com.johnsnowlabs.nlp.annotators.classifier.dl
MultiClassifierDLApproach
Related Doc:
package 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
Whether to output to annotators log folder
Batch size
Tensorflow config Protobytes passed to the TF session
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
Max sequence length to feed into TensorFlow
The minimum threshold for each label to be accepted.
The minimum threshold for each label to be accepted. Default is 0.5
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
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
outputLogsPath
shufflePerEpoch
The minimum threshold for each label to be accepted.
The minimum threshold for each label to be accepted. Default is 0.5
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
Whether to shuffle the training data on each Epoch
The minimum threshold for each label to be accepted.
The minimum threshold for each label to be accepted. Default is 0.5
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
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