Class: Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SchemaModelevaluationMetricsPairwiseTextGenerationEvaluationMetrics

Inherits:
Object
  • Object
show all
Includes:
Core::Hashable, Core::JsonObjectSupport
Defined in:
lib/google/apis/aiplatform_v1/classes.rb,
lib/google/apis/aiplatform_v1/representations.rb,
lib/google/apis/aiplatform_v1/representations.rb

Overview

Metrics for general pairwise text generation evaluation results.

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(**args) ⇒ GoogleCloudAiplatformV1SchemaModelevaluationMetricsPairwiseTextGenerationEvaluationMetrics

Returns a new instance of GoogleCloudAiplatformV1SchemaModelevaluationMetricsPairwiseTextGenerationEvaluationMetrics.



36421
36422
36423
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 36421

def initialize(**args)
   update!(**args)
end

Instance Attribute Details

#accuracy ⇒ Float

Fraction of cases where the autorater agreed with the human raters. Corresponds to the JSON property accuracy

Returns:

  • (Float) 


36349
36350
36351
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 36349

def accuracy
  @accuracy
end

#baseline_model_win_rate ⇒ Float

Percentage of time the autorater decided the baseline model had the better response. Corresponds to the JSON property baselineModelWinRate

Returns:

  • (Float) 


36355
36356
36357
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 36355

def baseline_model_win_rate
  @baseline_model_win_rate
end

#cohens_kappa ⇒ Float

A measurement of agreement between the autorater and human raters that takes the likelihood of random agreement into account. Corresponds to the JSON property cohensKappa

Returns:

  • (Float) 


36361
36362
36363
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 36361

def cohens_kappa
  @cohens_kappa
end

#f1_score ⇒ Float

Harmonic mean of precision and recall. Corresponds to the JSON property f1Score

Returns:

  • (Float) 


36366
36367
36368
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 36366

def f1_score
  @f1_score
end

#false_negative_count ⇒ Fixnum

Number of examples where the autorater chose the baseline model, but humans preferred the model. Corresponds to the JSON property falseNegativeCount

Returns:

  • (Fixnum) 


36372
36373
36374
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 36372

def false_negative_count
  @false_negative_count
end

#false_positive_count ⇒ Fixnum

Number of examples where the autorater chose the model, but humans preferred the baseline model. Corresponds to the JSON property falsePositiveCount

Returns:

  • (Fixnum) 


36378
36379
36380
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 36378

def false_positive_count
  @false_positive_count
end

#human_preference_baseline_model_win_rate ⇒ Float

Percentage of time humans decided the baseline model had the better response. Corresponds to the JSON property humanPreferenceBaselineModelWinRate

Returns:

  • (Float) 


36383
36384
36385
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 36383

def human_preference_baseline_model_win_rate
  @human_preference_baseline_model_win_rate
end

#human_preference_model_win_rate ⇒ Float

Percentage of time humans decided the model had the better response. Corresponds to the JSON property humanPreferenceModelWinRate

Returns:

  • (Float) 


36388
36389
36390
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 36388

def human_preference_model_win_rate
  @human_preference_model_win_rate
end

#model_win_rate ⇒ Float

Percentage of time the autorater decided the model had the better response. Corresponds to the JSON property modelWinRate

Returns:

  • (Float) 


36393
36394
36395
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 36393

def model_win_rate
  @model_win_rate
end

#precision ⇒ Float

Fraction of cases where the autorater and humans thought the model had a better response out of all cases where the autorater thought the model had a better response. True positive divided by all positive. Corresponds to the JSON property precision

Returns:

  • (Float) 


36400
36401
36402
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 36400

def precision
  @precision
end

#recall ⇒ Float

Fraction of cases where the autorater and humans thought the model had a better response out of all cases where the humans thought the model had a better response. Corresponds to the JSON property recall

Returns:

  • (Float) 


36407
36408
36409
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 36407

def recall
  @recall
end

#true_negative_count ⇒ Fixnum

Number of examples where both the autorater and humans decided that the model had the worse response. Corresponds to the JSON property trueNegativeCount

Returns:

  • (Fixnum) 


36413
36414
36415
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 36413

def true_negative_count
  @true_negative_count
end

#true_positive_count ⇒ Fixnum

Number of examples where both the autorater and humans decided that the model had the better response. Corresponds to the JSON property truePositiveCount

Returns:

  • (Fixnum) 


36419
36420
36421
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 36419

def true_positive_count
  @true_positive_count
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



36426
36427
36428
36429
36430
36431
36432
36433
36434
36435
36436
36437
36438
36439
36440
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 36426

def update!(**args)
  @accuracy = args[:accuracy] if args.key?(:accuracy)
  @baseline_model_win_rate = args[:baseline_model_win_rate] if args.key?(:baseline_model_win_rate)
  @cohens_kappa = args[:cohens_kappa] if args.key?(:cohens_kappa)
  @f1_score = args[:f1_score] if args.key?(:f1_score)
  @false_negative_count = args[:false_negative_count] if args.key?(:false_negative_count)
  @false_positive_count = args[:false_positive_count] if args.key?(:false_positive_count)
  @human_preference_baseline_model_win_rate = args[:human_preference_baseline_model_win_rate] if args.key?(:human_preference_baseline_model_win_rate)
  @human_preference_model_win_rate = args[:human_preference_model_win_rate] if args.key?(:human_preference_model_win_rate)
  @model_win_rate = args[:model_win_rate] if args.key?(:model_win_rate)
  @precision = args[:precision] if args.key?(:precision)
  @recall = args[:recall] if args.key?(:recall)
  @true_negative_count = args[:true_negative_count] if args.key?(:true_negative_count)
  @true_positive_count = args[:true_positive_count] if args.key?(:true_positive_count)
end