package dl

Ordering
  1. Alphabetic
Visibility
  1. Public
  2. All

Type Members

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

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

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

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

    val labels = 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.

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

    Annotators Main Page for a list of transformer based classifiers

  2. class ClassifierDLApproach extends AnnotatorApproach[ClassifierDLModel] with ParamsAndFeaturesWritable

    Trains a ClassifierDL for generic Multi-class Text Classification.

    Trains a ClassifierDL for generic Multi-class Text Classification.

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

    For instantiated/pretrained models, see ClassifierDLModel.

    Notes:

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

    Example

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

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

    Then traning can be done like so:

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

    MultiClassifierDLApproach for multi-class classification

    SentimentDLApproach for sentiment analysis

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

    ClassifierDL for generic Multi-class Text Classification.

    ClassifierDL for generic Multi-class Text Classification.

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

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

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

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

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

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

    Example

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

    MultiClassifierDLModel for multi-class classification

    SentimentDLModel for sentiment analysis

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

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

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

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

    val labels = 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.

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

    Annotators Main Page for a list of transformer based classifiers

  5. class MultiClassifierDLApproach extends AnnotatorApproach[MultiClassifierDLModel] with ParamsAndFeaturesWritable

    Trains a MultiClassifierDL for Multi-label Text Classification.

    Trains a MultiClassifierDL for Multi-label Text Classification.

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

    For instantiated/pretrained models, see MultiClassifierDLModel.

    The input to MultiClassifierDL are Sentence Embeddings such as the state-of-the-art UniversalSentenceEncoder, BertSentenceEmbeddings, or SentenceEmbeddings.

    In machine learning, multi-label classification and the strongly related problem of multi-output classification are variants of the classification problem where multiple labels may be assigned to each instance. Multi-label classification is a generalization of multiclass classification, which is the single-label problem of categorizing instances into precisely one of more than two classes; in the multi-label problem there is no constraint on how many of the classes the instance can be assigned to. Formally, multi-label classification is the problem of finding a model that maps inputs x to binary vectors y (assigning a value of 0 or 1 for each element (label) in y).

    Notes:

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

    Example

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

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

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

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

    Multi-label classification on Wikipedia

    ClassifierDLApproach for single-class classification

    SentimentDLApproach for sentiment analysis

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

    MultiClassifierDL for Multi-label Text Classification.

    MultiClassifierDL for Multi-label Text Classification.

    MultiClassifierDL Bidirectional GRU with Convolution model we have built inside TensorFlow and supports up to 100 classes. The input to MultiClassifierDL is Sentence Embeddings such as state-of-the-art UniversalSentenceEncoder, BertSentenceEmbeddings, or SentenceEmbeddings.

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

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

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

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

    In machine learning, multi-label classification and the strongly related problem of multi-output classification are variants of the classification problem where multiple labels may be assigned to each instance. Multi-label classification is a generalization of multiclass classification, which is the single-label problem of categorizing instances into precisely one of more than two classes; in the multi-label problem there is no constraint on how many of the classes the instance can be assigned to. Formally, multi-label classification is the problem of finding a model that maps inputs x to binary vectors y (assigning a value of 0 or 1 for each element (label) in y).

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

    Example

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

    Multi-label classification on Wikipedia

    ClassifierDLModel for single-class classification

    SentimentDLModel for sentiment analysis

  7. trait ReadBertForTokenTensorflowModel extends ReadTensorflowModel
  8. trait ReadClassifierDLTensorflowModel extends ReadTensorflowModel
  9. trait ReadDistilBertForTokenTensorflowModel extends ReadTensorflowModel
  10. trait ReadMultiClassifierDLTensorflowModel extends ReadTensorflowModel
  11. trait ReadSentimentDLTensorflowModel extends ReadTensorflowModel
  12. trait ReadablePretrainedBertForTokenModel extends ParamsAndFeaturesReadable[BertForTokenClassification] with HasPretrained[BertForTokenClassification]
  13. trait ReadablePretrainedClassifierDL extends ParamsAndFeaturesReadable[ClassifierDLModel] with HasPretrained[ClassifierDLModel]
  14. trait ReadablePretrainedDistilBertForTokenModel extends ParamsAndFeaturesReadable[DistilBertForTokenClassification] with HasPretrained[DistilBertForTokenClassification]
  15. trait ReadablePretrainedMultiClassifierDL extends ParamsAndFeaturesReadable[MultiClassifierDLModel] with HasPretrained[MultiClassifierDLModel]
  16. trait ReadablePretrainedSentimentDL extends ParamsAndFeaturesReadable[SentimentDLModel] with HasPretrained[SentimentDLModel]
  17. class SentimentDLApproach extends AnnotatorApproach[SentimentDLModel] with ParamsAndFeaturesWritable

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

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

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

    For the instantiated/pretrained models, see SentimentDLModel.

    Notes:

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

    Example

    In this example, sentiment.csv is in the form

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

    The model can then be trained with

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

    ClassifierDLApproach for general single-class classification

    MultiClassifierDLApproach for general multi-class classification

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

    SentimentDL, an annotator for multi-class sentiment analysis.

    SentimentDL, an annotator for multi-class sentiment analysis.

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

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

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

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

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

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

    Example

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

    ClassifierDLModel for general single-class classification

    MultiClassifierDLModel for general multi-class classification

Value Members

  1. object BertForTokenClassification extends ReadablePretrainedBertForTokenModel with ReadBertForTokenTensorflowModel with Serializable

    This is the companion object of BertForTokenClassification.

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

  2. 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.

  3. 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.

  4. object DistilBertForTokenClassification extends ReadablePretrainedDistilBertForTokenModel with ReadDistilBertForTokenTensorflowModel with Serializable

    This is the companion object of DistilBertForTokenClassification.

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

  5. 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.

  6. 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.

  7. 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.

Ungrouped