com.johnsnowlabs.nlp.annotators.classifier.dl
MultiClassifierDLModel
Related Docs:
object MultiClassifierDLModel
| package dl
com.johnsnowlabs.nlp.annotators.classifier.dl
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
ConfigProto from tensorflow, serialized into byte array.
ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()
requirement for annotators copies
requirement for annotators copies
datasetParams
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
Tensorflow config Protobytes passed to the TF session
input annotations columns currently used
Gets annotation column name going to generate
Gets annotation column name going to generate
The minimum threshold for each label to be accepted.
The minimum threshold for each label to be accepted. Default is 0.5
Output annotator type : SENTENCE_EMBEDDINGS
Output annotator type : SENTENCE_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 : CATEGORY
Output annotator type : CATEGORY
Tensorflow config Protobytes passed to the TF session
datasetParams
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
The minimum threshold for each label to be accepted.
The minimum threshold for each label to be accepted. Default is 0.5
The minimum threshold for each label to be accepted.
The minimum threshold for each label to be accepted. Default is 0.5
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
Required input and expected output annotator types
MultiClassifierDL is a Multi-label Text Classification. MultiClassifierDL Bidirectional GRU with Convolution model we have built inside TensorFlow and supports up to 100 classes. The input to MultiClassifierDL is Sentence Embeddings such as state-of-the-art UniversalSentenceEncoder, BertSentenceEmbeddings, or SentenceEmbeddings
In machine learning, multi-label classification and the strongly related problem of multi-output classification are variants of the classification problem where multiple labels may be assigned to each instance. Multi-label classification is a generalization of multiclass classification, which is the single-label problem of categorizing instances into precisely one of more than two classes; in the multi-label problem there is no constraint on how many of the classes the instance can be assigned to. Formally, multi-label classification is the problem of finding a model that maps inputs x to binary vectors y (assigning a value of 0 or 1 for each element (label) in y). https://en.wikipedia.org/wiki/Multi-label_classification
NOTE: This annotator accepts an array of labels in type of String. NOTE: UniversalSentenceEncoder and SentenceEmbeddings can be used for the inputCol
See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/classifier/dl/MultiClassifierDLTestSpec.scala for further reference on how to use this API