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
Dataset params
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 (Default: 0.5f
)
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
Dataset params
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 (Default: 0.5f
)
Unique identifier for storage (Default: this.uid
)
Unique identifier for storage (Default: this.uid
)
The minimum threshold for each label to be accepted (Default: 0.5f
)
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
MultiClassifierDL for 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.
This is the instantiated model of the MultiClassifierDLApproach. For training your own model, please see the documentation of that class.
Pretrained models can be loaded with
pretrained
of the companion object:The default model is
"multiclassifierdl_use_toxic"
, if no name is provided. It uses embeddings from the UniversalSentenceEncoder and classifies toxic comments. The data is based on the Jigsaw Toxic Comment Classification Challenge. For available pretrained models please see the Models Hub.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).
For extended examples of usage, see the Spark NLP Workshop and the MultiClassifierDLTestSpec.
Example
SentimentDLModel for sentiment analysis
ClassifierDLModel for single-class classification
Multi-label classification on Wikipedia