quantile regression pipeline code:
Scope (7)
Association result (1)
Here we get an association result:
Different language targets (2)
The pre-loaded templates are used to produce Machine Learning (ML) computational workflow specifications for: Classification, Latent Semantic Analysis, Quantile Regression, Recommendations, Random tabular data generation. The implementations are in Python, R, and WL. Here is example of Python recommender creation spec:
Here is an example of an R pipeline generation for Quantile Regression:
Introspection question and answers (2)
Concretize can answer "introspection" questions. For example, here we get the WL template for recommendations:
Here we query the number of templates (known):
Bring your own templates (2)
Load the NLP-Template-Engine:
Get the "training" templates data (from CSV file you have created or changed) for a new workflow ("SendMail"):
Add the ingested data for the new workflow (from the CSV file) into the NLP-Template-Engine:
Parse natural language specification with the newly ingested and on-boarded workflow ("SendMail"):
Here is a screenshot with an example of using Concretize for random tabular data generation:
Publisher
Anton Antonov
Compatibility
Wolfram Language Version 13.2
External Links
Version History
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1.0.6
– 28 August 2024
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1.0.5
– 16 August 2024
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1.0.4
– 15 August 2024
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1.0.3
– 14 April 2023
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1.0.2
– 13 April 2023
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1.0.1
– 13 April 2023
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1.0.0
– 12 April 2023
Artistic License 2.0
Paclet Source
See Also