Type Members

1.  class DocumentLogRegClassifierApproach extends AnnotatorApproach[DocumentLogRegClassifierModel] with CheckLicense

   Trains a model to classify documents with a Logarithmic Regression algorithm.

   Trains a model to classify documents with a Logarithmic Regression algorithm. Training data requires columns for text and their label. The result is a trained DocumentLogRegClassifierModel.

   Example

   Define pipeline stages to prepare the data

   val document_assembler = new DocumentAssembler()
     .setInputCol("text")
     .setOutputCol("document")
   
   val tokenizer = new Tokenizer()
     .setInputCols("document")
     .setOutputCol("token")
   
   val normalizer = new Normalizer()
     .setInputCols("token")
     .setOutputCol("normalized")
   
   val stopwords_cleaner = new StopWordsCleaner()
     .setInputCols("normalized")
     .setOutputCol("cleanTokens")
     .setCaseSensitive(false)
   
   val stemmer = new Stemmer()
     .setInputCols("cleanTokens")
     .setOutputCol("stem")

   Define the document classifier and fit training data to it

   val logreg = new DocumentLogRegClassifierApproach()
     .setInputCols("stem")
     .setLabelCol("category")
     .setOutputCol("prediction")
   
   val pipeline = new Pipeline().setStages(Array(
     document_assembler,
     tokenizer,
     normalizer,
     stopwords_cleaner,
     stemmer,
     logreg
   ))
   
   val model = pipeline.fit(trainingData)

   See also

   DocumentLogRegClassifierModel for instantiated models

2.  class DocumentLogRegClassifierModel extends Model[DocumentLogRegClassifierModel] with RawAnnotator[DocumentLogRegClassifierModel] with CanBeLazy with CheckLicense

   Classifies documents with a Logarithmic Regression algorithm.

   Classifies documents with a Logarithmic Regression algorithm. Currently there are no pretrained models available. Please see DocumentLogRegClassifierApproach to train your own model.

   Please check out the Models Hub for available models in the future.

3.  class MedicalBertForSequenceClassification extends AnnotatorModel[MedicalBertForSequenceClassification] with HasBatchedAnnotate[MedicalBertForSequenceClassification] with WriteTensorflowModel with HasCaseSensitiveProperties with CheckLicense

   MedicalBertForSequenceClassification can load Bert Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g.

   MedicalBertForSequenceClassification can load Bert Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for multi-class document classification tasks.

   Pretrained models can be loaded with pretrained of the companion object:

   val sequenceClassifier = MedicalBertForSequenceClassification.pretrained()
     .setInputCols("token", "document")
     .setOutputCol("label")

   The default model is "bert_sequence_classifier_ade", if no name is provided.

   For available pretrained models please see the Models Hub.

   Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. The Spark NLP Workshop example shows how to import them https://github.com/JohnSnowLabs/spark-nlp/discussions/5669.

   Example

   import spark.implicits._
   import com.johnsnowlabs.nlp.base._
   import com.johnsnowlabs.nlp.annotator._
   import org.apache.spark.ml.Pipeline
   
   val documentAssembler = new DocumentAssembler()
     .setInputCol("text")
     .setOutputCol("document")
   
   val tokenizer = new Tokenizer()
     .setInputCols("document")
     .setOutputCol("token")
   
   val sequenceClassifier = MedicalBertForSequenceClassification.pretrained()
     .setInputCols("token", "document")
     .setOutputCol("label")
     .setCaseSensitive(true)
   
   val pipeline = new Pipeline().setStages(Array(
     documentAssembler,
     tokenizer,
     sequenceClassifier
   ))
   
   val data = Seq("John Lenon was born in London and lived in Paris. My name is Sarah and I live in London").toDF("text")
   val result = pipeline.fit(data).transform(data)
   
   result.select("label.result").show(false)
   +--------------------+
   |result              |
   +--------------------+
   |[True, False]       |
   +--------------------+

   See also

   MedicalBertForSequenceClassification for sequnece-level classification

   Annotators Main Page for a list of transformer based classifiers

4.  class MedicalBertForTokenClassifier extends AnnotatorModel[MedicalBertForTokenClassifier] with HasBatchedAnnotate[MedicalBertForTokenClassifier] with WriteTensorflowModel with HasCaseSensitiveProperties with CheckLicense
5.  class MedicalDistilBertForSequenceClassification extends AnnotatorModel[MedicalDistilBertForSequenceClassification] with HasBatchedAnnotate[MedicalDistilBertForSequenceClassification] with WriteTensorflowModel with HasCaseSensitiveProperties with CheckLicense

   MedicalDistilBertForSequenceClassification can load DistilBERT Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g.

   MedicalDistilBertForSequenceClassification can load DistilBERT Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for multi-class document classification tasks.

   Pretrained models can be loaded with pretrained of the companion object:

   val sequenceClassifier = MedicalDistilBertForSequenceClassification.pretrained()
     .setInputCols("token", "document")
     .setOutputCol("label")

   The default model is "distilbert_base_sequence_classifier_imdb", if no name is provided.

   For available pretrained models please see the Models Hub.

   Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. The Spark NLP Workshop example shows how to import them https://github.com/JohnSnowLabs/spark-nlp/discussions/5669. and the MedicalDistilBertForSequenceClassificationTestSpec.

   Example

   import spark.implicits._
   import com.johnsnowlabs.nlp.base._
   import com.johnsnowlabs.nlp.annotator._
   import org.apache.spark.ml.Pipeline
   
   val documentAssembler = new DocumentAssembler()
     .setInputCol("text")
     .setOutputCol("document")
   
   val tokenizer = new Tokenizer()
     .setInputCols("document")
     .setOutputCol("token")
   
   val sequenceClassifier = MedicalDistilBertForSequenceClassification.pretrained()
     .setInputCols("token", "document")
     .setOutputCol("label")
     .setCaseSensitive(true)
   
   val pipeline = new Pipeline().setStages(Array(
     documentAssembler,
     tokenizer,
     sequenceClassifier
   ))
   
   val data = Seq("John Lenon was born in London and lived in Paris. My name is Sarah and I live in London").toDF("text")
   val result = pipeline.fit(data).transform(data)
   
   result.select("label.result").show(false)
   +--------------------+
   |result              |
   +--------------------+
   |[neg, neg]          |
   |[pos, pos, pos, pos]|
   +--------------------+

   See also

   MedicalDistilBertForSequenceClassification for sequence-level classification

   Annotators Main Page for a list of transformer based classifiers

6.  trait ReadBertForTokenTensorflowModel extends ReadTensorflowModel
7.  trait ReadDistilBertForSequenceTensorflowModel extends ReadTensorflowModel
8.  trait ReadMedicalBertForSequenceClassification extends ReadTensorflowModel
9.  trait ReadablePretrainedBertForSequenceModel extends ParamsAndFeaturesReadable[MedicalBertForSequenceClassification] with HasPretrained[MedicalBertForSequenceClassification]
10.  trait ReadablePretrainedBertForTokenModel extends ParamsAndFeaturesReadable[MedicalBertForTokenClassifier] with HasPretrained[MedicalBertForTokenClassifier]
11.  trait ReadablePretrainedDistilBertForSequenceModel extends ParamsAndFeaturesReadable[MedicalDistilBertForSequenceClassification] with HasPretrained[MedicalDistilBertForSequenceClassification]
12.  trait ReadablePretrainedDocumentLogRegClassifierModel extends ParamsAndFeaturesReadable[DocumentLogRegClassifierModel] with HasPretrained[DocumentLogRegClassifierModel]