p

com.johnsnowlabs.finance

sequence_classification

package sequence_classification

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Type Members

  1. class FinanceBertForSequenceClassification extends MedicalBertForSequenceClassification

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

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

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

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

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

    For available pretrained models please see the Models Hub.

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

    Example

    import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val tokenizer = new Tokenizer()
  .setInputCols("document")
  .setOutputCol("token")

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

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  tokenizer,
  sequenceClassifier
))

val data = Seq("John Lenon was born in London and lived in Paris. My name is Sarah and I live in London").toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(false)
+--------------------+
|result              |
+--------------------+
|[True, False]       |
+--------------------+
    See also

    MedicalBertForSequenceClassification for sequnece-level classification

    Annotators Main Page for a list of transformer based classifiers

  2. class FinanceClassifierDLApproach extends ClassifierDLApproach

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

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

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

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

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

    For available pretrained models please see the Models Hub.

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

    Example

    import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val tokenizer = new Tokenizer()
  .setInputCols("document")
  .setOutputCol("token")

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

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  tokenizer,
  sequenceClassifier
))

val data = Seq("John Lenon was born in London and lived in Paris. My name is Sarah and I live in London").toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(false)
+--------------------+
|result              |
+--------------------+
|[True, False]       |
+--------------------+
    See also

    MedicalBertForSequenceClassification for sequnece-level classification

    Annotators Main Page for a list of transformer based classifiers

  3. class FinanceClassifierDLModel extends ClassifierDLModel

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

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

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

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

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

    For available pretrained models please see the Models Hub.

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

    Example

    import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val tokenizer = new Tokenizer()
  .setInputCols("document")
  .setOutputCol("token")

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

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  tokenizer,
  sequenceClassifier
))

val data = Seq("John Lenon was born in London and lived in Paris. My name is Sarah and I live in London").toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(false)
+--------------------+
|result              |
+--------------------+
|[True, False]       |
+--------------------+
    See also

    MedicalBertForSequenceClassification for sequnece-level classification

    Annotators Main Page for a list of transformer based classifiers

  4. class FinanceDocumentMLClassifierApproach extends DocumentMLClassifierApproach

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

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

    Example

    Define pipeline stages to prepare the data

    val document_assembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val tokenizer = new Tokenizer()
  .setInputCols("document")
  .setOutputCol("token")

val normalizer = new Normalizer()
  .setInputCols("token")
  .setOutputCol("normalized")

val stopwords_cleaner = new StopWordsCleaner()
  .setInputCols("normalized")
  .setOutputCol("cleanTokens")
  .setCaseSensitive(false)

val stemmer = new Stemmer()
  .setInputCols("cleanTokens")
  .setOutputCol("stem")

    Define the document classifier and fit training data to it

    val logreg = new DocumentLogRegClassifierApproach()
  .setInputCols("stem")
  .setLabelCol("category")
  .setOutputCol("prediction")

val pipeline = new Pipeline().setStages(Array(
  document_assembler,
  tokenizer,
  normalizer,
  stopwords_cleaner,
  stemmer,
  logreg
))

val model = pipeline.fit(trainingData)
    See also

    DocumentLogRegClassifierModel for instantiated models

  5. class FinanceDocumentMLClassifierModel extends DocumentMLClassifierModel

    Classifies documents with a Logarithmic Regression algorithm.

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

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

  6. class FinanceFewShotClassifierApproach extends FewShotClassifierApproach
  7. class FinanceFewShotClassifierModel extends FewShotClassifierModel
  8. trait ReadFinanceBertForSequenceClassificationTensorflowModel extends ReadTensorflowModel
  9. trait ReadablePretrainedFinanceBertForSequenceModel extends ParamsAndFeaturesReadable[FinanceBertForSequenceClassification] with HasPretrained[FinanceBertForSequenceClassification]

Value Members

  1. object FinanceBertForSequenceClassification extends ReadablePretrainedFinanceBertForSequenceModel with ReadFinanceBertForSequenceClassificationTensorflowModel with Serializable

    This is the companion object of FinanceBertForSequenceClassification.

    This is the companion object of FinanceBertForSequenceClassification. se refer to that class for the documentation.

  2. object FinanceClassifierDLModel extends ReadablePretrainedClassifierDL with ReadClassifierDLTensorflowModel with Serializable
  3. object FinanceDocumentMLClassifierModel extends ReadablePretrainedDocumentMLClassifierModel with Serializable
  4. object FinanceFewShotClassifierApproach extends FinanceFewShotClassifierApproach
  5. object FinanceFewShotClassifierModel extends ReadsGenericClassifierGraph[FinanceFewShotClassifierModel] with ReadablePretrainedGenericClassifier[FinanceFewShotClassifierModel] with Serializable

Ungrouped