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package dl

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  1. class AlbertForQuestionAnswering extends AnnotatorModel[AlbertForQuestionAnswering] with HasBatchedAnnotate[AlbertForQuestionAnswering] with WriteTensorflowModel with WriteOnnxModel with WriteSentencePieceModel with HasCaseSensitiveProperties with HasEngine

    AlbertForQuestionAnswering can load ALBERT Models with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute span start logits and span end logits).

    AlbertForQuestionAnswering can load ALBERT Models with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute span start logits and span end logits).

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

    val spanClassifier = AlbertForQuestionAnswering.pretrained()
      .setInputCols(Array("document_question", "document_context"))
      .setOutputCol("answer")

    The default model is "albert_base_qa_squad2", 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 to see more extended examples, see AlbertForQuestionAnsweringTestSpec.

    Example

    import spark.implicits._
    import com.johnsnowlabs.nlp.base._
    import com.johnsnowlabs.nlp.annotator._
    import org.apache.spark.ml.Pipeline
    
    val document = new MultiDocumentAssembler()
      .setInputCols("question", "context")
      .setOutputCols("document_question", "document_context")
    
    val questionAnswering = AlbertForQuestionAnswering.pretrained()
      .setInputCols(Array("document_question", "document_context"))
      .setOutputCol("answer")
      .setCaseSensitive(false)
    
    val pipeline = new Pipeline().setStages(Array(
      document,
      questionAnswering
    ))
    
    val data = Seq("What's my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context")
    val result = pipeline.fit(data).transform(data)
    
    result.select("label.result").show(false)
    +---------------------+
    |result               |
    +---------------------+
    |[Clara]              |
    ++--------------------+
    See also

    AlbertForSequenceClassification for sequence-level classification

    Annotators Main Page for a list of transformer based classifiers

  2. class AlbertForSequenceClassification extends AnnotatorModel[AlbertForSequenceClassification] with HasBatchedAnnotate[AlbertForSequenceClassification] with WriteTensorflowModel with WriteOnnxModel with WriteSentencePieceModel with HasCaseSensitiveProperties with HasClassifierActivationProperties with HasEngine

    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 to see more extended examples, see 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("I loved this movie when I was a child.", "It was pretty boring.").toDF("text")
    val result = pipeline.fit(data).transform(data)
    
    result.select("label.result").show(false)
    +------+
    |result|
    +------+
    |[pos] |
    |[neg] |
    +------+
    See also

    AlbertForSequenceClassification for sequence-level classification

    Annotators Main Page for a list of transformer based classifiers

  3. class AlbertForTokenClassification extends AnnotatorModel[AlbertForTokenClassification] with HasBatchedAnnotate[AlbertForTokenClassification] with WriteTensorflowModel with WriteOnnxModel with WriteSentencePieceModel with HasCaseSensitiveProperties with HasEngine

    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

    AlbertForTokenClassification for token-level classification

    Annotators Main Page for a list of transformer based classifiers

  4. class BartForZeroShotClassification extends AnnotatorModel[BartForZeroShotClassification] with HasBatchedAnnotate[BartForZeroShotClassification] with WriteTensorflowModel with HasCaseSensitiveProperties with HasClassifierActivationProperties with HasEngine with HasCandidateLabelsProperties

    BartForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks.

    BartForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks. Equivalent of BartForZeroShotClassification models, but these models don't require a hardcoded number of potential classes, they can be chosen at runtime. It usually means it's slower but it is much more flexible.

    Note that the model will loop through all provided labels. So the more labels you have, the longer this process will take.

    Any combination of sequences and labels can be passed and each combination will be posed as a premise/hypothesis pair and passed to the pretrained model.

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

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

    The default model is "bart_large_zero_shot_classifier_mnli", 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.

    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 = BartForZeroShotClassification .pretrained()
      .setInputCols("token", "document")
      .setOutputCol("label")
      .setCaseSensitive(true)
    
    val pipeline = new Pipeline().setStages(Array(
      documentAssembler,
      tokenizer,
      sequenceClassifier
    ))
    
    val data = Seq("I loved this movie when I was a child.", "It was pretty boring.").toDF("text")
    val result = pipeline.fit(data).transform(data)
    
    result.select("label.result").show(false)
    +------+
    |result|
    +------+
    |[pos] |
    |[neg] |
    +------+
    See also

    BartForZeroShotClassification for sequence-level classification

    Annotators Main Page for a list of transformer based classifiers

  5. class BertForQuestionAnswering extends AnnotatorModel[BertForQuestionAnswering] with HasBatchedAnnotate[BertForQuestionAnswering] with WriteTensorflowModel with WriteOnnxModel with HasCaseSensitiveProperties with HasEngine

    BertForQuestionAnswering can load Bert Models with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute span start logits and span end logits).

    BertForQuestionAnswering can load Bert Models with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute span start logits and span end logits).

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

    val spanClassifier = BertForQuestionAnswering.pretrained()
      .setInputCols(Array("document_question", "document_context"))
      .setOutputCol("answer")

    The default model is "bert_base_cased_qa_squad2", 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 🚀. To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see BertForQuestionAnsweringTestSpec.

    Example

    import spark.implicits._
    import com.johnsnowlabs.nlp.base._
    import com.johnsnowlabs.nlp.annotator._
    import org.apache.spark.ml.Pipeline
    
    val document = new MultiDocumentAssembler()
      .setInputCols("question", "context")
      .setOutputCols("document_question", "document_context")
    
    val questionAnswering = BertForQuestionAnswering.pretrained()
      .setInputCols(Array("document_question", "document_context"))
      .setOutputCol("answer")
      .setCaseSensitive(true)
    
    val pipeline = new Pipeline().setStages(Array(
      document,
      questionAnswering
    ))
    
    val data = Seq("What's my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context")
    val result = pipeline.fit(data).transform(data)
    
    result.select("label.result").show(false)
    +---------------------+
    |result               |
    +---------------------+
    |[Clara]              |
    ++--------------------+
    See also

    BertForSequenceClassification for span-level classification

    Annotators Main Page for a list of transformer based classifiers

  6. class BertForSequenceClassification extends AnnotatorModel[BertForSequenceClassification] with HasBatchedAnnotate[BertForSequenceClassification] with WriteTensorflowModel with WriteOnnxModel with HasCaseSensitiveProperties with HasClassifierActivationProperties with HasEngine

    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 to see more extended examples, see 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("I loved this movie when I was a child.", "It was pretty boring.").toDF("text")
    val result = pipeline.fit(data).transform(data)
    
    result.select("label.result").show(false)
    +------+
    |result|
    +------+
    |[pos] |
    |[neg] |
    +------+
    See also

    BertForSequenceClassification for sequence-level classification

    Annotators Main Page for a list of transformer based classifiers

  7. class BertForTokenClassification extends AnnotatorModel[BertForTokenClassification] with HasBatchedAnnotate[BertForTokenClassification] with WriteTensorflowModel with WriteOnnxModel with HasCaseSensitiveProperties with HasEngine

    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 to see more extended examples, see 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

    BertForTokenClassification for token-level classification

    Annotators Main Page for a list of transformer based classifiers

  8. class BertForZeroShotClassification extends AnnotatorModel[BertForZeroShotClassification] with HasBatchedAnnotate[BertForZeroShotClassification] with WriteTensorflowModel with WriteOnnxModel with HasCaseSensitiveProperties with HasClassifierActivationProperties with HasEngine with HasCandidateLabelsProperties

    BertForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks.

    BertForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks. Equivalent of BertForSequenceClassification models, but these models don't require a hardcoded number of potential classes, they can be chosen at runtime. It usually means it's slower but it is much more flexible.

    Note that the model will loop through all provided labels. So the more labels you have, the longer this process will take.

    Any combination of sequences and labels can be passed and each combination will be posed as a premise/hypothesis pair and passed to the pretrained model.

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

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

    The default model is "bert_zero_shot_classifier_mnli", 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.

    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 = BertForZeroShotClassification.pretrained()
      .setInputCols("token", "document")
      .setOutputCol("label")
      .setCaseSensitive(true)
    
    val pipeline = new Pipeline().setStages(Array(
      documentAssembler,
      tokenizer,
      sequenceClassifier
    ))
    
    val data = Seq("I loved this movie when I was a child.", "It was pretty boring.").toDF("text")
    val result = pipeline.fit(data).transform(data)
    
    result.select("label.result").show(false)
    +------+
    |result|
    +------+
    |[pos] |
    |[neg] |
    +------+
    See also

    BertForZeroShotClassification for sequence-level classification

    Annotators Main Page for a list of transformer based classifiers

  9. class CamemBertForQuestionAnswering extends AnnotatorModel[CamemBertForQuestionAnswering] with HasBatchedAnnotate[CamemBertForQuestionAnswering] with WriteTensorflowModel with WriteOnnxModel with WriteSentencePieceModel with HasCaseSensitiveProperties with HasEngine

    CamemBertForQuestionAnswering can load CamemBERT Models with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute span start logits and span end logits).

    CamemBertForQuestionAnswering can load CamemBERT Models with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute span start logits and span end logits).

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

    val spanClassifier = CamemBertForQuestionAnswering.pretrained()
      .setInputCols(Array("document_question", "document_context"))
      .setOutputCol("answer")

    The default model is "camembert_base_qa_fquad", 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 to see more extended examples, see CamemBertForQuestionAnsweringTestSpec.

    Example

    import spark.implicits._
    import com.johnsnowlabs.nlp.base._
    import com.johnsnowlabs.nlp.annotator._
    import org.apache.spark.ml.Pipeline
    
    val document = new MultiDocumentAssembler()
      .setInputCols("question", "context")
      .setOutputCols("document_question", "document_context")
    
    val questionAnswering = CamemBertForQuestionAnswering.pretrained()
      .setInputCols(Array("document_question", "document_context"))
      .setOutputCol("answer")
      .setCaseSensitive(true)
    
    val pipeline = new Pipeline().setStages(Array(
      document,
      questionAnswering
    ))
    
    val data = Seq("What's my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context")
    val result = pipeline.fit(data).transform(data)
    
    result.select("label.result").show(false)
    +---------------------+
    |result               |
    +---------------------+
    |[Clara]              |
    ++--------------------+
    See also

    CamemBertForQuestionAnswering for sequence-level classification

    Annotators Main Page for a list of transformer based classifiers

  10. class CamemBertForSequenceClassification extends AnnotatorModel[CamemBertForSequenceClassification] with HasBatchedAnnotate[CamemBertForSequenceClassification] with WriteTensorflowModel with WriteOnnxModel with WriteSentencePieceModel with HasCaseSensitiveProperties with HasClassifierActivationProperties with HasEngine

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

    CamemBertForSequenceClassification can load CamemBERT 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 = CamemBertForSequenceClassification.pretrained()
      .setInputCols("token", "document")
      .setOutputCol("label")

    The default model is camembert_base_sequence_classifier_allocine", 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 to see more extended examples, see CamemBertForSequenceClassification.

    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 = CamemBertForSequenceClassification.pretrained()
      .setInputCols("token", "document")
      .setOutputCol("label")
      .setCaseSensitive(true)
    
    val pipeline = new Pipeline().setStages(Array(
      documentAssembler,
      tokenizer,
      sequenceClassifier
    ))
    
    val data = Seq("j'ai adoré ce film lorsque j'étais enfant.", "Je déteste ça.").toDF("text")
    val result = pipeline.fit(data).transform(data)
    
    result.select("label.result").show(false)
    +------+
    |result|
    +------+
    |[pos] |
    |[neg] |
    +------+
    See also

    CamemBertForSequenceClassification for sequence-level classification

    Annotators Main Page for a list of transformer based classifiers

  11. class CamemBertForTokenClassification extends AnnotatorModel[CamemBertForTokenClassification] with HasBatchedAnnotate[CamemBertForTokenClassification] with WriteTensorflowModel with WriteOnnxModel with WriteSentencePieceModel with HasCaseSensitiveProperties with HasEngine

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

    CamemBertForTokenClassification can load CamemBERT 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 = CamemBertForTokenClassification.pretrained()
      .setInputCols("token", "document")
      .setOutputCol("label")

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

    For available pretrained models please see the Models Hub.

    and the CamemBertForTokenClassificationTestSpec. 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 = CamemBertForTokenClassification.pretrained()
      .setInputCols("token", "document")
      .setOutputCol("label")
      .setCaseSensitive(true)
    
    val pipeline = new Pipeline().setStages(Array(
      documentAssembler,
      tokenizer,
      tokenClassifier
    ))
    
    val data = Seq("george washington est allé à washington").toDF("text")
    val result = pipeline.fit(data).transform(data)
    
    result.select("label.result").show(false)
    +------------------------------+
    |result                        |
    +------------------------------+
    |[I-PER, I-PER, O, O, O, I-LOC]|
    +------------------------------+
    See also

    CamemBertForTokenClassification for token-level classification

    Annotators Main Page for a list of transformer based classifiers

  12. class ClassifierDLApproach extends AnnotatorApproach[ClassifierDLModel] with ParamsAndFeaturesWritable with ClassifierEncoder

    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:

    Setting a test dataset to monitor model metrics can be done with .setTestDataset. The method expects a path to a parquet file containing a dataframe that has the same required columns as the training dataframe. The pre-processing steps for the training dataframe should also be applied to the test dataframe. The following example will show how to create the test dataset:

    val documentAssembler = new DocumentAssembler()
      .setInputCol("text")
      .setOutputCol("document")
    
    val embeddings = UniversalSentenceEncoder.pretrained()
      .setInputCols("document")
      .setOutputCol("sentence_embeddings")
    
    val preProcessingPipeline = new Pipeline().setStages(Array(documentAssembler, embeddings))
    
    val Array(train, test) = data.randomSplit(Array(0.8, 0.2))
    preProcessingPipeline
      .fit(test)
      .transform(test)
      .write
      .mode("overwrite")
      .parquet("test_data")
    
    val classifier = new ClassifierDLApproach()
      .setInputCols("sentence_embeddings")
      .setOutputCol("category")
      .setLabelColumn("label")
      .setTestDataset("test_data")

    For extended examples of usage, see the Examples [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

    MultiClassifierDLApproach for multi-class classification

    SentimentDLApproach for sentiment analysis

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

    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 Examples 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

    MultiClassifierDLModel for multi-class classification

    SentimentDLModel for sentiment analysis

  14. trait ClassifierEncoder extends EvaluationDLParams
  15. trait ClassifierMetrics extends Logging
  16. class DeBertaForQuestionAnswering extends AnnotatorModel[DeBertaForQuestionAnswering] with HasBatchedAnnotate[DeBertaForQuestionAnswering] with WriteTensorflowModel with WriteOnnxModel with WriteSentencePieceModel with HasCaseSensitiveProperties with HasEngine

    DeBertaForQuestionAnswering can load DeBERTa Models with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute span start logits and span end logits).

    DeBertaForQuestionAnswering can load DeBERTa Models with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute span start logits and span end logits).

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

    val spanClassifier = DeBertaForQuestionAnswering.pretrained()
      .setInputCols(Array("document_question", "document_context"))
      .setOutputCol("answer")

    The default model is "deberta_v3_xsmall_qa_squad2", 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 to see more extended examples, see DeBertaForQuestionAnsweringTestSpec.

    Example

    import spark.implicits._
    import com.johnsnowlabs.nlp.base._
    import com.johnsnowlabs.nlp.annotator._
    import org.apache.spark.ml.Pipeline
    
    val document = new MultiDocumentAssembler()
      .setInputCols("question", "context")
      .setOutputCols("document_question", "document_context")
    
    val questionAnswering = DeBertaForQuestionAnswering.pretrained()
      .setInputCols(Array("document_question", "document_context"))
      .setOutputCol("answer")
      .setCaseSensitive(true)
    
    val pipeline = new Pipeline().setStages(Array(
      document,
      questionAnswering
    ))
    
    val data = Seq("What's my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context")
    val result = pipeline.fit(data).transform(data)
    
    result.select("label.result").show(false)
    +---------------------+
    |result               |
    +---------------------+
    |[Clara]              |
    ++--------------------+
    See also

    DeBertaForQuestionAnswering for span-level classification

    Annotators Main Page for a list of transformer based classifiers

  17. class DeBertaForSequenceClassification extends AnnotatorModel[DeBertaForSequenceClassification] with HasBatchedAnnotate[DeBertaForSequenceClassification] with WriteOnnxModel with WriteTensorflowModel with WriteSentencePieceModel with HasCaseSensitiveProperties with HasClassifierActivationProperties with HasEngine

    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 to see more extended examples, see 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("I loved this movie when I was a child.", "It was pretty boring.").toDF("text")
    val result = pipeline.fit(data).transform(data)
    
    result.select("label.result").show(false)
    +------+
    |result|
    +------+
    |[pos] |
    |[neg] |
    +------+
    See also

    DeBertaForSequenceClassification for sequence-level classification

    Annotators Main Page for a list of transformer based classifiers

  18. class DeBertaForTokenClassification extends AnnotatorModel[DeBertaForTokenClassification] with HasBatchedAnnotate[DeBertaForTokenClassification] with WriteTensorflowModel with WriteOnnxModel with WriteSentencePieceModel with HasCaseSensitiveProperties with HasEngine

    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_v3_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 🚀. 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 = 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

    DeBertaForTokenClassification for token-level classification

    Annotators Main Page for a list of transformer based classifiers

  19. class DeBertaForZeroShotClassification extends AnnotatorModel[DeBertaForZeroShotClassification] with HasBatchedAnnotate[DeBertaForZeroShotClassification] with WriteTensorflowModel with WriteSentencePieceModel with HasCaseSensitiveProperties with HasClassifierActivationProperties with HasEngine with HasCandidateLabelsProperties

    DeBertaForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks.

    DeBertaForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks. Equivalent of DeBertaForZeroShotClassification models, but these models don't require a hardcoded number of potential classes, they can be chosen at runtime. It usually means it's slower but it is much more flexible.

    Note that the model will loop through all provided labels. So the more labels you have, the longer this process will take.

    Any combination of sequences and labels can be passed and each combination will be posed as a premise/hypothesis pair and passed to the pretrained model.

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

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

    The default model is "deberta_base_zero_shot_classifier_mnli_anli_v3", 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.

    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 = DeBertaForZeroShotClassification .pretrained()
      .setInputCols("token", "document")
      .setOutputCol("label")
      .setCaseSensitive(true)
    
    val pipeline = new Pipeline().setStages(Array(
      documentAssembler,
      tokenizer,
      sequenceClassifier
    ))
    
    val data = Seq("I loved this movie when I was a child.", "It was pretty boring.").toDF("text")
    val result = pipeline.fit(data).transform(data)
    
    result.select("label.result").show(false)
    +------+
    |result|
    +------+
    |[pos] |
    |[neg] |
    +------+
    See also

    DeBertaForZeroShotClassification for sequence-level classification

    Annotators Main Page for a list of transformer based classifiers

  20. class DistilBertForQuestionAnswering extends AnnotatorModel[DistilBertForQuestionAnswering] with HasBatchedAnnotate[DistilBertForQuestionAnswering] with WriteTensorflowModel with WriteOnnxModel with HasCaseSensitiveProperties with HasEngine

    DistilBertForQuestionAnswering can load DistilBert Models with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute span start logits and span end logits).

    DistilBertForQuestionAnswering can load DistilBert Models with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute span start logits and span end logits).

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

    val spanClassifier = DistilBertForQuestionAnswering.pretrained()
      .setInputCols(Array("document_question", "document_context"))
      .setOutputCol("answer")

    The default model is "distilbert_base_cased_qa_squad2", 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 to see more extended examples, see DistilBertForSequenceClassificationTestSpec.

    Example

    import spark.implicits._
    import com.johnsnowlabs.nlp.base._
    import com.johnsnowlabs.nlp.annotator._
    import org.apache.spark.ml.Pipeline
    
    val document = new MultiDocumentAssembler()
      .setInputCols("question", "context")
      .setOutputCols("document_question", "document_context")
    
    val questionAnswering = DistilBertForQuestionAnswering.pretrained()
      .setInputCols(Array("document_question", "document_context"))
      .setOutputCol("answer")
      .setCaseSensitive(true)
    
    val pipeline = new Pipeline().setStages(Array(
      document,
      questionAnswering
    ))
    
    val data = Seq("What's my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context")
    val result = pipeline.fit(data).transform(data)
    
    result.select("label.result").show(false)
    +---------------------+
    |result               |
    +---------------------+
    |[Clara]              |
    ++--------------------+
    See also

    DistilBertForSequenceClassification for sequence-level classification

    Annotators Main Page for a list of transformer based classifiers

  21. class DistilBertForSequenceClassification extends AnnotatorModel[DistilBertForSequenceClassification] with HasBatchedAnnotate[DistilBertForSequenceClassification] with WriteTensorflowModel with WriteOnnxModel with HasCaseSensitiveProperties with HasClassifierActivationProperties with HasEngine

    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 to see more extended examples, see 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("I loved this movie when I was a child.", "It was pretty boring.").toDF("text")
    val result = pipeline.fit(data).transform(data)
    
    result.select("label.result").show(false)
    +------+
    |result|
    +------+
    |[pos] |
    |[neg] |
    +------+
    See also

    DistilBertForSequenceClassification for sequence-level classification

    Annotators Main Page for a list of transformer based classifiers

  22. class DistilBertForTokenClassification extends AnnotatorModel[DistilBertForTokenClassification] with HasBatchedAnnotate[DistilBertForTokenClassification] with WriteTensorflowModel with WriteOnnxModel with HasCaseSensitiveProperties with HasEngine

    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 to see more extended examples, see 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

    DistilBertForTokenClassification for token-level classification

    Annotators Main Page for a list of transformer based classifiers

  23. class DistilBertForZeroShotClassification extends AnnotatorModel[DistilBertForZeroShotClassification] with HasBatchedAnnotate[DistilBertForZeroShotClassification] with WriteTensorflowModel with HasCaseSensitiveProperties with HasClassifierActivationProperties with HasEngine with HasCandidateLabelsProperties

    DistilBertForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks.

    DistilBertForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks. Equivalent of DistilBertForZeroShotClassification models, but these models don't require a hardcoded number of potential classes, they can be chosen at runtime. It usually means it's slower but it is much more flexible.

    Note that the model will loop through all provided labels. So the more labels you have, the longer this process will take.

    Any combination of sequences and labels can be passed and each combination will be posed as a premise/hypothesis pair and passed to the pretrained model.

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

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

    The default model is "distilbert_base_zero_shot_classifier_uncased_mnli", 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.

    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 = DistilBertForZeroShotClassification .pretrained()
      .setInputCols("token", "document")
      .setOutputCol("label")
      .setCaseSensitive(true)
    
    val pipeline = new Pipeline().setStages(Array(
      documentAssembler,
      tokenizer,
      sequenceClassifier
    ))
    
    val data = Seq("I loved this movie when I was a child.", "It was pretty boring.").toDF("text")
    val result = pipeline.fit(data).transform(data)
    
    result.select("label.result").show(false)
    +------+
    |result|
    +------+
    |[pos] |
    |[neg] |
    +------+
    See also

    DistilBertForZeroShotClassification for sequence-level classification

    Annotators Main Page for a list of transformer based classifiers

  24. class LongformerForQuestionAnswering extends AnnotatorModel[LongformerForQuestionAnswering] with HasBatchedAnnotate[LongformerForQuestionAnswering] with WriteTensorflowModel with HasCaseSensitiveProperties with HasEngine

    LongformerForQuestionAnswering can load Longformer Models with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute span start logits and span end logits).

    LongformerForQuestionAnswering can load Longformer Models with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute span start logits and span end logits).

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

    val spanClassifier = LongformerForQuestionAnswering.pretrained()
      .setInputCols(Array("document_question", "document_context"))
      .setOutputCol("answer")

    The default model is "longformer_base_base_qa_squad2", 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 to see more extended examples, see LongformerForQuestionAnsweringTestSpec.

    Example

    import spark.implicits._
    import com.johnsnowlabs.nlp.base._
    import com.johnsnowlabs.nlp.annotator._
    import org.apache.spark.ml.Pipeline
    
    val document = new MultiDocumentAssembler()
      .setInputCols("question", "context")
      .setOutputCols("document_question", "document_context")
    
    val questionAnswering = LongformerForQuestionAnswering.pretrained()
      .setInputCols(Array("document_question", "document_context"))
      .setOutputCol("answer")
      .setCaseSensitive(true)
    
    val pipeline = new Pipeline().setStages(Array(
      document,
      questionAnswering
    ))
    
    val data = Seq("What's my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context")
    val result = pipeline.fit(data).transform(data)
    
    result.select("label.result").show(false)
    +---------------------+
    |result               |
    +---------------------+
    |[Clara]              |
    ++--------------------+
    See also

    LongformerForSequenceClassification for sequence-level classification

    Annotators Main Page for a list of transformer based classifiers

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

    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 to see more extended examples, see 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("I loved this movie when I was a child.", "It was pretty boring.").toDF("text")
    val result = pipeline.fit(data).transform(data)
    
    result.select("label.result").show(false)
    +------+
    |result|
    +------+
    |[pos] |
    |[neg] |
    +------+
    See also

    LongformerForSequenceClassification for sequence-level classification

    Annotators Main Page for a list of transformer based classifiers

  26. class LongformerForTokenClassification extends AnnotatorModel[LongformerForTokenClassification] with HasBatchedAnnotate[LongformerForTokenClassification] with WriteTensorflowModel with HasCaseSensitiveProperties with HasEngine

    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

    LongformerForTokenClassification for token-level classification

    Annotators Main Page for a list of transformer based classifiers

  27. class MPNetForQuestionAnswering extends AnnotatorModel[MPNetForQuestionAnswering] with HasBatchedAnnotate[MPNetForQuestionAnswering] with WriteOnnxModel with HasCaseSensitiveProperties with HasEngine

    MPNetForQuestionAnswering can load MPNet Models with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute span start logits and span end logits).

    MPNetForQuestionAnswering can load MPNet Models with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute span start logits and span end logits).

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

    val spanClassifier = MPNetForQuestionAnswering.pretrained()
      .setInputCols(Array("document_question", "document_context"))
      .setOutputCol("answer")

    The default model is "mpnet_base_question_answering_squad2", 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 to see more extended examples, see MPNetForQuestionAnsweringTestSpec.

    Example

    import spark.implicits._
    import com.johnsnowlabs.nlp.base._
    import com.johnsnowlabs.nlp.annotator._
    import org.apache.spark.ml.Pipeline
    
    val document = new MultiDocumentAssembler()
      .setInputCols("question", "context")
      .setOutputCols("document_question", "document_context")
    
    val questionAnswering = MPNetForQuestionAnswering.pretrained()
      .setInputCols(Array("document_question", "document_context"))
      .setOutputCol("answer")
      .setCaseSensitive(true)
    
    val pipeline = new Pipeline().setStages(Array(
      document,
      questionAnswering
    ))
    
    val data = Seq("What's my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context")
    val result = pipeline.fit(data).transform(data)
    
    result.select("label.result").show(false)
    +---------------------+
    |result               |
    +---------------------+
    |[Clara]              |
    ++--------------------+
    See also

    MPNetForSequenceClassification for sequence-level classification

    Annotators Main Page for a list of transformer based classifiers

  28. class MPNetForSequenceClassification extends AnnotatorModel[MPNetForSequenceClassification] with HasBatchedAnnotate[MPNetForSequenceClassification] with WriteOnnxModel with HasCaseSensitiveProperties with HasClassifierActivationProperties with HasEngine

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

    MPNetForSequenceClassification can load MPNet 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.

    Note that currently, only SetFit models can be imported.

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

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

    The default model is "mpnet_sequence_classifier_ukr_message", 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 to see more extended examples, see MPNetForSequenceClassificationTestSpec.

    Example

    import com.johnsnowlabs.nlp.base._
    import com.johnsnowlabs.nlp.annotator._
    import org.apache.spark.ml.Pipeline
    import spark.implicits._
    
    val document = new DocumentAssembler()
      .setInputCol("text")
      .setOutputCol("document")
    
    val tokenizer = new Tokenizer()
      .setInputCols(Array("document"))
      .setOutputCol("token")
    
    val sequenceClassifier = MPNetForSequenceClassification
      .pretrained()
      .setInputCols(Array("document", "token"))
      .setOutputCol("label")
    
    val texts = Seq(
      "I love driving my car.",
      "The next bus will arrive in 20 minutes.",
      "pineapple on pizza is the worst 🤮")
    val data = texts.toDF("text")
    
    val pipeline = new Pipeline().setStages(Array(document, tokenizer, sequenceClassifier))
    val pipelineModel = pipeline.fit(data)
    val results = pipelineModel.transform(data)
    
    results.select("label.result").show()
    +--------------------+
    |              result|
    +--------------------+
    |     [TRANSPORT/CAR]|
    |[TRANSPORT/MOVEMENT]|
    |              [FOOD]|
    +--------------------+
    See also

    MPNetForSequenceClassification for sequence-level classification

    Annotators Main Page for a list of transformer based classifiers

  29. class MultiClassifierDLApproach extends AnnotatorApproach[MultiClassifierDLModel] with ParamsAndFeaturesWritable with ClassifierEncoder

    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:

    Setting a test dataset to monitor model metrics can be done with .setTestDataset. The method expects a path to a parquet file containing a dataframe that has the same required columns as the training dataframe. The pre-processing steps for the training dataframe should also be applied to the test dataframe. The following example will show how to create the test dataset:

    val documentAssembler = new DocumentAssembler()
      .setInputCol("text")
      .setOutputCol("document")
    
    val embeddings = UniversalSentenceEncoder.pretrained()
      .setInputCols("document")
      .setOutputCol("sentence_embeddings")
    
    val preProcessingPipeline = new Pipeline().setStages(Array(documentAssembler, embeddings))
    
    val Array(train, test) = data.randomSplit(Array(0.8, 0.2))
    preProcessingPipeline
      .fit(test)
      .transform(test)
      .write
      .mode("overwrite")
      .parquet("test_data")
    
    val multiClassifier = new MultiClassifierDLApproach()
      .setInputCols("sentence_embeddings")
      .setOutputCol("category")
      .setLabelColumn("label")
      .setTestDataset("test_data")

    For extended examples of usage, see the Examples 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

    Multi-label classification on Wikipedia

    ClassifierDLApproach for single-class classification

    SentimentDLApproach for sentiment analysis

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

    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 Examples 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

    Multi-label classification on Wikipedia

    ClassifierDLModel for single-class classification

    SentimentDLModel for sentiment analysis

  31. trait ReadAlbertForQuestionAnsweringDLModel extends ReadTensorflowModel with ReadOnnxModel with ReadSentencePieceModel
  32. trait ReadAlbertForSequenceDLModel extends ReadTensorflowModel with ReadOnnxModel with ReadSentencePieceModel
  33. trait ReadAlbertForTokenDLModel extends ReadTensorflowModel with ReadOnnxModel with ReadSentencePieceModel
  34. trait ReadBartForZeroShotDLModel extends ReadTensorflowModel
  35. trait ReadBertForQuestionAnsweringDLModel extends ReadTensorflowModel with ReadOnnxModel
  36. trait ReadBertForSequenceDLModel extends ReadTensorflowModel with ReadOnnxModel
  37. trait ReadBertForTokenDLModel extends ReadTensorflowModel with ReadOnnxModel
  38. trait ReadBertForZeroShotDLModel extends ReadTensorflowModel with ReadOnnxModel
  39. trait ReadCamemBertForQADLModel extends ReadTensorflowModel with ReadOnnxModel with ReadSentencePieceModel
  40. trait ReadCamemBertForSequenceDLModel extends ReadTensorflowModel with ReadOnnxModel with ReadSentencePieceModel
  41. trait ReadCamemBertForTokenDLModel extends ReadTensorflowModel with ReadOnnxModel with ReadSentencePieceModel
  42. trait ReadClassifierDLTensorflowModel extends ReadTensorflowModel
  43. trait ReadDeBertaForQuestionAnsweringDLModel extends ReadTensorflowModel with ReadOnnxModel with ReadSentencePieceModel
  44. trait ReadDeBertaForSequenceDLModel extends ReadTensorflowModel with ReadOnnxModel with ReadSentencePieceModel
  45. trait ReadDeBertaForTokenDLModel extends ReadTensorflowModel with ReadOnnxModel with ReadSentencePieceModel
  46. trait ReadDeBertaForZeroShotDLModel extends ReadTensorflowModel with ReadSentencePieceModel
  47. trait ReadDistilBertForQuestionAnsweringDLModel extends ReadTensorflowModel with ReadOnnxModel
  48. trait ReadDistilBertForSequenceDLModel extends ReadTensorflowModel with ReadOnnxModel
  49. trait ReadDistilBertForTokenDLModel extends ReadTensorflowModel with ReadOnnxModel
  50. trait ReadDistilBertForZeroShotDLModel extends ReadTensorflowModel
  51. trait ReadLongformerForQuestionAnsweringDLModel extends ReadTensorflowModel
  52. trait ReadLongformerForSequenceDLModel extends ReadTensorflowModel
  53. trait ReadLongformerForTokenDLModel extends ReadTensorflowModel
  54. trait ReadMPNetForQuestionAnsweringDLModel extends ReadOnnxModel
  55. trait ReadMPNetForSequenceDLModel extends ReadOnnxModel
  56. trait ReadMultiClassifierDLTensorflowModel extends ReadTensorflowModel
  57. trait ReadRoBertaForQuestionAnsweringDLModel extends ReadTensorflowModel with ReadOnnxModel
  58. trait ReadRoBertaForSequenceDLModel extends ReadTensorflowModel with ReadOnnxModel
  59. trait ReadRoBertaForTokenDLModel extends ReadTensorflowModel with ReadOnnxModel
  60. trait ReadRoBertaForZeroShotDLModel extends ReadTensorflowModel
  61. trait ReadSentimentDLTensorflowModel extends ReadTensorflowModel
  62. trait ReadTapasForQuestionAnsweringDLModel extends ReadTensorflowModel
  63. trait ReadXlmRoBertaForQuestionAnsweringDLModel extends ReadTensorflowModel with ReadOnnxModel with ReadSentencePieceModel
  64. trait ReadXlmRoBertaForSequenceDLModel extends ReadTensorflowModel with ReadOnnxModel with ReadSentencePieceModel
  65. trait ReadXlmRoBertaForTokenDLModel extends ReadTensorflowModel with ReadOnnxModel with ReadSentencePieceModel
  66. trait ReadXlmRoBertaForZeroShotDLModel extends ReadTensorflowModel with ReadSentencePieceModel
  67. trait ReadXlnetForSequenceDLModel extends ReadTensorflowModel with ReadSentencePieceModel
  68. trait ReadXlnetForTokenDLModel extends ReadTensorflowModel with ReadSentencePieceModel
  69. trait ReadablePretrainedAlbertForQAModel extends ParamsAndFeaturesReadable[AlbertForQuestionAnswering] with HasPretrained[AlbertForQuestionAnswering]
  70. trait ReadablePretrainedAlbertForSequenceModel extends ParamsAndFeaturesReadable[AlbertForSequenceClassification] with HasPretrained[AlbertForSequenceClassification]
  71. trait ReadablePretrainedAlbertForTokenModel extends ParamsAndFeaturesReadable[AlbertForTokenClassification] with HasPretrained[AlbertForTokenClassification]
  72. trait ReadablePretrainedBartForZeroShotModel extends ParamsAndFeaturesReadable[BartForZeroShotClassification] with HasPretrained[BartForZeroShotClassification]
  73. trait ReadablePretrainedBertForQAModel extends ParamsAndFeaturesReadable[BertForQuestionAnswering] with HasPretrained[BertForQuestionAnswering]
  74. trait ReadablePretrainedBertForSequenceModel extends ParamsAndFeaturesReadable[BertForSequenceClassification] with HasPretrained[BertForSequenceClassification]
  75. trait ReadablePretrainedBertForTokenModel extends ParamsAndFeaturesReadable[BertForTokenClassification] with HasPretrained[BertForTokenClassification]
  76. trait ReadablePretrainedBertForZeroShotModel extends ParamsAndFeaturesReadable[BertForZeroShotClassification] with HasPretrained[BertForZeroShotClassification]
  77. trait ReadablePretrainedCamemBertForQAModel extends ParamsAndFeaturesReadable[CamemBertForQuestionAnswering] with HasPretrained[CamemBertForQuestionAnswering]
  78. trait ReadablePretrainedCamemBertForSequenceModel extends ParamsAndFeaturesReadable[CamemBertForSequenceClassification] with HasPretrained[CamemBertForSequenceClassification]
  79. trait ReadablePretrainedCamemBertForTokenModel extends ParamsAndFeaturesReadable[CamemBertForTokenClassification] with HasPretrained[CamemBertForTokenClassification]
  80. trait ReadablePretrainedClassifierDL extends ParamsAndFeaturesReadable[ClassifierDLModel] with HasPretrained[ClassifierDLModel]
  81. trait ReadablePretrainedDeBertaForQAModel extends ParamsAndFeaturesReadable[DeBertaForQuestionAnswering] with HasPretrained[DeBertaForQuestionAnswering]
  82. trait ReadablePretrainedDeBertaForSequenceModel extends ParamsAndFeaturesReadable[DeBertaForSequenceClassification] with HasPretrained[DeBertaForSequenceClassification]
  83. trait ReadablePretrainedDeBertaForTokenModel extends ParamsAndFeaturesReadable[DeBertaForTokenClassification] with HasPretrained[DeBertaForTokenClassification]
  84. trait ReadablePretrainedDeBertaForZeroShotModel extends ParamsAndFeaturesReadable[DeBertaForZeroShotClassification] with HasPretrained[DeBertaForZeroShotClassification]
  85. trait ReadablePretrainedDistilBertForQAModel extends ParamsAndFeaturesReadable[DistilBertForQuestionAnswering] with HasPretrained[DistilBertForQuestionAnswering]
  86. trait ReadablePretrainedDistilBertForSequenceModel extends ParamsAndFeaturesReadable[DistilBertForSequenceClassification] with HasPretrained[DistilBertForSequenceClassification]
  87. trait ReadablePretrainedDistilBertForTokenModel extends ParamsAndFeaturesReadable[DistilBertForTokenClassification] with HasPretrained[DistilBertForTokenClassification]
  88. trait ReadablePretrainedDistilBertForZeroShotModel extends ParamsAndFeaturesReadable[DistilBertForZeroShotClassification] with HasPretrained[DistilBertForZeroShotClassification]
  89. trait ReadablePretrainedLongformerForQAModel extends ParamsAndFeaturesReadable[LongformerForQuestionAnswering] with HasPretrained[LongformerForQuestionAnswering]
  90. trait ReadablePretrainedLongformerForSequenceModel extends ParamsAndFeaturesReadable[LongformerForSequenceClassification] with HasPretrained[LongformerForSequenceClassification]
  91. trait ReadablePretrainedLongformerForTokenModel extends ParamsAndFeaturesReadable[LongformerForTokenClassification] with HasPretrained[LongformerForTokenClassification]
  92. trait ReadablePretrainedMPNetForQAModel extends ParamsAndFeaturesReadable[MPNetForQuestionAnswering] with HasPretrained[MPNetForQuestionAnswering]
  93. trait ReadablePretrainedMPNetForSequenceModel extends ParamsAndFeaturesReadable[MPNetForSequenceClassification] with HasPretrained[MPNetForSequenceClassification]
  94. trait ReadablePretrainedMultiClassifierDL extends ParamsAndFeaturesReadable[MultiClassifierDLModel] with HasPretrained[MultiClassifierDLModel]
  95. trait ReadablePretrainedRoBertaForQAModel extends ParamsAndFeaturesReadable[RoBertaForQuestionAnswering] with HasPretrained[RoBertaForQuestionAnswering]
  96. trait ReadablePretrainedRoBertaForSequenceModel extends ParamsAndFeaturesReadable[RoBertaForSequenceClassification] with HasPretrained[RoBertaForSequenceClassification]
  97. trait ReadablePretrainedRoBertaForTokenModel extends ParamsAndFeaturesReadable[RoBertaForTokenClassification] with HasPretrained[RoBertaForTokenClassification]
  98. trait ReadablePretrainedRoBertaForZeroShotModel extends ParamsAndFeaturesReadable[RoBertaForZeroShotClassification] with HasPretrained[RoBertaForZeroShotClassification]
  99. trait ReadablePretrainedSentimentDL extends ParamsAndFeaturesReadable[SentimentDLModel] with HasPretrained[SentimentDLModel]
  100. trait ReadablePretrainedTapasForQAModel extends ParamsAndFeaturesReadable[TapasForQuestionAnswering] with HasPretrained[TapasForQuestionAnswering]
  101. trait ReadablePretrainedXlmRoBertaForQAModel extends ParamsAndFeaturesReadable[XlmRoBertaForQuestionAnswering] with HasPretrained[XlmRoBertaForQuestionAnswering]
  102. trait ReadablePretrainedXlmRoBertaForSequenceModel extends ParamsAndFeaturesReadable[XlmRoBertaForSequenceClassification] with HasPretrained[XlmRoBertaForSequenceClassification]
  103. trait ReadablePretrainedXlmRoBertaForTokenModel extends ParamsAndFeaturesReadable[XlmRoBertaForTokenClassification] with HasPretrained[XlmRoBertaForTokenClassification]
  104. trait ReadablePretrainedXlmRoBertaForZeroShotModel extends ParamsAndFeaturesReadable[XlmRoBertaForZeroShotClassification] with HasPretrained[XlmRoBertaForZeroShotClassification]
  105. trait ReadablePretrainedXlnetForSequenceModel extends ParamsAndFeaturesReadable[XlnetForSequenceClassification] with HasPretrained[XlnetForSequenceClassification]
  106. trait ReadablePretrainedXlnetForTokenModel extends ParamsAndFeaturesReadable[XlnetForTokenClassification] with HasPretrained[XlnetForTokenClassification]
  107. class RoBertaForQuestionAnswering extends AnnotatorModel[RoBertaForQuestionAnswering] with HasBatchedAnnotate[RoBertaForQuestionAnswering] with WriteTensorflowModel with WriteOnnxModel with HasCaseSensitiveProperties with HasEngine

    RoBertaForQuestionAnswering can load RoBERTa Models with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute span start logits and span end logits).

    RoBertaForQuestionAnswering can load RoBERTa Models with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute span start logits and span end logits).

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

    val spanClassifier = RoBertaForQuestionAnswering.pretrained()
      .setInputCols(Array("document_question", "document_context"))
      .setOutputCol("answer")

    The default model is "roberta_base_qa_squad2", 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 to see more extended examples, see RoBertaForQuestionAnsweringTestSpec.

    Example

    import spark.implicits._
    import com.johnsnowlabs.nlp.base._
    import com.johnsnowlabs.nlp.annotator._
    import org.apache.spark.ml.Pipeline
    
    val document = new MultiDocumentAssembler()
      .setInputCols("question", "context")
      .setOutputCols("document_question", "document_context")
    
    val questionAnswering = RoBertaForQuestionAnswering.pretrained()
      .setInputCols(Array("document_question", "document_context"))
      .setOutputCol("answer")
      .setCaseSensitive(true)
    
    val pipeline = new Pipeline().setStages(Array(
      document,
      questionAnswering
    ))
    
    val data = Seq("What's my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context")
    val result = pipeline.fit(data).transform(data)
    
    result.select("label.result").show(false)
    +---------------------+
    |result               |
    +---------------------+
    |[Clara]              |
    ++--------------------+
    See also

    RoBertaForSequenceClassification for sequence-level classification

    Annotators Main Page for a list of transformer based classifiers

  108. class RoBertaForSequenceClassification extends AnnotatorModel[RoBertaForSequenceClassification] with HasBatchedAnnotate[RoBertaForSequenceClassification] with WriteTensorflowModel with WriteOnnxModel with HasCaseSensitiveProperties with HasClassifierActivationProperties with HasEngine

    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 to see more extended examples, see 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("I loved this movie when I was a child.", "It was pretty boring.").toDF("text")
    val result = pipeline.fit(data).transform(data)
    
    result.select("label.result").show(false)
    +------+
    |result|
    +------+
    |[pos] |
    |[neg] |
    +------+
    See also

    RoBertaForSequenceClassification for sequence-level classification

    Annotators Main Page for a list of transformer based classifiers

  109. class RoBertaForTokenClassification extends AnnotatorModel[RoBertaForTokenClassification] with HasBatchedAnnotate[RoBertaForTokenClassification] with WriteTensorflowModel with WriteOnnxModel with HasCaseSensitiveProperties with HasEngine

    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

    RoBertaForTokenClassification for token-level classification

    Annotators Main Page for a list of transformer based classifiers

  110. class RoBertaForZeroShotClassification extends AnnotatorModel[RoBertaForZeroShotClassification] with HasBatchedAnnotate[RoBertaForZeroShotClassification] with WriteTensorflowModel with HasCaseSensitiveProperties with HasClassifierActivationProperties with HasEngine with HasCandidateLabelsProperties

    RoBertaForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks.

    RoBertaForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks. Equivalent of RoBertaForZeroShotClassification models, but these models don't require a hardcoded number of potential classes, they can be chosen at runtime. It usually means it's slower but it is much more flexible.

    Note that the model will loop through all provided labels. So the more labels you have, the longer this process will take.

    Any combination of sequences and labels can be passed and each combination will be posed as a premise/hypothesis pair and passed to the pretrained model.

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

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

    The default model is "roberta_base_zero_shot_classifier_nli", 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.

    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 = RoBertaForZeroShotClassification .pretrained()
      .setInputCols("token", "document")
      .setOutputCol("label")
      .setCaseSensitive(true)
    
    val pipeline = new Pipeline().setStages(Array(
      documentAssembler,
      tokenizer,
      sequenceClassifier
    ))
    
    val data = Seq("I loved this movie when I was a child.", "It was pretty boring.").toDF("text")
    val result = pipeline.fit(data).transform(data)
    
    result.select("label.result").show(false)
    +------+
    |result|
    +------+
    |[pos] |
    |[neg] |
    +------+
    See also

    RoBertaForZeroShotClassification for sequence-level classification

    Annotators Main Page for a list of transformer based classifiers

  111. class SentimentDLApproach extends AnnotatorApproach[SentimentDLModel] with ParamsAndFeaturesWritable with ClassifierEncoder

    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:

    Setting a test dataset to monitor model metrics can be done with .setTestDataset. The method expects a path to a parquet file containing a dataframe that has the same required columns as the training dataframe. The pre-processing steps for the training dataframe should also be applied to the test dataframe. The following example will show how to create the test dataset:

    val documentAssembler = new DocumentAssembler()
      .setInputCol("text")
      .setOutputCol("document")
    
    val embeddings = UniversalSentenceEncoder.pretrained()
      .setInputCols("document")
      .setOutputCol("sentence_embeddings")
    
    val preProcessingPipeline = new Pipeline().setStages(Array(documentAssembler, embeddings))
    
    val Array(train, test) = data.randomSplit(Array(0.8, 0.2))
    preProcessingPipeline
      .fit(test)
      .transform(test)
      .write
      .mode("overwrite")
      .parquet("test_data")
    
    val classifier = new SentimentDLApproach()
      .setInputCols("sentence_embeddings")
      .setOutputCol("sentiment")
      .setLabelColumn("label")
      .setTestDataset("test_data")

    For extended examples of usage, see the Examples 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

    ClassifierDLApproach for general single-class classification

    MultiClassifierDLApproach for general multi-class classification

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

    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 Examples 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

    ClassifierDLModel for general single-class classification

    MultiClassifierDLModel for general multi-class classification

  113. class TapasForQuestionAnswering extends BertForQuestionAnswering

    TapasForQuestionAnswering is an implementation of TaPas - a BERT-based model specifically designed for answering questions about tabular data.

    TapasForQuestionAnswering is an implementation of TaPas - a BERT-based model specifically designed for answering questions about tabular data. It takes TABLE and DOCUMENT annotations as input and tries to answer the questions in the document by using the data from the table. The model is based in BertForQuestionAnswering and shares all its parameters with it.

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

    val tapas = TapasForQuestionAnswering.pretrained()
      .setInputCols(Array("document_question", "table"))
      .setOutputCol("answer")

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

    For available pretrained models please see the Models Hub.

    Example

    import spark.implicits._
    import com.johnsnowlabs.nlp.base._
    import com.johnsnowlabs.nlp.annotator._
    import org.apache.spark.ml.Pipeline
    
     val questions =
       """
        |Who earns 100,000,000?
        |Who has more money?
        |How old are they?
        |""".stripMargin.trim
    
     val jsonData =
       """
        |{
        | "header": ["name", "money", "age"],
        | "rows": [
        |   ["Donald Trump", "$100,000,000", "75"],
        |   ["Elon Musk", "$20,000,000,000,000", "55"]
        | ]
        |}
        |""".stripMargin.trim
    
     val data = Seq((jsonData, questions))
      .toDF("json_table", "questions")
      .repartition(1)
    
    val docAssembler = new MultiDocumentAssembler()
      .setInputCols("json_table", "questions")
      .setOutputCols("document_table", "document_questions")
    
    val sentenceDetector = SentenceDetectorDLModel
      .pretrained()
      .setInputCols(Array("document_questions"))
      .setOutputCol("question")
    
    val tableAssembler = new TableAssembler()
      .setInputFormat("json")
      .setInputCols(Array("document_table"))
      .setOutputCol("table")
    
    val tapas = TapasForQuestionAnswering
      .pretrained()
      .setInputCols(Array("question", "table"))
      .setOutputCol("answer")
    
    val pipeline = new Pipeline()
      .setStages(
        Array(
          docAssembler,
          sentenceDetector,
          tableAssembler,
           tapas))
    
    val pipelineModel = pipeline.fit(data)
    val result = pipeline.fit(data).transform(data)
    
    result
      .selectExpr("explode(answer) as answer")
      .selectExpr(
        "answer.metadata.question",
        "answer.result")
    
    +-----------------------+----------------------------------------+
    |question               |result                                  |
    +-----------------------+----------------------------------------+
    |Who earns 100,000,000? |Donald Trump                            |
    |Who has more money?    |Elon Musk                               |
    |How much they all earn?|COUNT($100,000,000, $20,000,000,000,000)|
    |How old are they?      |AVERAGE(75, 55)                         |
    +-----------------------+----------------------------------------+
    See also

    https://aclanthology.org/2020.acl-main.398/ for more details about the TaPas model

    TableAssembler for loading tabular data

    Annotators Main Page for a list of transformer based classifiers

  114. class XlmRoBertaForQuestionAnswering extends AnnotatorModel[XlmRoBertaForQuestionAnswering] with HasBatchedAnnotate[XlmRoBertaForQuestionAnswering] with WriteTensorflowModel with WriteOnnxModel with WriteSentencePieceModel with HasCaseSensitiveProperties with HasEngine

    XlmRoBertaForQuestionAnswering can load XLM-RoBERTa Models with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute span start logits and span end logits).

    XlmRoBertaForQuestionAnswering can load XLM-RoBERTa Models with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute span start logits and span end logits).

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

    val spanClassifier = XlmRoBertaForQuestionAnswering.pretrained()
      .setInputCols(Array("document_question", "document_context"))
      .setOutputCol("answer")

    The default model is "xlm_roberta_base_qa_squad2", 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 to see more extended examples, see XlmRoBertaForQuestionAnsweringTestSpec.

    Example

    import spark.implicits._
    import com.johnsnowlabs.nlp.base._
    import com.johnsnowlabs.nlp.annotator._
    import org.apache.spark.ml.Pipeline
    
    val document = new MultiDocumentAssembler()
      .setInputCols("question", "context")
      .setOutputCols("document_question", "document_context")
    
    val questionAnswering = XlmRoBertaForQuestionAnswering.pretrained()
      .setInputCols(Array("document_question", "document_context"))
      .setOutputCol("answer")
      .setCaseSensitive(true)
    
    val pipeline = new Pipeline().setStages(Array(
      document,
      questionAnswering
    ))
    
    val data = Seq("What's my name?", "My name is Clara and I live in Berkeley.").toDF("question", "context")
    val result = pipeline.fit(data).transform(data)
    
    result.select("label.result").show(false)
    +---------------------+
    |result               |
    +---------------------+
    |[Clara]              |
    ++--------------------+
    See also

    XlmRoBertaForSequenceClassification for sequence-level classification

    Annotators Main Page for a list of transformer based classifiers

  115. class XlmRoBertaForSequenceClassification extends AnnotatorModel[XlmRoBertaForSequenceClassification] with HasBatchedAnnotate[XlmRoBertaForSequenceClassification] with WriteOnnxModel with WriteTensorflowModel with WriteSentencePieceModel with HasCaseSensitiveProperties with HasClassifierActivationProperties with HasEngine

    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 to see more extended examples, see 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("I loved this movie when I was a child.", "It was pretty boring.").toDF("text")
    val result = pipeline.fit(data).transform(data)
    
    result.select("label.result").show(false)
    +------+
    |result|
    +------+
    |[pos] |
    |[neg] |
    +------+
    See also

    XlmRoBertaForSequenceClassification for sequence-level classification

    Annotators Main Page for a list of transformer based classifiers

  116. class XlmRoBertaForTokenClassification extends AnnotatorModel[XlmRoBertaForTokenClassification] with HasBatchedAnnotate[XlmRoBertaForTokenClassification] with WriteOnnxModel with WriteTensorflowModel with WriteSentencePieceModel with HasCaseSensitiveProperties with HasEngine

    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

    XlmRoBertaForTokenClassification for token-level classification

    Annotators Main Page for a list of transformer based classifiers

  117. class XlmRoBertaForZeroShotClassification extends AnnotatorModel[XlmRoBertaForZeroShotClassification] with HasBatchedAnnotate[XlmRoBertaForZeroShotClassification] with WriteTensorflowModel with WriteSentencePieceModel with HasCaseSensitiveProperties with HasClassifierActivationProperties with HasEngine with HasCandidateLabelsProperties

    XlmRoBertaForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks.

    XlmRoBertaForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks. Equivalent of XlmRoBertaForZeroShotClassification models, but these models don't require a hardcoded number of potential classes, they can be chosen at runtime. It usually means it's slower but it is much more flexible.

    Note that the model will loop through all provided labels. So the more labels you have, the longer this process will take.

    Any combination of sequences and labels can be passed and each combination will be posed as a premise/hypothesis pair and passed to the pretrained model.

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

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

    The default model is "xlm_roberta_large_zero_shot_classifier_xnli_anli", 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.

    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 = XlmRoBertaForZeroShotClassification .pretrained()
      .setInputCols("token", "document")
      .setOutputCol("label")
      .setCaseSensitive(true)
    
    val pipeline = new Pipeline().setStages(Array(
      documentAssembler,
      tokenizer,
      sequenceClassifier
    ))
    
    val data = Seq("I loved this movie when I was a child.", "It was pretty boring.").toDF("text")
    val result = pipeline.fit(data).transform(data)
    
    result.select("label.result").show(false)
    +------+
    |result|
    +------+
    |[pos] |
    |[neg] |
    +------+
    See also

    XlmRoBertaForZeroShotClassification for sequence-level classification

    Annotators Main Page for a list of transformer based classifiers

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

    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 to see more extended examples, see 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("I loved this movie when I was a child.", "It was pretty boring.").toDF("text")
    val result = pipeline.fit(data).transform(data)
    
    result.select("label.result").show(false)
    +------+
    |result|
    +------+
    |[pos] |
    |[neg] |
    +------+
    See also

    XlnetForSequenceClassification for sequence-level classification

    Annotators Main Page for a list of transformer based classifiers

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

    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

    XlnetForTokenClassification for token-level classification

    Annotators Main Page for a list of transformer based classifiers

Value Members

  1. object AlbertForQuestionAnswering extends ReadablePretrainedAlbertForQAModel with ReadAlbertForQuestionAnsweringDLModel with Serializable

    This is the companion object of AlbertForQuestionAnswering.

    This is the companion object of AlbertForQuestionAnswering. Please refer to that class for the documentation.

  2. object AlbertForSequenceClassification extends ReadablePretrainedAlbertForSequenceModel with ReadAlbertForSequenceDLModel with Serializable

    This is the companion object of AlbertForSequenceClassification.

    This is the companion object of AlbertForSequenceClassification. Please refer to that class for the documentation.

  3. object AlbertForTokenClassification extends ReadablePretrainedAlbertForTokenModel with ReadAlbertForTokenDLModel with Serializable

    This is the companion object of AlbertForTokenClassification.

    This is the companion object of AlbertForTokenClassification. Please refer to that class for the documentation.

  4. object BartForZeroShotClassification extends ReadablePretrainedBartForZeroShotModel with ReadBartForZeroShotDLModel with Serializable

    This is the companion object of BartForZeroShotClassification.

    This is the companion object of BartForZeroShotClassification. Please refer to that class for the documentation.

  5. object BertForQuestionAnswering extends ReadablePretrainedBertForQAModel with ReadBertForQuestionAnsweringDLModel with Serializable

    This is the companion object of BertForQuestionAnswering.

    This is the companion object of BertForQuestionAnswering. Please refer to that class for the documentation.

  6. object BertForSequenceClassification extends ReadablePretrainedBertForSequenceModel with ReadBertForSequenceDLModel with Serializable

    This is the companion object of BertForSequenceClassification.

    This is the companion object of BertForSequenceClassification. Please refer to that class for the documentation.

  7. object BertForTokenClassification extends ReadablePretrainedBertForTokenModel with ReadBertForTokenDLModel with Serializable

    This is the companion object of BertForTokenClassification.

    This is the companion object of BertForTokenClassification. Please refer to that class for the documentation.

  8. object BertForZeroShotClassification extends ReadablePretrainedBertForZeroShotModel with ReadBertForZeroShotDLModel with Serializable

    This is the companion object of BertForZeroShotClassification.

    This is the companion object of BertForZeroShotClassification. Please refer to that class for the documentation.

  9. object CamemBertForQuestionAnswering extends ReadablePretrainedCamemBertForQAModel with ReadCamemBertForQADLModel with Serializable

    This is the companion object of CamemBertForQuestionAnswering.

    This is the companion object of CamemBertForQuestionAnswering. Please refer to that class for the documentation.

  10. object CamemBertForSequenceClassification extends ReadablePretrainedCamemBertForSequenceModel with ReadCamemBertForSequenceDLModel with Serializable

    This is the companion object of CamemBertForSequenceClassification.

    This is the companion object of CamemBertForSequenceClassification. Please refer to that class for the documentation.

  11. object CamemBertForTokenClassification extends ReadablePretrainedCamemBertForTokenModel with ReadCamemBertForTokenDLModel with Serializable

    This is the companion object of CamemBertForTokenClassification.

    This is the companion object of CamemBertForTokenClassification. Please refer to that class for the documentation.

  12. object ClassifierDLApproach extends DefaultParamsReadable[ClassifierDLApproach] with Serializable

    This is the companion object of ClassifierDLApproach.

    This is the companion object of ClassifierDLApproach. Please refer to that class for the documentation.

  13. object ClassifierDLModel extends ReadablePretrainedClassifierDL with ReadClassifierDLTensorflowModel with Serializable

    This is the companion object of ClassifierDLModel.

    This is the companion object of ClassifierDLModel. Please refer to that class for the documentation.

  14. object DeBertaForQuestionAnswering extends ReadablePretrainedDeBertaForQAModel with ReadDeBertaForQuestionAnsweringDLModel with Serializable

    This is the companion object of DeBertaForQuestionAnswering.

    This is the companion object of DeBertaForQuestionAnswering. Please refer to that class for the documentation.

  15. object DeBertaForSequenceClassification extends ReadablePretrainedDeBertaForSequenceModel with ReadDeBertaForSequenceDLModel with Serializable

    This is the companion object of DeBertaForSequenceClassification.

    This is the companion object of DeBertaForSequenceClassification. Please refer to that class for the documentation.

  16. object DeBertaForTokenClassification extends ReadablePretrainedDeBertaForTokenModel with ReadDeBertaForTokenDLModel with Serializable

    This is the companion object of DeBertaForTokenClassification.

    This is the companion object of DeBertaForTokenClassification. Please refer to that class for the documentation.

  17. object DeBertaForZeroShotClassification extends ReadablePretrainedDeBertaForZeroShotModel with ReadDeBertaForZeroShotDLModel with Serializable

    This is the companion object of DeBertaForZeroShotClassification.

    This is the companion object of DeBertaForZeroShotClassification. Please refer to that class for the documentation.

  18. object DistilBertForQuestionAnswering extends ReadablePretrainedDistilBertForQAModel with ReadDistilBertForQuestionAnsweringDLModel with Serializable

    This is the companion object of DistilBertForQuestionAnswering.

    This is the companion object of DistilBertForQuestionAnswering. Please refer to that class for the documentation.

  19. object DistilBertForSequenceClassification extends ReadablePretrainedDistilBertForSequenceModel with ReadDistilBertForSequenceDLModel with Serializable

    This is the companion object of DistilBertForSequenceClassification.

    This is the companion object of DistilBertForSequenceClassification. Please refer to that class for the documentation.

  20. object DistilBertForTokenClassification extends ReadablePretrainedDistilBertForTokenModel with ReadDistilBertForTokenDLModel with Serializable

    This is the companion object of DistilBertForTokenClassification.

    This is the companion object of DistilBertForTokenClassification. Please refer to that class for the documentation.

  21. object DistilBertForZeroShotClassification extends ReadablePretrainedDistilBertForZeroShotModel with ReadDistilBertForZeroShotDLModel with Serializable

    This is the companion object of DistilBertForZeroShotClassification.

    This is the companion object of DistilBertForZeroShotClassification. Please refer to that class for the documentation.

  22. object LongformerForQuestionAnswering extends ReadablePretrainedLongformerForQAModel with ReadLongformerForQuestionAnsweringDLModel with Serializable

    This is the companion object of LongformerForQuestionAnswering.

    This is the companion object of LongformerForQuestionAnswering. Please refer to that class for the documentation.

  23. object LongformerForSequenceClassification extends ReadablePretrainedLongformerForSequenceModel with ReadLongformerForSequenceDLModel with Serializable

    This is the companion object of LongformerForSequenceClassification.

    This is the companion object of LongformerForSequenceClassification. Please refer to that class for the documentation.

  24. object LongformerForTokenClassification extends ReadablePretrainedLongformerForTokenModel with ReadLongformerForTokenDLModel with Serializable

    This is the companion object of LongformerForTokenClassification.

    This is the companion object of LongformerForTokenClassification. Please refer to that class for the documentation.

  25. object MPNetForQuestionAnswering extends ReadablePretrainedMPNetForQAModel with ReadMPNetForQuestionAnsweringDLModel with Serializable

    This is the companion object of MPNetForQuestionAnswering.

    This is the companion object of MPNetForQuestionAnswering. Please refer to that class for the documentation.

  26. object MPNetForSequenceClassification extends ReadablePretrainedMPNetForSequenceModel with ReadMPNetForSequenceDLModel with Serializable

    This is the companion object of MPNetForSequenceClassification.

    This is the companion object of MPNetForSequenceClassification. Please refer to that class for the documentation.

  27. object MultiClassifierDLModel extends ReadablePretrainedMultiClassifierDL with ReadMultiClassifierDLTensorflowModel with Serializable

    This is the companion object of MultiClassifierDLModel.

    This is the companion object of MultiClassifierDLModel. Please refer to that class for the documentation.

  28. object RoBertaForQuestionAnswering extends ReadablePretrainedRoBertaForQAModel with ReadRoBertaForQuestionAnsweringDLModel with Serializable

    This is the companion object of RoBertaForQuestionAnswering.

    This is the companion object of RoBertaForQuestionAnswering. Please refer to that class for the documentation.

  29. object RoBertaForSequenceClassification extends ReadablePretrainedRoBertaForSequenceModel with ReadRoBertaForSequenceDLModel with Serializable

    This is the companion object of RoBertaForSequenceClassification.

    This is the companion object of RoBertaForSequenceClassification. Please refer to that class for the documentation.

  30. object RoBertaForTokenClassification extends ReadablePretrainedRoBertaForTokenModel with ReadRoBertaForTokenDLModel with Serializable

    This is the companion object of RoBertaForTokenClassification.

    This is the companion object of RoBertaForTokenClassification. Please refer to that class for the documentation.

  31. object RoBertaForZeroShotClassification extends ReadablePretrainedRoBertaForZeroShotModel with ReadRoBertaForZeroShotDLModel with Serializable

    This is the companion object of RoBertaForZeroShotClassification.

    This is the companion object of RoBertaForZeroShotClassification. Please refer to that class for the documentation.

  32. object SentimentApproach extends DefaultParamsReadable[SentimentDLApproach]

    This is the companion object of SentimentApproach.

    This is the companion object of SentimentApproach. Please refer to that class for the documentation.

  33. object SentimentDLModel extends ReadablePretrainedSentimentDL with ReadSentimentDLTensorflowModel with Serializable

    This is the companion object of SentimentDLModel.

    This is the companion object of SentimentDLModel. Please refer to that class for the documentation.

  34. object TapasForQuestionAnswering extends ReadablePretrainedTapasForQAModel with ReadTapasForQuestionAnsweringDLModel with Serializable

    This is the companion object of TapasForQuestionAnswering.

    This is the companion object of TapasForQuestionAnswering. Please refer to that class for the documentation.

  35. object XlmRoBertaForQuestionAnswering extends ReadablePretrainedXlmRoBertaForQAModel with ReadXlmRoBertaForQuestionAnsweringDLModel with Serializable

    This is the companion object of XlmRoBertaForQuestionAnswering.

    This is the companion object of XlmRoBertaForQuestionAnswering. Please refer to that class for the documentation.

  36. object XlmRoBertaForSequenceClassification extends ReadablePretrainedXlmRoBertaForSequenceModel with ReadXlmRoBertaForSequenceDLModel with Serializable

    This is the companion object of XlmRoBertaForSequenceClassification.

    This is the companion object of XlmRoBertaForSequenceClassification. Please refer to that class for the documentation.

  37. object XlmRoBertaForTokenClassification extends ReadablePretrainedXlmRoBertaForTokenModel with ReadXlmRoBertaForTokenDLModel with Serializable

    This is the companion object of XlmRoBertaForTokenClassification.

    This is the companion object of XlmRoBertaForTokenClassification. Please refer to that class for the documentation.

  38. object XlmRoBertaForZeroShotClassification extends ReadablePretrainedXlmRoBertaForZeroShotModel with ReadXlmRoBertaForZeroShotDLModel with Serializable

    This is the companion object of XlmRoBertaForZeroShotClassification.

    This is the companion object of XlmRoBertaForZeroShotClassification. Please refer to that class for the documentation.

  39. object XlnetForSequenceClassification extends ReadablePretrainedXlnetForSequenceModel with ReadXlnetForSequenceDLModel with Serializable

    This is the companion object of XlnetForSequenceClassification.

    This is the companion object of XlnetForSequenceClassification. Please refer to that class for the documentation.

  40. object XlnetForTokenClassification extends ReadablePretrainedXlnetForTokenModel with ReadXlnetForTokenDLModel with Serializable

    This is the companion object of XlnetForTokenClassification.

    This is the companion object of XlnetForTokenClassification. Please refer to that class for the documentation.

Ungrouped