com.johnsnowlabs.finance.sequence_classification
FinanceClassifierDLModel
Companion object FinanceClassifierDLModel
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. 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
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annotate(annotations: Seq[Annotation]): Seq[Annotation]
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onWrite(path: String, spark: SparkSession): Unit
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optionalInputAnnotatorTypes: Array[String]
- Definition Classes
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outputAnnotatorType: String
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outputCol: Param[String]
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params: Array[Param[_]]
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parent: Estimator[ClassifierDLModel]
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save(path: String): Unit
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def
set[T](feature: StructFeature[T], value: T): FinanceClassifierDLModel.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
set[K, V](feature: MapFeature[K, V], value: Map[K, V]): FinanceClassifierDLModel.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
set[T](feature: SetFeature[T], value: Set[T]): FinanceClassifierDLModel.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
set[T](feature: ArrayFeature[T], value: Array[T]): FinanceClassifierDLModel.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
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final
def
set(paramPair: ParamPair[_]): FinanceClassifierDLModel.this.type
- Attributes
- protected
- Definition Classes
- Params
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final
def
set(param: String, value: Any): FinanceClassifierDLModel.this.type
- Attributes
- protected
- Definition Classes
- Params
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final
def
set[T](param: Param[T], value: T): FinanceClassifierDLModel.this.type
- Definition Classes
- Params
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def
setConfigProtoBytes(bytes: Array[Int]): FinanceClassifierDLModel.this.type
- Definition Classes
- ClassifierDLModel
-
def
setDatasetParams(params: ClassifierDatasetEncoderParams): FinanceClassifierDLModel.this.type
- Definition Classes
- ClassifierDLModel
-
def
setDefault[T](feature: StructFeature[T], value: () ⇒ T): FinanceClassifierDLModel.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
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def
setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): FinanceClassifierDLModel.this.type
- Attributes
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- Definition Classes
- HasFeatures
-
def
setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): FinanceClassifierDLModel.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): FinanceClassifierDLModel.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
final
def
setDefault(paramPairs: ParamPair[_]*): FinanceClassifierDLModel.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
setDefault[T](param: Param[T], value: T): FinanceClassifierDLModel.this.type
- Attributes
- protected[org.apache.spark.ml]
- Definition Classes
- Params
-
final
def
setInputCols(value: String*): FinanceClassifierDLModel.this.type
- Definition Classes
- HasInputAnnotationCols
-
def
setInputCols(value: Array[String]): FinanceClassifierDLModel.this.type
- Definition Classes
- HasInputAnnotationCols
-
def
setLazyAnnotator(value: Boolean): FinanceClassifierDLModel.this.type
- Definition Classes
- CanBeLazy
-
def
setModelIfNotSet(spark: SparkSession, tf: TensorflowWrapper): FinanceClassifierDLModel.this.type
- Definition Classes
- ClassifierDLModel
-
final
def
setOutputCol(value: String): FinanceClassifierDLModel.this.type
- Definition Classes
- HasOutputAnnotationCol
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def
setParent(parent: Estimator[ClassifierDLModel]): ClassifierDLModel
- Definition Classes
- Model
-
def
setStorageRef(value: String): FinanceClassifierDLModel.this.type
- Definition Classes
- HasStorageRef
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val
storageRef: Param[String]
- Definition Classes
- HasStorageRef
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synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
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toString(): String
- Definition Classes
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def
transform(dataset: Dataset[_]): DataFrame
- Definition Classes
- AnnotatorModel → Transformer
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def
transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
- Definition Classes
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- @Since( "2.0.0" )
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def
transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
- Definition Classes
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def
transformSchema(schema: StructType): StructType
- Definition Classes
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def
transformSchema(schema: StructType, logging: Boolean): StructType
- Attributes
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- Definition Classes
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- Annotations
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val
uid: String
- Definition Classes
- FinanceClassifierDLModel → ClassifierDLModel → Identifiable
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def
validate(schema: StructType): Boolean
- Attributes
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def
validateStorageRef(dataset: Dataset[_], inputCols: Array[String], annotatorType: String): Unit
- Definition Classes
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wait(): Unit
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def
wrapColumnMetadata(col: Column): Column
- Attributes
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- Definition Classes
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def
write: MLWriter
- Definition Classes
- ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
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def
writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String): Unit
- Definition Classes
- WriteTensorflowModel
-
def
writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]]): Unit
- Definition Classes
- WriteTensorflowModel
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def
writeTensorflowModelV2(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]], savedSignatures: Option[Map[String, String]]): Unit
- Definition Classes
- WriteTensorflowModel