sparknlp.base#

Contains all the basic components to create a Spark NLP Pipeline.

This module contains basic transformers and extensions to the Spark Pipeline interface. These are the LightPipeline and RecursivePipeline which offer additional functionality.

Classes

`Chunk2Doc`	Converts a `CHUNK` type column back into `DOCUMENT`.
`Doc2Chunk`	Converts `DOCUMENT` type annotations into `CHUNK` type with the contents of a `chunkCol`.
`DocumentAssembler`	Prepares data into a format that is processable by Spark NLP.
`EmbeddingsFinisher`	Extracts embeddings from Annotations into a more easily usable form.
`Finisher`	Converts annotation results into a format that easier to use.
`GraphFinisher`	Helper class to convert the knowledge graph from GraphExtraction into a generic format, such as RDF.
`HasRecursiveFit`	Properties for the implementation of the RecursivePipeline.
`HasRecursiveTransform`	Properties for the implementation of the RecursivePipeline.
`LightPipeline`	Creates a LightPipeline from a Spark PipelineModel.
`RecursivePipeline`	Recursive pipelines are Spark NLP specific pipelines that allow a Spark ML Pipeline to know about itself on every Pipeline Stage task.
`RecursivePipelineModel`	Fitted RecursivePipeline.
`TokenAssembler`	This transformer reconstructs a `DOCUMENT` type annotation from tokens, usually after these have been normalized, lemmatized, normalized, spell checked, etc, in order to use this document annotation in further annotators.

sparknlp.annotator.YakeKeywordExtraction

sparknlp.base.Chunk2Doc