com.johnsnowlabs.nlp.embeddings
SentenceEmbeddings
Related Docs:
object SentenceEmbeddings
| package embeddings
com.johnsnowlabs.nlp.embeddings
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
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
Number of embedding dimensions (Default: 100)
Number of embedding dimensions (Default: 100)
Override for additional custom schema checks
Override for additional custom schema checks
Number of embedding dimensions (Default: 100)
Number of embedding dimensions (Default: 100)
input annotations columns currently used
Gets annotation column name going to generate
Gets annotation column name going to generate
Input annotator type : DOCUMENT, WORD_EMBEDDINGS
Input annotator type : DOCUMENT, WORD_EMBEDDINGS
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
Output annotator type : SENTENCE_EMBEDDINGS
Output annotator type : SENTENCE_EMBEDDINGS
Choose how you would like to aggregate Word Embeddings to Sentence Embeddings (Default: "AVERAGE").
Choose how you would like to aggregate Word Embeddings to Sentence Embeddings (Default: "AVERAGE").
Can either be "AVERAGE" or "SUM".
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
Choose how you would like to aggregate Word Embeddings to Sentence Embeddings (Default: "AVERAGE").
Choose how you would like to aggregate Word Embeddings to Sentence Embeddings (Default: "AVERAGE").
Can either be "AVERAGE" or "SUM".
Unique identifier for storage (Default: this.uid)
Unique identifier for storage (Default: this.uid)
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()
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
Converts the results from WordEmbeddings, BertEmbeddings, or ElmoEmbeddings into sentence or document embeddings by either summing up or averaging all the word embeddings in a sentence or a document (depending on the inputCols).
This can be configured with
setPoolingStrategy, which either be"AVERAGE"or"SUM".For more extended examples see the Spark NLP Workshop. and the SentenceEmbeddingsTestSpec.
Example