com.johnsnowlabs.nlp.annotators.ld.dl
LanguageDetectorDL
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object LanguageDetectorDL
| package dl
com.johnsnowlabs.nlp.annotators.ld.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
alphabet
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
coalesceSentences
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
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
ConfigProto from tensorflow, serialized into byte array.
ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()
input annotations columns currently used
languages
Gets annotation column name going to generate
Gets annotation column name going to generate
threshold
Annotator reference id.
Annotator reference id. Used to identify elements in metadata or to refer to this annotator type
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
language
alphabet used to feed the TensorFlow model for prediction
If sets to true the output of all sentences will be averaged to one output instead of one output per sentence.
If sets to true the output of all sentences will be averaged to one output instead of one output per sentence. Default to true.
ConfigProto from tensorflow, serialized into byte array.
ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()
Overrides required annotators column if different than default
Overrides required annotators column if different than default
language used to map prediction to ISO 639-1 language codes
Overrides annotation column name when transforming
Overrides annotation column name when transforming
The minimum threshold for the final result otheriwse it will be either Unknown or the value set in thresholdLabel.
The minimum threshold for the final result otheriwse it will be either Unknown or the value set in thresholdLabel.
Value is between 0.0 to 1.0 Try to set this lower if your text is hard to predict
In case the score of prediction is less than threshold, what should be the label.
In case the score of prediction is less than threshold, what should be the label. Default is Unknown.
threshold
thresholdLabel
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
Language Identification and Detection by using CNNs and RNNs architectures in TensowrFlow LanguageDetectorDL is an annotator that detects the language of documents or sentences depending on the inputCols
The models are trained on large datasets such as Wikipedia and Tatoeba The output is a language code in Wiki Code style: https://en.wikipedia.org/wiki/List_of_Wikipedias