Type Members

1.  class ClassifierDLApproach extends AnnotatorApproach[ClassifierDLModel] with ParamsAndFeaturesWritable

   ClassifierDL is a generic Multi-class Text Classification.

   ClassifierDL is a generic Multi-class Text Classification. ClassifierDL uses the state-of-the-art Universal Sentence Encoder as an input for text classifications. The ClassifierDL annotator uses a deep learning model (DNNs) we have built inside TensorFlow and supports up to 100 classes

   NOTE: This annotator accepts a label column of a single item in either type of String, Int, Float, or Double.

   NOTE: UniversalSentenceEncoder, BertSentenceEmbeddings, or 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/ClassifierDLTestSpec.scala for further reference on how to use this API

2.  class ClassifierDLModel extends AnnotatorModel[ClassifierDLModel] with HasSimpleAnnotate[ClassifierDLModel] with WriteTensorflowModel with HasStorageRef with ParamsAndFeaturesWritable

   ClassifierDL is a generic Multi-class Text Classification.

   ClassifierDL is a generic Multi-class Text Classification. ClassifierDL uses the state-of-the-art Universal Sentence Encoder as an input for text classifications. The ClassifierDL annotator uses a deep learning model (DNNs) we have built inside TensorFlow and supports up to 100 classes

   NOTE: This annotator accepts a label column of a single item in either type of String, Int, Float, or Double.

   NOTE: UniversalSentenceEncoder, BertSentenceEmbeddings, or 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/ClassifierDLTestSpec.scala for further reference on how to use this API

3.  class MultiClassifierDLApproach extends AnnotatorApproach[MultiClassifierDLModel] with ParamsAndFeaturesWritable

   MultiClassifierDL is a Multi-label Text Classification.

   MultiClassifierDL is a Multi-label Text Classification. MultiClassifierDL uses a Bidirectional GRU with Convolution model that 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

4.  class MultiClassifierDLModel extends AnnotatorModel[MultiClassifierDLModel] with HasSimpleAnnotate[MultiClassifierDLModel] with WriteTensorflowModel with HasStorageRef with ParamsAndFeaturesWritable

   MultiClassifierDL is a Multi-label Text Classification.

   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

5.  trait ReadClassifierDLTensorflowModel extends ReadTensorflowModel
6.  trait ReadMultiClassifierDLTensorflowModel extends ReadTensorflowModel
7.  trait ReadSentimentDLTensorflowModel extends ReadTensorflowModel
8.  trait ReadablePretrainedClassifierDL extends ParamsAndFeaturesReadable[ClassifierDLModel] with HasPretrained[ClassifierDLModel]
9.  trait ReadablePretrainedMultiClassifierDL extends ParamsAndFeaturesReadable[MultiClassifierDLModel] with HasPretrained[MultiClassifierDLModel]
10.  trait ReadablePretrainedSentimentDL extends ParamsAndFeaturesReadable[SentimentDLModel] with HasPretrained[SentimentDLModel]
11.  class SentimentDLApproach extends AnnotatorApproach[SentimentDLModel] with ParamsAndFeaturesWritable
12.  class SentimentDLModel extends AnnotatorModel[SentimentDLModel] with HasSimpleAnnotate[SentimentDLModel] with WriteTensorflowModel with HasStorageRef with ParamsAndFeaturesWritable