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 used to feed the TensorFlow model for prediction
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
Output average of sentences instead of one output per sentence (Default: true
).
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
input annotations columns currently used
Gets annotation column name going to generate
Gets annotation column name going to generate
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 used to map prediction to ISO 639-1 language codes
Languages the model was trained with.
Overrides required annotators column if different than default
Overrides required annotators column if different than default
Overrides annotation column name when transforming
Overrides annotation column name when transforming
The minimum threshold for the final result, otherwise it will be either "unk"
or the value set in
thresholdLabel
(Default: 0.1f
).
The minimum threshold for the final result, otherwise it will be either "unk"
or the value set in
thresholdLabel
(Default: 0.1f
).
Value is between 0.0 to 1.0. Try to set this lower if your text is hard to predict
Value for the classification, if confidence is less than threshold
(Default: "unk"
).
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
A list of (hyper-)parameter keys this annotator can take. Users can set and get the parameter values through setters and getters, respectively.
Language Identification and Detection by using CNN and RNN architectures in TensorFlow.
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. Depending on the language (how similar the characters are), the LanguageDetectorDL works best with text longer than 140 characters. The output is a language code in Wiki Code style.Pretrained models can be loaded with
pretrained
of the companion object:The default model is
"ld_wiki_tatoeba_cnn_21"
, default language is"xx"
(meaning multi-lingual), if no values are provided. For available pretrained models please see the Models Hub.For extended examples of usage, see the Spark NLP Workshop And the LanguageDetectorDLTestSpec.
Example