EntityMetrics¶

class oci.ai_language.models.EntityMetrics(**kwargs)¶

Bases: object

Entity level named entity recognition model metrics

Methods

__init__(**kwargs) Initializes a new EntityMetrics object with values from keyword arguments.

Attributes

`f1`	[Required] Gets the f1 of this EntityMetrics.
`label`	[Required] Gets the label of this EntityMetrics.
`precision`	[Required] Gets the precision of this EntityMetrics.
`recall`	[Required] Gets the recall of this EntityMetrics.

__init__(**kwargs)¶

Initializes a new EntityMetrics object with values from keyword arguments. The following keyword arguments are supported (corresponding to the getters/setters of this class):

Parameters:	label (str) – The value to assign to the label property of this EntityMetrics. f1 (float) – The value to assign to the f1 property of this EntityMetrics. precision (float) – The value to assign to the precision property of this EntityMetrics. recall (float) – The value to assign to the recall property of this EntityMetrics.

f1¶

[Required] Gets the f1 of this EntityMetrics. F1-score, is a measure of a model’s accuracy on a dataset

Returns:	The f1 of this EntityMetrics.
Return type:	float

label¶

[Required] Gets the label of this EntityMetrics. Entity label

Returns:	The label of this EntityMetrics.
Return type:	str

precision¶

[Required] Gets the precision of this EntityMetrics. 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 precision of this EntityMetrics.
Return type:	float

recall¶

[Required] Gets the recall of this EntityMetrics. 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 recall of this EntityMetrics.
Return type:	float