Package com.oracle.bmc.ailanguage.model

Class NamedEntityRecognitionModelMetrics

java.lang.Object

com.oracle.bmc.http.client.internal.ExplicitlySetBmcModel

com.oracle.bmc.ailanguage.model.NamedEntityRecognitionModelMetrics

@Generated(value="OracleSDKGenerator",
           comments="API Version: 20221001")
public final class NamedEntityRecognitionModelMetrics
extends com.oracle.bmc.http.client.internal.ExplicitlySetBmcModel

Model level named entity recognition metrics
Note: Objects should always be created or deserialized using the NamedEntityRecognitionModelMetrics.Builder.

This model distinguishes fields that are null because they are unset from fields that are explicitly set to null. This is done in the setter methods of the NamedEntityRecognitionModelMetrics.Builder, which maintain a set of all explicitly set fields called NamedEntityRecognitionModelMetrics.Builder.__explicitlySet__. The hashCode() and equals(Object) methods are implemented to take the explicitly set fields into account. The constructor, on the other hand, does not take the explicitly set fields into account (since the constructor cannot distinguish explicit null from unset null).

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	NamedEntityRecognitionModelMetrics.Builder

Field Summary

Fields inherited from class com.oracle.bmc.http.client.internal.ExplicitlySetBmcModel

EXPLICITLY_SET_FILTER_NAME, EXPLICITLY_SET_PROPERTY_NAME

Constructor Summary

Constructors

Constructor	Description
NamedEntityRecognitionModelMetrics(Float microF1, Float microPrecision, Float microRecall, Float macroF1, Float macroPrecision, Float macroRecall, Float weightedF1, Float weightedPrecision, Float weightedRecall)	Deprecated.

Method Summary

Modifier and Type	Method	Description
static NamedEntityRecognitionModelMetrics.Builder	builder()	Create a new builder.
boolean	equals(Object o)
Float	getMacroF1()	F1-score, is a measure of a model\u2019s accuracy on a dataset
Float	getMacroPrecision()	Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
Float	getMacroRecall()	Measures the model’s ability to predict actual positive classes.
Float	getMicroF1()	F1-score, is a measure of a model\u2019s accuracy on a dataset
Float	getMicroPrecision()	Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
Float	getMicroRecall()	Measures the model’s ability to predict actual positive classes.
Float	getWeightedF1()	F1-score, is a measure of a model\u2019s accuracy on a dataset
Float	getWeightedPrecision()	Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
Float	getWeightedRecall()	Measures the model’s ability to predict actual positive classes.
int	hashCode()
NamedEntityRecognitionModelMetrics.Builder	toBuilder()
String	toString()
String	toString(boolean includeByteArrayContents)	Return a string representation of the object.

Methods inherited from class com.oracle.bmc.http.client.internal.ExplicitlySetBmcModel

markPropertyAsExplicitlySet, wasPropertyExplicitlySet

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

NamedEntityRecognitionModelMetrics

@Deprecated
@ConstructorProperties({"microF1","microPrecision","microRecall","macroF1","macroPrecision","macroRecall","weightedF1","weightedPrecision","weightedRecall"})
public NamedEntityRecognitionModelMetrics(Float microF1,
                                          Float microPrecision,
                                          Float microRecall,
                                          Float macroF1,
                                          Float macroPrecision,
                                          Float macroRecall,
                                          Float weightedF1,
                                          Float weightedPrecision,
                                          Float weightedRecall)

Deprecated.

Method Detail

builder

public static NamedEntityRecognitionModelMetrics.Builder builder()

Create a new builder.

toBuilder

public NamedEntityRecognitionModelMetrics.Builder toBuilder()

getMicroF1

public Float getMicroF1()

F1-score, is a measure of a model\u2019s accuracy on a dataset

Returns:

the value

getMicroPrecision

public Float getMicroPrecision()

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

Returns:

the value

getMicroRecall

public Float getMicroRecall()

Measures the model’s ability to predict actual positive classes.

It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

Returns:

the value

getMacroF1

public Float getMacroF1()

F1-score, is a measure of a model\u2019s accuracy on a dataset

Returns:

the value

getMacroPrecision

public Float getMacroPrecision()

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

Returns:

the value

getMacroRecall

public Float getMacroRecall()

Measures the model’s ability to predict actual positive classes.

It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

Returns:

the value

getWeightedF1

public Float getWeightedF1()

F1-score, is a measure of a model\u2019s accuracy on a dataset

Returns:

the value

getWeightedPrecision

public Float getWeightedPrecision()

Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

Returns:

the value

getWeightedRecall

public Float getWeightedRecall()

Measures the model’s ability to predict actual positive classes.

It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

Returns:

the value

toString

public String toString()

Overrides:

toString in class com.oracle.bmc.http.client.internal.ExplicitlySetBmcModel

toString

public String toString(boolean includeByteArrayContents)

Return a string representation of the object.

Parameters:

includeByteArrayContents - true to include the full contents of byte arrays

Returns:

string representation

equals

public boolean equals(Object o)

Overrides:

equals in class com.oracle.bmc.http.client.internal.ExplicitlySetBmcModel

hashCode

public int hashCode()

Overrides:

hashCode in class com.oracle.bmc.http.client.internal.ExplicitlySetBmcModel