com.johnsnowlabs.nlp.annotators
NormalizerModel
Related Docs:
object NormalizerModel
| package annotators
com.johnsnowlabs.nlp.annotators
internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI
internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI
ToDo: Review implementation, Current implementation generates spaces between non-words, potentially breaking tokens
ToDo: Review implementation, Current implementation generates spaces between non-words, potentially breaking tokens
Annotations that correspond to inputAnnotationCols generated by previous annotators if any
any number of annotations processed for every input annotation. Not necessary one to one relationship
normalization regex patterns which match will be removed from token
requirement for annotators copies
requirement for annotators copies
Wraps annotate to happen inside SparkSQL user defined functions in order to act with org.apache.spark.sql.Column
Wraps annotate to happen inside SparkSQL user defined functions in order to act with org.apache.spark.sql.Column
udf function to be applied to inputCols using this annotator's annotate function as part of ML transformation
Override for additional custom schema checks
Override for additional custom schema checks
input annotations columns currently used
Gets annotation column name going to generate
Gets annotation column name going to generate
Txt file with delimited words to be transformed into something else
Txt file with delimited words to be transformed into something else
Input annotator type : TOKEN
Input annotator type : TOKEN
columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified
columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified
whether to convert strings to lowercase
Set the maximum allowed length for each token
Set the minimum allowed length for each token
Output annotator type : TOKEN
Output annotator type : TOKEN
Overrides required annotators column if different than default
Overrides required annotators column if different than default
Overrides annotation column name when transforming
Overrides annotation column name when transforming
slangDict
slangDict
whether or not to be case sensitive to match slangs.
whether or not to be case sensitive to match slangs. Defaults to false.
Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content
Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content
Dataset[Row]
requirement for pipeline transformation validation.
requirement for pipeline transformation validation. It is called on fit()
required internal uid for saving annotator
required internal uid for saving annotator
takes a Dataset and checks to see if all the required annotation types are present.
takes a Dataset and checks to see if all the required annotation types are present.
to be validated
True if all the required types are present, else false
A list of (hyper-)parameter keys this annotator can take. Users can set and get the parameter values through setters and getters, respectively.
Required input and expected output annotator types
Instantiated Model of the Normalizer. For usage and examples, please see the documentation of that class.
Normalizer for the base class