com.johnsnowlabs.nlp.annotators.sbd.deep
DeepSentenceDetector
Related Docs:
object DeepSentenceDetector
| package deep
com.johnsnowlabs.nlp.annotators.sbd.deep
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
takes a document and annotations and produces new annotations of this annotator's annotation type
takes a document and annotations and produces new annotations of this annotator's annotation type
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
requirement for annotators copies
requirement for annotators copies
characters used to explicitly mark sentence bounds
characters used to explicitly mark sentence bounds
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
An array of symbols that deep sentence detector will consider as an end of sentence punctuation
whether to explode each sentence into a different row, for better parallelization.
whether to explode each sentence into a different row, for better parallelization. Defaults to false.
Override for additional custom schema checks
Override for additional custom schema checks
Custom sentence separator text
Custom sentence separator text
Whether to split sentences into different Dataset rows.
Whether to split sentences into different Dataset rows. Useful for higher parallelism in fat rows. Defaults to false.
input annotations columns currently used
Get the maximum allowed length for each sentence
Get the maximum allowed length for each sentence
Get the minimum allowed length for each sentence
Get the minimum allowed length for each sentence
Gets annotation column name going to generate
Gets annotation column name going to generate
Length at which sentences will be forcibly split
Length at which sentences will be forcibly split
Whether to consider abbreviation strategies for better accuracy but slower performance.
Whether to consider abbreviation strategies for better accuracy but slower performance. Defaults to true.
Use only custom bounds without considering those of Pragmatic Segmenter.
Use only custom bounds without considering those of Pragmatic Segmenter. Defaults to false. Needs customBounds.
Whether to include rule-based sentence detector as first filter
Input annotator types : DOCUMENT, TOKEN, CHUNK
Input annotator types : DOCUMENT, TOKEN, CHUNK
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
Set the maximum allowed length for each sentence
Set the maximum allowed length for each sentence
Set the minimum allowed length for each sentence
Set the minimum allowed length for each sentence
Input annotator types : DOCUMENT
Input annotator types : DOCUMENT
Custom sentence separator text
Custom sentence separator text
An array of symbols that deep sentence detector will consider as an end of sentence punctuation.
An array of symbols that deep sentence detector will consider as an end of sentence punctuation. Defaults to “.”, “!”, “?”
Whether to split sentences into different Dataset rows.
Whether to split sentences into different Dataset rows. Useful for higher parallelism in fat rows. Defaults to false.
Whether to include rule-based sentence detector as first filter.
Whether to include rule-based sentence detector as first filter. Defaults to false.
Overrides required annotators column if different than default
Overrides required annotators column if different than default
Set the maximum allowed length for each sentence
Set the maximum allowed length for each sentence
Set the minimum allowed length for each sentence
Set the minimum allowed length for each sentence
Overrides annotation column name when transforming
Overrides annotation column name when transforming
Length at which sentences will be forcibly split
Length at which sentences will be forcibly split
Whether to consider abbreviation strategies for better accuracy but slower performance.
Whether to consider abbreviation strategies for better accuracy but slower performance. Defaults to true.
Use only custom bounds without considering those of Pragmatic Segmenter.
Use only custom bounds without considering those of Pragmatic Segmenter. Defaults to false. Needs customBounds.
length at which sentences will be forcibly split.
length at which sentences will be forcibly split.
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()
internal uid required to generate writable annotators
internal uid required to generate writable annotators
whether to apply abbreviations at sentence detection
whether to apply abbreviations at sentence detection
whether to only utilize custom bounds for sentence detection
whether to only utilize custom bounds for sentence detection
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
Required input and expected output annotator types
Finds sentence bounds in raw text. Applies a Named Entity Recognition DL model.
See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/main/scala/com/johnsnowlabs/nlp/annotators/sbd/deep/DeepSentenceDetector.scala for further reference on how to use this API.