Class EntityMetrics

Entity level named entity recognition model metrics

EntityMetrics

public class EntityMetrics

[Required(ErrorMessage = "F1 is required.")]
[JsonProperty(PropertyName = "f1")]
public float? F1 { get; set; }

Type	Description
float?	F1-score, is a measure of a model\u2019s accuracy on a dataset

Required

[Required(ErrorMessage = "Label is required.")]
[JsonProperty(PropertyName = "label")]
public string Label { get; set; }

Type	Description
string	Entity label

Required

[Required(ErrorMessage = "Precision is required.")]
[JsonProperty(PropertyName = "precision")]
public float? Precision { get; set; }

Type	Description
float?	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)

Required

[Required(ErrorMessage = "Recall is required.")]
[JsonProperty(PropertyName = "recall")]
public float? Recall { get; set; }

Type	Description
float?	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.

Required