dl

Type Members

1. class AlbertForSequenceClassification extends AnnotatorModel[AlbertForSequenceClassification] with HasBatchedAnnotate[AlbertForSequenceClassification] with WriteTensorflowModel with WriteSentencePieceModel with HasCaseSensitiveProperties with HasClassifierActivationProperties

   

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

   AlbertForSequenceClassification can load ALBERT 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 = AlbertForSequenceClassification.pretrained()
     .setInputCols("token", "document")
     .setOutputCol("label")

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

   For available pretrained models please see the Models Hub.

   To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669. and the AlbertForSequenceClassification.

   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 = AlbertForSequenceClassification.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

   Annotators Main Page for a list of transformer based classifiers

   AlbertForSequenceClassification for sequence-level classification

2. class AlbertForTokenClassification extends AnnotatorModel[AlbertForTokenClassification] with HasBatchedAnnotate[AlbertForTokenClassification] with WriteTensorflowModel with WriteSentencePieceModel with HasCaseSensitiveProperties

   

   AlbertForTokenClassification can load ALBERT Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g.

   AlbertForTokenClassification can load ALBERT Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.

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

   val tokenClassifier = AlbertForTokenClassification.pretrained()
     .setInputCols("token", "document")
     .setOutputCol("label")

   The default model is "albert_base_token_classifier_conll03", if no name is provided.

   For available pretrained models please see the Models Hub.

   and the AlbertForTokenClassificationTestSpec. To see which models are compatible and how to import them see 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 tokenClassifier = AlbertForTokenClassification.pretrained()
     .setInputCols("token", "document")
     .setOutputCol("label")
     .setCaseSensitive(true)
   
   val pipeline = new Pipeline().setStages(Array(
     documentAssembler,
     tokenizer,
     tokenClassifier
   ))
   
   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                                                                              |
   +------------------------------------------------------------------------------------+
   |[B-PER, I-PER, O, O, O, B-LOC, O, O, O, B-LOC, O, O, O, O, B-PER, O, O, O, O, B-LOC]|
   +------------------------------------------------------------------------------------+

   See also

   Annotators Main Page for a list of transformer based classifiers

   AlbertForTokenClassification for token-level classification

3. class BertForSequenceClassification extends AnnotatorModel[BertForSequenceClassification] with HasBatchedAnnotate[BertForSequenceClassification] with WriteTensorflowModel with HasCaseSensitiveProperties with HasClassifierActivationProperties

   

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

   BertForSequenceClassification 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 = BertForSequenceClassification.pretrained()
     .setInputCols("token", "document")
     .setOutputCol("label")

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

   For available pretrained models please see the Models Hub.

   To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669. and the BertForSequenceClassificationTestSpec.

   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 = BertForSequenceClassification.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

   Annotators Main Page for a list of transformer based classifiers

   BertForSequenceClassification for sequnece-level classification

4. class BertForTokenClassification extends AnnotatorModel[BertForTokenClassification] with HasBatchedAnnotate[BertForTokenClassification] with WriteTensorflowModel with HasCaseSensitiveProperties

   

   BertForTokenClassification can load Bert Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g.

   BertForTokenClassification can load Bert Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.

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

   val tokenClassifier = BertForTokenClassification.pretrained()
     .setInputCols("token", "document")
     .setOutputCol("label")

   The default model is "bert_base_token_classifier_conll03", if no name is provided.

   For available pretrained models please see the Models Hub.

   To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669. and the BertForTokenClassificationTestSpec.

   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 tokenClassifier = BertForTokenClassification.pretrained()
     .setInputCols("token", "document")
     .setOutputCol("label")
     .setCaseSensitive(true)
   
   val pipeline = new Pipeline().setStages(Array(
     documentAssembler,
     tokenizer,
     tokenClassifier
   ))
   
   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                                                                              |
   +------------------------------------------------------------------------------------+
   |[B-PER, I-PER, O, O, O, B-LOC, O, O, O, B-LOC, O, O, O, O, B-PER, O, O, O, O, B-LOC]|
   +------------------------------------------------------------------------------------+

   See also

   Annotators Main Page for a list of transformer based classifiers

   BertForTokenClassification for token-level classification

5. class ClassifierDLApproach extends AnnotatorApproach[ClassifierDLModel] with ParamsAndFeaturesWritable

   

   Trains a ClassifierDL for generic Multi-class Text Classification.

   Trains a ClassifierDL for generic Multi-class Text Classification.

   ClassifierDL uses the state-of-the-art Universal Sentence Encoder as an input for text classifications. The ClassifierDL annotator uses a deep learning model (DNNs) we have built inside TensorFlow and supports up to 100 classes.

   For instantiated/pretrained models, see ClassifierDLModel.

   Notes:

   • This annotator accepts a label column of a single item in either type of String, Int, Float, or Double.
   • UniversalSentenceEncoder, BertSentenceEmbeddings, or SentenceEmbeddings can be used for the inputCol.

   For extended examples of usage, see the Spark NLP Workshop [1] [2] and the ClassifierDLTestSpec.

   Example

   In this example, the training data "sentiment.csv" has the form of

   text,label
   This movie is the best movie I have wached ever! In my opinion this movie can win an award.,0
   This was a terrible movie! The acting was bad really bad!,1
   ...

   Then traning can be done like so:

   import com.johnsnowlabs.nlp.base.DocumentAssembler
   import com.johnsnowlabs.nlp.embeddings.UniversalSentenceEncoder
   import com.johnsnowlabs.nlp.annotators.classifier.dl.ClassifierDLApproach
   import org.apache.spark.ml.Pipeline
   
   val smallCorpus = spark.read.option("header","true").csv("src/test/resources/classifier/sentiment.csv")
   
   val documentAssembler = new DocumentAssembler()
     .setInputCol("text")
     .setOutputCol("document")
   
   val useEmbeddings = UniversalSentenceEncoder.pretrained()
     .setInputCols("document")
     .setOutputCol("sentence_embeddings")
   
   val docClassifier = new ClassifierDLApproach()
     .setInputCols("sentence_embeddings")
     .setOutputCol("category")
     .setLabelColumn("label")
     .setBatchSize(64)
     .setMaxEpochs(20)
     .setLr(5e-3f)
     .setDropout(0.5f)
   
   val pipeline = new Pipeline()
     .setStages(
       Array(
         documentAssembler,
         useEmbeddings,
         docClassifier
       )
     )
   
   val pipelineModel = pipeline.fit(smallCorpus)

   See also

   SentimentDLApproach for sentiment analysis

   MultiClassifierDLApproach for multi-class classification

6. class ClassifierDLModel extends AnnotatorModel[ClassifierDLModel] with HasSimpleAnnotate[ClassifierDLModel] with WriteTensorflowModel with HasStorageRef with ParamsAndFeaturesWritable

   

   ClassifierDL for generic Multi-class Text Classification.

   ClassifierDL for generic Multi-class Text Classification.

   ClassifierDL uses the state-of-the-art Universal Sentence Encoder as an input for text classifications. The ClassifierDL annotator uses a deep learning model (DNNs) we have built inside TensorFlow and supports up to 100 classes.

   This is the instantiated model of the ClassifierDLApproach. For training your own model, please see the documentation of that class.

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

   val classifierDL = ClassifierDLModel.pretrained()
     .setInputCols("sentence_embeddings")
     .setOutputCol("classification")

   The default model is "classifierdl_use_trec6", if no name is provided. It uses embeddings from the UniversalSentenceEncoder and is trained on the TREC-6 dataset. For available pretrained models please see the Models Hub.

   For extended examples of usage, see the Spark NLP Workshop and the ClassifierDLTestSpec.

   Example

   import spark.implicits._
   import com.johnsnowlabs.nlp.base.DocumentAssembler
   import com.johnsnowlabs.nlp.annotator.SentenceDetector
   import com.johnsnowlabs.nlp.annotators.classifier.dl.ClassifierDLModel
   import com.johnsnowlabs.nlp.embeddings.UniversalSentenceEncoder
   import org.apache.spark.ml.Pipeline
   
   val documentAssembler = new DocumentAssembler()
     .setInputCol("text")
     .setOutputCol("document")
   
   val sentence = new SentenceDetector()
     .setInputCols("document")
     .setOutputCol("sentence")
   
   val useEmbeddings = UniversalSentenceEncoder.pretrained()
     .setInputCols("document")
     .setOutputCol("sentence_embeddings")
   
   val sarcasmDL = ClassifierDLModel.pretrained("classifierdl_use_sarcasm")
     .setInputCols("sentence_embeddings")
     .setOutputCol("sarcasm")
   
   val pipeline = new Pipeline()
     .setStages(Array(
       documentAssembler,
       sentence,
       useEmbeddings,
       sarcasmDL
     ))
   
   val data = Seq(
     "I'm ready!",
     "If I could put into words how much I love waking up at 6 am on Mondays I would."
   ).toDF("text")
   val result = pipeline.fit(data).transform(data)
   
   result.selectExpr("explode(arrays_zip(sentence, sarcasm)) as out")
     .selectExpr("out.sentence.result as sentence", "out.sarcasm.result as sarcasm")
     .show(false)
   +-------------------------------------------------------------------------------+-------+
   |sentence                                                                       |sarcasm|
   +-------------------------------------------------------------------------------+-------+
   |I'm ready!                                                                     |normal |
   |If I could put into words how much I love waking up at 6 am on Mondays I would.|sarcasm|
   +-------------------------------------------------------------------------------+-------+

   See also

   SentimentDLModel for sentiment analysis

   MultiClassifierDLModel for multi-class classification

7. class DeBertaForSequenceClassification extends AnnotatorModel[DeBertaForSequenceClassification] with HasBatchedAnnotate[DeBertaForSequenceClassification] with WriteTensorflowModel with WriteSentencePieceModel with HasCaseSensitiveProperties with HasClassifierActivationProperties

   

   DeBertaForSequenceClassification can load DeBerta v2 & v3 Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g.

   DeBertaForSequenceClassification can load DeBerta v2 & v3 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 = DeBertaForSequenceClassification.pretrained()
     .setInputCols("token", "document")
     .setOutputCol("label")

   The default model is "deberta_v3_xsmall_sequence_classifier_imdb", if no name is provided.

   For available pretrained models please see the Models Hub.

   To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669. and the DeBertaForSequenceClassification.

   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 = DeBertaForSequenceClassification.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

   Annotators Main Page for a list of transformer based classifiers

   DeBertaForSequenceClassification for sequence-level classification

8. class DeBertaForTokenClassification extends AnnotatorModel[DeBertaForTokenClassification] with HasBatchedAnnotate[DeBertaForTokenClassification] with WriteTensorflowModel with WriteSentencePieceModel with HasCaseSensitiveProperties

   

   DeBertaForTokenClassification can load DeBERTA Models v2 and v3 with a token classification head on top (a linear layer on top of the hidden-states output) e.g.

   DeBertaForTokenClassification can load DeBERTA Models v2 and v3 with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.

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

   val tokenClassifier = DeBertaForTokenClassification.pretrained()
     .setInputCols("token", "document")
     .setOutputCol("label")

   The default model is "deberta_xsmall_token_classifier_conll03", if no name is provided.

   For available pretrained models please see the Models Hub.

   and the DeBertaForTokenClassificationTestSpec. 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 tokenClassifier = DeBertaForTokenClassification.pretrained()
     .setInputCols("token", "document")
     .setOutputCol("label")
     .setCaseSensitive(true)
   
   val pipeline = new Pipeline().setStages(Array(
     documentAssembler,
     tokenizer,
     tokenClassifier
   ))
   
   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                                                                              |
   +------------------------------------------------------------------------------------+
   |[B-PER, I-PER, O, O, O, B-LOC, O, O, O, B-LOC, O, O, O, O, B-PER, O, O, O, O, B-LOC]|
   +------------------------------------------------------------------------------------+

   See also

   Annotators Main Page for a list of transformer based classifiers

   DeBertaForTokenClassification for token-level classification

9. class DistilBertForSequenceClassification extends AnnotatorModel[DistilBertForSequenceClassification] with HasBatchedAnnotate[DistilBertForSequenceClassification] with WriteTensorflowModel with HasCaseSensitiveProperties with HasClassifierActivationProperties

   

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

   DistilBertForSequenceClassification 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 = DistilBertForSequenceClassification.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.

   To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669. and the DistilBertForSequenceClassificationTestSpec.

   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 = DistilBertForSequenceClassification.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

   Annotators Main Page for a list of transformer based classifiers

   DistilBertForSequenceClassification for sequence-level classification

10. class DistilBertForTokenClassification extends AnnotatorModel[DistilBertForTokenClassification] with HasBatchedAnnotate[DistilBertForTokenClassification] with WriteTensorflowModel with HasCaseSensitiveProperties

    

    DistilBertForTokenClassification can load Bert Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g.

    DistilBertForTokenClassification can load Bert Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.

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

    val tokenClassifier = DistilBertForTokenClassification.pretrained()
      .setInputCols("token", "document")
      .setOutputCol("label")

    The default model is "distilbert_base_token_classifier_conll03", if no name is provided.

    For available pretrained models please see the Models Hub.

    To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669. and the DistilBertForTokenClassificationTestSpec.

    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 tokenClassifier = DistilBertForTokenClassification.pretrained()
      .setInputCols("token", "document")
      .setOutputCol("label")
      .setCaseSensitive(true)
    
    val pipeline = new Pipeline().setStages(Array(
      documentAssembler,
      tokenizer,
      tokenClassifier
    ))
    
    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                                                                              |
    +------------------------------------------------------------------------------------+
    |[B-PER, I-PER, O, O, O, B-LOC, O, O, O, B-LOC, O, O, O, O, B-PER, O, O, O, O, B-LOC]|
    +------------------------------------------------------------------------------------+

    See also

    Annotators Main Page for a list of transformer based classifiers

    DistilBertForTokenClassification for token-level classification

11. class LongformerForSequenceClassification extends AnnotatorModel[LongformerForSequenceClassification] with HasBatchedAnnotate[LongformerForSequenceClassification] with WriteTensorflowModel with HasCaseSensitiveProperties with HasClassifierActivationProperties

    

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

    LongformerForSequenceClassification can load Longformer 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 = LongformerForSequenceClassification.pretrained()
      .setInputCols("token", "document")
      .setOutputCol("label")

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

    For available pretrained models please see the Models Hub.

    To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669. and the LongformerForSequenceClassification.

    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 = LongformerForSequenceClassification.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

    Annotators Main Page for a list of transformer based classifiers

    LongformerForSequenceClassification for sequence-level classification

12. class LongformerForTokenClassification extends AnnotatorModel[LongformerForTokenClassification] with HasBatchedAnnotate[LongformerForTokenClassification] with WriteTensorflowModel with HasCaseSensitiveProperties

    

    LongformerForTokenClassification can load Longformer Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g.

    LongformerForTokenClassification can load Longformer Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.

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

    val tokenClassifier = LongformerForTokenClassification.pretrained()
      .setInputCols("token", "document")
      .setOutputCol("label")

    The default model is "longformer_base_token_classifier_conll03", if no name is provided.

    For available pretrained models please see the Models Hub.

    and the LongformerForTokenClassificationTestSpec. To see which models are compatible and how to import them see 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 tokenClassifier = LongformerForTokenClassification.pretrained()
      .setInputCols("token", "document")
      .setOutputCol("label")
      .setCaseSensitive(true)
    
    val pipeline = new Pipeline().setStages(Array(
      documentAssembler,
      tokenizer,
      tokenClassifier
    ))
    
    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                                                                              |
    +------------------------------------------------------------------------------------+
    |[B-PER, I-PER, O, O, O, B-LOC, O, O, O, B-LOC, O, O, O, O, B-PER, O, O, O, O, B-LOC]|
    +------------------------------------------------------------------------------------+

    See also

    Annotators Main Page for a list of transformer based classifiers

    LongformerForTokenClassification for token-level classification

13. class MultiClassifierDLApproach extends AnnotatorApproach[MultiClassifierDLModel] with ParamsAndFeaturesWritable

    

    Trains a MultiClassifierDL for Multi-label Text Classification.

    Trains a MultiClassifierDL for Multi-label Text Classification.

    MultiClassifierDL uses a Bidirectional GRU with a convolutional model that we have built inside TensorFlow and supports up to 100 classes.

    For instantiated/pretrained models, see MultiClassifierDLModel.

    The input to MultiClassifierDL are Sentence Embeddings such as the 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).

    Notes:

    • This annotator requires an array of labels in type of String.
    • UniversalSentenceEncoder, BertSentenceEmbeddings, or SentenceEmbeddings can be used for the inputCol.

    For extended examples of usage, see the Spark NLP Workshop and the MultiClassifierDLTestSpec.

    Example

    In this example, the training data has the form (Note: labels can be arbitrary)

    mr,ref
    "name[Alimentum], area[city centre], familyFriendly[no], near[Burger King]",Alimentum is an adult establish found in the city centre area near Burger King.
    "name[Alimentum], area[city centre], familyFriendly[yes]",Alimentum is a family-friendly place in the city centre.
    ...

    It needs some pre-processing first, so the labels are of type Array[String]. This can be done like so:

    import spark.implicits._
    import com.johnsnowlabs.nlp.annotators.classifier.dl.MultiClassifierDLApproach
    import com.johnsnowlabs.nlp.base.DocumentAssembler
    import com.johnsnowlabs.nlp.embeddings.UniversalSentenceEncoder
    import org.apache.spark.ml.Pipeline
    import org.apache.spark.sql.functions.{col, udf}
    
    // Process training data to create text with associated array of labels
    def splitAndTrim = udf { labels: String =>
      labels.split(", ").map(x=>x.trim)
    }
    
    val smallCorpus = spark.read
      .option("header", true)
      .option("inferSchema", true)
      .option("mode", "DROPMALFORMED")
      .csv("src/test/resources/classifier/e2e.csv")
      .withColumn("labels", splitAndTrim(col("mr")))
      .withColumn("text", col("ref"))
      .drop("mr")
    
    smallCorpus.printSchema()
    // root
    // |-- ref: string (nullable = true)
    // |-- labels: array (nullable = true)
    // |    |-- element: string (containsNull = true)
    
    // Then create pipeline for training
    val documentAssembler = new DocumentAssembler()
      .setInputCol("text")
      .setOutputCol("document")
      .setCleanupMode("shrink")
    
    val embeddings = UniversalSentenceEncoder.pretrained()
      .setInputCols("document")
      .setOutputCol("embeddings")
    
    val docClassifier = new MultiClassifierDLApproach()
      .setInputCols("embeddings")
      .setOutputCol("category")
      .setLabelColumn("labels")
      .setBatchSize(128)
      .setMaxEpochs(10)
      .setLr(1e-3f)
      .setThreshold(0.5f)
      .setValidationSplit(0.1f)
    
    val pipeline = new Pipeline()
      .setStages(
        Array(
          documentAssembler,
          embeddings,
          docClassifier
        )
      )
    
    val pipelineModel = pipeline.fit(smallCorpus)

    See also

    SentimentDLApproach for sentiment analysis

    ClassifierDLApproach for single-class classification

    Multi-label classification on Wikipedia

14. class MultiClassifierDLModel extends AnnotatorModel[MultiClassifierDLModel] with HasSimpleAnnotate[MultiClassifierDLModel] with WriteTensorflowModel with HasStorageRef with ParamsAndFeaturesWritable

    

    MultiClassifierDL for Multi-label Text Classification.

    MultiClassifierDL for Multi-label Text Classification.

    MultiClassifierDL Bidirectional GRU with Convolution model 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.

    This is the instantiated model of the MultiClassifierDLApproach. For training your own model, please see the documentation of that class.

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

    val multiClassifier = MultiClassifierDLModel.pretrained()
      .setInputCols("sentence_embeddings")
      .setOutputCol("categories")

    The default model is "multiclassifierdl_use_toxic", if no name is provided. It uses embeddings from the UniversalSentenceEncoder and classifies toxic comments. The data is based on the Jigsaw Toxic Comment Classification Challenge. For available pretrained models please see the Models Hub.

    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).

    For extended examples of usage, see the Spark NLP Workshop and the MultiClassifierDLTestSpec.

    Example

    import spark.implicits._
    import com.johnsnowlabs.nlp.base.DocumentAssembler
    import com.johnsnowlabs.nlp.annotators.classifier.dl.MultiClassifierDLModel
    import com.johnsnowlabs.nlp.embeddings.UniversalSentenceEncoder
    import org.apache.spark.ml.Pipeline
    
    val documentAssembler = new DocumentAssembler()
      .setInputCol("text")
      .setOutputCol("document")
    
    val useEmbeddings = UniversalSentenceEncoder.pretrained()
      .setInputCols("document")
      .setOutputCol("sentence_embeddings")
    
    val multiClassifierDl = MultiClassifierDLModel.pretrained()
      .setInputCols("sentence_embeddings")
      .setOutputCol("classifications")
    
    val pipeline = new Pipeline()
      .setStages(Array(
        documentAssembler,
        useEmbeddings,
        multiClassifierDl
      ))
    
    val data = Seq(
      "This is pretty good stuff!",
      "Wtf kind of crap is this"
    ).toDF("text")
    val result = pipeline.fit(data).transform(data)
    
    result.select("text", "classifications.result").show(false)
    +--------------------------+----------------+
    |text                      |result          |
    +--------------------------+----------------+
    |This is pretty good stuff!|[]              |
    |Wtf kind of crap is this  |[toxic, obscene]|
    +--------------------------+----------------+

    See also

    SentimentDLModel for sentiment analysis

    ClassifierDLModel for single-class classification

    Multi-label classification on Wikipedia

15. trait ReadAlbertForSequenceTensorflowModel extends ReadTensorflowModel with ReadSentencePieceModel

    

16. trait ReadAlbertForTokenTensorflowModel extends ReadTensorflowModel with ReadSentencePieceModel

    

17. trait ReadBertForSequenceTensorflowModel extends ReadTensorflowModel

    

18. trait ReadBertForTokenTensorflowModel extends ReadTensorflowModel

    

19. trait ReadClassifierDLTensorflowModel extends ReadTensorflowModel

    

20. trait ReadDeBertaForSequenceTensorflowModel extends ReadTensorflowModel with ReadSentencePieceModel

    

21. trait ReadDeBertaForTokenTensorflowModel extends ReadTensorflowModel with ReadSentencePieceModel

    

22. trait ReadDistilBertForSequenceTensorflowModel extends ReadTensorflowModel

    

23. trait ReadDistilBertForTokenTensorflowModel extends ReadTensorflowModel

    

24. trait ReadLongformerForSequenceTensorflowModel extends ReadTensorflowModel

    

25. trait ReadLongformerForTokenTensorflowModel extends ReadTensorflowModel

    

26. trait ReadMultiClassifierDLTensorflowModel extends ReadTensorflowModel

    

27. trait ReadRoBertaForSequenceTensorflowModel extends ReadTensorflowModel

    

28. trait ReadRoBertaForTokenTensorflowModel extends ReadTensorflowModel

    

29. trait ReadSentimentDLTensorflowModel extends ReadTensorflowModel

    

30. trait ReadXlmRoBertaForSequenceTensorflowModel extends ReadTensorflowModel with ReadSentencePieceModel

    

31. trait ReadXlmRoBertaForTokenTensorflowModel extends ReadTensorflowModel with ReadSentencePieceModel

    

32. trait ReadXlnetForSequenceTensorflowModel extends ReadTensorflowModel with ReadSentencePieceModel

    

33. trait ReadXlnetForTokenTensorflowModel extends ReadTensorflowModel with ReadSentencePieceModel

    

34. trait ReadablePretrainedAlbertForSequenceModel extends ParamsAndFeaturesReadable[AlbertForSequenceClassification] with HasPretrained[AlbertForSequenceClassification]

    

35. trait ReadablePretrainedAlbertForTokenModel extends ParamsAndFeaturesReadable[AlbertForTokenClassification] with HasPretrained[AlbertForTokenClassification]

    

36. trait ReadablePretrainedBertForSequenceModel extends ParamsAndFeaturesReadable[BertForSequenceClassification] with HasPretrained[BertForSequenceClassification]

    

37. trait ReadablePretrainedBertForTokenModel extends ParamsAndFeaturesReadable[BertForTokenClassification] with HasPretrained[BertForTokenClassification]

    

38. trait ReadablePretrainedClassifierDL extends ParamsAndFeaturesReadable[ClassifierDLModel] with HasPretrained[ClassifierDLModel]

    

39. trait ReadablePretrainedDeBertaForSequenceModel extends ParamsAndFeaturesReadable[DeBertaForSequenceClassification] with HasPretrained[DeBertaForSequenceClassification]

    

40. trait ReadablePretrainedDeBertaForTokenModel extends ParamsAndFeaturesReadable[DeBertaForTokenClassification] with HasPretrained[DeBertaForTokenClassification]

    

41. trait ReadablePretrainedDistilBertForSequenceModel extends ParamsAndFeaturesReadable[DistilBertForSequenceClassification] with HasPretrained[DistilBertForSequenceClassification]

    

42. trait ReadablePretrainedDistilBertForTokenModel extends ParamsAndFeaturesReadable[DistilBertForTokenClassification] with HasPretrained[DistilBertForTokenClassification]

    

43. trait ReadablePretrainedLongformerForSequenceModel extends ParamsAndFeaturesReadable[LongformerForSequenceClassification] with HasPretrained[LongformerForSequenceClassification]

    

44. trait ReadablePretrainedLongformerForTokenModel extends ParamsAndFeaturesReadable[LongformerForTokenClassification] with HasPretrained[LongformerForTokenClassification]

    

45. trait ReadablePretrainedMultiClassifierDL extends ParamsAndFeaturesReadable[MultiClassifierDLModel] with HasPretrained[MultiClassifierDLModel]

    

46. trait ReadablePretrainedRoBertaForSequenceModel extends ParamsAndFeaturesReadable[RoBertaForSequenceClassification] with HasPretrained[RoBertaForSequenceClassification]

    

47. trait ReadablePretrainedRoBertaForTokenModel extends ParamsAndFeaturesReadable[RoBertaForTokenClassification] with HasPretrained[RoBertaForTokenClassification]

    

48. trait ReadablePretrainedSentimentDL extends ParamsAndFeaturesReadable[SentimentDLModel] with HasPretrained[SentimentDLModel]

    

49. trait ReadablePretrainedXlmRoBertaForSequenceModel extends ParamsAndFeaturesReadable[XlmRoBertaForSequenceClassification] with HasPretrained[XlmRoBertaForSequenceClassification]

    

50. trait ReadablePretrainedXlmRoBertaForTokenModel extends ParamsAndFeaturesReadable[XlmRoBertaForTokenClassification] with HasPretrained[XlmRoBertaForTokenClassification]

    

51. trait ReadablePretrainedXlnetForSequenceModel extends ParamsAndFeaturesReadable[XlnetForSequenceClassification] with HasPretrained[XlnetForSequenceClassification]

    

52. trait ReadablePretrainedXlnetForTokenModel extends ParamsAndFeaturesReadable[XlnetForTokenClassification] with HasPretrained[XlnetForTokenClassification]

    

53. class RoBertaForSequenceClassification extends AnnotatorModel[RoBertaForSequenceClassification] with HasBatchedAnnotate[RoBertaForSequenceClassification] with WriteTensorflowModel with HasCaseSensitiveProperties with HasClassifierActivationProperties

    

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

    RoBertaForSequenceClassification can load RoBERTa 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 = RoBertaForSequenceClassification.pretrained()
      .setInputCols("token", "document")
      .setOutputCol("label")

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

    For available pretrained models please see the Models Hub.

    To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669. and the RoBertaForSequenceClassification.

    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 = RoBertaForSequenceClassification.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

    Annotators Main Page for a list of transformer based classifiers

    RoBertaForSequenceClassification for sequence-level classification

54. class RoBertaForTokenClassification extends AnnotatorModel[RoBertaForTokenClassification] with HasBatchedAnnotate[RoBertaForTokenClassification] with WriteTensorflowModel with HasCaseSensitiveProperties

    

    RoBertaForTokenClassification can load RoBERTa Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g.

    RoBertaForTokenClassification can load RoBERTa Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.

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

    val tokenClassifier = RoBertaForTokenClassification.pretrained()
      .setInputCols("token", "document")
      .setOutputCol("label")

    The default model is "roberta_base_token_classifier_conll03", if no name is provided.

    For available pretrained models please see the Models Hub.

    and the RoBertaForTokenClassificationTestSpec. To see which models are compatible and how to import them see 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 tokenClassifier = RoBertaForTokenClassification.pretrained()
      .setInputCols("token", "document")
      .setOutputCol("label")
      .setCaseSensitive(true)
    
    val pipeline = new Pipeline().setStages(Array(
      documentAssembler,
      tokenizer,
      tokenClassifier
    ))
    
    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                                                                              |
    +------------------------------------------------------------------------------------+
    |[B-PER, I-PER, O, O, O, B-LOC, O, O, O, B-LOC, O, O, O, O, B-PER, O, O, O, O, B-LOC]|
    +------------------------------------------------------------------------------------+

    See also

    Annotators Main Page for a list of transformer based classifiers

    RoBertaForTokenClassification for token-level classification

55. class SentimentDLApproach extends AnnotatorApproach[SentimentDLModel] with ParamsAndFeaturesWritable

    

    Trains a SentimentDL, an annotator for multi-class sentiment analysis.

    Trains a SentimentDL, an annotator for multi-class sentiment analysis.

    In natural language processing, sentiment analysis is the task of classifying the affective state or subjective view of a text. A common example is if either a product review or tweet can be interpreted positively or negatively.

    For the instantiated/pretrained models, see SentimentDLModel.

    Notes:

    • This annotator accepts a label column of a single item in either type of String, Int, Float, or Double. So positive sentiment can be expressed as either "positive" or 0, negative sentiment as "negative" or 1.
    • UniversalSentenceEncoder, BertSentenceEmbeddings, or SentenceEmbeddings can be used for the inputCol.

    For extended examples of usage, see the Spark NLP Workshop and the SentimentDLTestSpec.

    Example

    In this example, sentiment.csv is in the form

    text,label
    This movie is the best movie I have watched ever! In my opinion this movie can win an award.,0
    This was a terrible movie! The acting was bad really bad!,1

    The model can then be trained with

    import com.johnsnowlabs.nlp.base.DocumentAssembler
    import com.johnsnowlabs.nlp.annotator.UniversalSentenceEncoder
    import com.johnsnowlabs.nlp.annotators.classifier.dl.{SentimentDLApproach, SentimentDLModel}
    import org.apache.spark.ml.Pipeline
    
    val smallCorpus = spark.read.option("header", "true").csv("src/test/resources/classifier/sentiment.csv")
    
    val documentAssembler = new DocumentAssembler()
      .setInputCol("text")
      .setOutputCol("document")
    
    val useEmbeddings = UniversalSentenceEncoder.pretrained()
      .setInputCols("document")
      .setOutputCol("sentence_embeddings")
    
    val docClassifier = new SentimentDLApproach()
      .setInputCols("sentence_embeddings")
      .setOutputCol("sentiment")
      .setLabelColumn("label")
      .setBatchSize(32)
      .setMaxEpochs(1)
      .setLr(5e-3f)
      .setDropout(0.5f)
    
    val pipeline = new Pipeline()
      .setStages(
        Array(
          documentAssembler,
          useEmbeddings,
          docClassifier
        )
      )
    
    val pipelineModel = pipeline.fit(smallCorpus)

    See also

    MultiClassifierDLApproach for general multi-class classification

    ClassifierDLApproach for general single-class classification

56. class SentimentDLModel extends AnnotatorModel[SentimentDLModel] with HasSimpleAnnotate[SentimentDLModel] with WriteTensorflowModel with HasStorageRef with ParamsAndFeaturesWritable

    

    SentimentDL, an annotator for multi-class sentiment analysis.

    SentimentDL, an annotator for multi-class sentiment analysis.

    In natural language processing, sentiment analysis is the task of classifying the affective state or subjective view of a text. A common example is if either a product review or tweet can be interpreted positively or negatively.

    This is the instantiated model of the SentimentDLApproach. For training your own model, please see the documentation of that class.

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

    val sentiment = SentimentDLModel.pretrained()
      .setInputCols("sentence_embeddings")
      .setOutputCol("sentiment")

    The default model is "sentimentdl_use_imdb", if no name is provided. It is english sentiment analysis trained on the IMDB dataset. For available pretrained models please see the Models Hub.

    For extended examples of usage, see the Spark NLP Workshop and the SentimentDLTestSpec.

    Example

    import spark.implicits._
    import com.johnsnowlabs.nlp.base.DocumentAssembler
    import com.johnsnowlabs.nlp.annotator.UniversalSentenceEncoder
    import com.johnsnowlabs.nlp.annotators.classifier.dl.SentimentDLModel
    import org.apache.spark.ml.Pipeline
    
    val documentAssembler = new DocumentAssembler()
      .setInputCol("text")
      .setOutputCol("document")
    
    val useEmbeddings = UniversalSentenceEncoder.pretrained()
      .setInputCols("document")
      .setOutputCol("sentence_embeddings")
    
    val sentiment = SentimentDLModel.pretrained("sentimentdl_use_twitter")
      .setInputCols("sentence_embeddings")
      .setThreshold(0.7F)
      .setOutputCol("sentiment")
    
    val pipeline = new Pipeline().setStages(Array(
      documentAssembler,
      useEmbeddings,
      sentiment
    ))
    
    val data = Seq(
      "Wow, the new video is awesome!",
      "bruh what a damn waste of time"
    ).toDF("text")
    val result = pipeline.fit(data).transform(data)
    
    result.select("text", "sentiment.result").show(false)
    +------------------------------+----------+
    |text                          |result    |
    +------------------------------+----------+
    |Wow, the new video is awesome!|[positive]|
    |bruh what a damn waste of time|[negative]|
    +------------------------------+----------+

    See also

    MultiClassifierDLModel for general multi-class classification

    ClassifierDLModel for general single-class classification

57. class XlmRoBertaForSequenceClassification extends AnnotatorModel[XlmRoBertaForSequenceClassification] with HasBatchedAnnotate[XlmRoBertaForSequenceClassification] with WriteTensorflowModel with WriteSentencePieceModel with HasCaseSensitiveProperties with HasClassifierActivationProperties

    

    XlmRoBertaForSequenceClassification can load XLM-RoBERTa Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g.

    XlmRoBertaForSequenceClassification can load XLM-RoBERTa 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 = XlmRoBertaForSequenceClassification.pretrained()
      .setInputCols("token", "document")
      .setOutputCol("label")

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

    For available pretrained models please see the Models Hub.

    To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669. and the XlmRoBertaForSequenceClassification.

    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 = XlmRoBertaForSequenceClassification.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

    Annotators Main Page for a list of transformer based classifiers

    XlmRoBertaForSequenceClassification for sequence-level classification

58. class XlmRoBertaForTokenClassification extends AnnotatorModel[XlmRoBertaForTokenClassification] with HasBatchedAnnotate[XlmRoBertaForTokenClassification] with WriteTensorflowModel with WriteSentencePieceModel with HasCaseSensitiveProperties

    

    XlmRoBertaForTokenClassification can load XLM-RoBERTa Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g.

    XlmRoBertaForTokenClassification can load XLM-RoBERTa Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.

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

    val tokenClassifier = XlmRoBertaForTokenClassification.pretrained()
      .setInputCols("token", "document")
      .setOutputCol("label")

    The default model is "xlm_roberta_base_token_classifier_conll03", if no name is provided.

    For available pretrained models please see the Models Hub.

    and the XlmRoBertaForTokenClassificationTestSpec. To see which models are compatible and how to import them see 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 tokenClassifier = XlmRoBertaForTokenClassification.pretrained()
      .setInputCols("token", "document")
      .setOutputCol("label")
      .setCaseSensitive(true)
    
    val pipeline = new Pipeline().setStages(Array(
      documentAssembler,
      tokenizer,
      tokenClassifier
    ))
    
    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                                                                              |
    +------------------------------------------------------------------------------------+
    |[B-PER, I-PER, O, O, O, B-LOC, O, O, O, B-LOC, O, O, O, O, B-PER, O, O, O, O, B-LOC]|
    +------------------------------------------------------------------------------------+

    See also

    Annotators Main Page for a list of transformer based classifiers

    XlmRoBertaForTokenClassification for token-level classification

59. class XlnetForSequenceClassification extends AnnotatorModel[XlnetForSequenceClassification] with HasBatchedAnnotate[XlnetForSequenceClassification] with WriteTensorflowModel with WriteSentencePieceModel with HasCaseSensitiveProperties with HasClassifierActivationProperties

    

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

    XlnetForSequenceClassification can load XLNet 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 = XlnetForSequenceClassification.pretrained()
      .setInputCols("token", "document")
      .setOutputCol("label")

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

    For available pretrained models please see the Models Hub.

    To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669. and the XlnetForSequenceClassification.

    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 = XlnetForSequenceClassification.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

    Annotators Main Page for a list of transformer based classifiers

    XlnetForSequenceClassification for sequence-level classification

60. class XlnetForTokenClassification extends AnnotatorModel[XlnetForTokenClassification] with HasBatchedAnnotate[XlnetForTokenClassification] with WriteTensorflowModel with WriteSentencePieceModel with HasCaseSensitiveProperties

    

    XlnetForTokenClassification can load XLNet Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g.

    XlnetForTokenClassification can load XLNet Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.

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

    val tokenClassifier = XlnetForTokenClassification.pretrained()
      .setInputCols("token", "document")
      .setOutputCol("label")

    The default model is "xlnet_base_token_classifier_conll03", if no name is provided.

    For available pretrained models please see the Models Hub.

    and the XlnetForTokenClassificationTestSpec. To see which models are compatible and how to import them see 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 tokenClassifier = XlnetForTokenClassification.pretrained()
      .setInputCols("token", "document")
      .setOutputCol("label")
      .setCaseSensitive(true)
    
    val pipeline = new Pipeline().setStages(Array(
      documentAssembler,
      tokenizer,
      tokenClassifier
    ))
    
    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                                                                              |
    +------------------------------------------------------------------------------------+
    |[B-PER, I-PER, O, O, O, B-LOC, O, O, O, B-LOC, O, O, O, O, B-PER, O, O, O, O, B-LOC]|
    +------------------------------------------------------------------------------------+

    See also

    Annotators Main Page for a list of transformer based classifiers

    XlnetForTokenClassification for token-level classification