com.johnsnowlabs.nlp.annotators.sentence_detector_dl
Trains TensorFlow model for multi-class text classification
Trains TensorFlow model for multi-class text classification
Maximum number of epochs to train
A flag indicating whether to split sentences into different Dataset rows.
A flag indicating whether to split sentences into different Dataset rows. Useful for higher parallelism in fat rows. Defaults to false.
Maximum number of epochs to train
Whether to split sentences into different Dataset rows.
Whether to split sentences into different Dataset rows. Useful for higher parallelism in fat rows. Defaults to false.
Get impossible penultimates
input annotations columns currently used
Get model architecture
Gets annotation column name going to generate
Gets annotation column name going to generate
Get output logs path
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.
Impossible penultimates
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
Model architecture
Output annotator type : CATEGORY
Output annotator type : CATEGORY
Path to folder to output logs.
Path to folder to output logs. If no path is specified, no logs are generated
Maximum number of epochs to train
Whether to split sentences into different Dataset rows.
Whether to split sentences into different Dataset rows. Useful for higher parallelism in fat rows. Defaults to false.
Set impossible penultimates
Overrides required annotators column if different than default
Overrides required annotators column if different than default
Set architecture
Overrides annotation column name when transforming
Overrides annotation column name when transforming
Set the output log path
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.
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.