com.johnsnowlabs.nlp.annotators.classifier.dl
ClassifierDLApproach
Related Docs:
object ClassifierDLApproach
| package dl
com.johnsnowlabs.nlp.annotators.classifier.dl
Batch size
ConfigProto from tensorflow, serialized into byte array.
ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()
Trains TensorFlow model for multi-class text classification
Trains TensorFlow model for multi-class text classification
Dropout coefficient
Whether to output to annotators log folder
Batch size
Tensorflow config Protobytes passed to the TF session
Dropout coefficient
Whether to output to annotators log folder
input annotations columns currently used
Column with label per each document
Learning Rate
Maximum number of epochs to train
Gets annotation column name going to generate
Gets annotation column name going to generate
Choose the proportion of training dataset to be validated against the model on each Epoch.
Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off.
Input annotator type : SENTENCE_EMBEDDINGS
Input 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
Column with label per each document
Learning Rate
Maximum number of epochs to train
Output annotator type : CATEGORY
Output annotator type : CATEGORY
Random seed
Batch size
Tensorflow config Protobytes passed to the TF session
Dropout coefficient
Whether to output to annotators log folder
Overrides required annotators column if different than default
Overrides required annotators column if different than default
Column with label per each document
Learning Rate
Maximum number of epochs to train
Overrides annotation column name when transforming
Overrides annotation column name when transforming
Choose the proportion of training dataset to be validated against the model on each Epoch.
Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off.
Level of verbosity during training
Level of verbosity during training
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
Choose the proportion of training dataset to be validated against the model on each Epoch.
Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off.
Level of verbosity during training
Required input and expected output annotator types
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