diff --git "a/distilbert-base-uncased-go-emotions-studen_report.html" "b/distilbert-base-uncased-go-emotions-studen_report.html" new file mode 100644--- /dev/null +++ "b/distilbert-base-uncased-go-emotions-studen_report.html" @@ -0,0 +1,1178 @@ + + + + + Giskard Scan Results + + + + + + + + + +
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+ 7 issues detected +
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+ Ethical + + + 3 + +
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+ Robustness + + + 2 + +
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+ Underconfidence + + + 1 + +
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+ Performance + + + 1 + +
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Your model seems to be sensitive to gender, ethnic, or religion based perturbations in the input data. These perturbations can include switching some words from feminine to masculine, countries or nationalities. This happens when:

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  • Underrepresentation of certain demographic groups in the training data
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  • Data is reflecting some structural biases and societal prejudices
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  • Use of complex models with large number of parameters that tend to overfit the training data
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To learn more about causes and solutions, check our guide on unethical behaviour.

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Issues

+ + 2 + major + + + 1 + medium + + +
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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + Feature `text` + + + + + Switch Religion + + Fail rate = 0.227 + + + + 5/22 tested samples (22.73%) changed prediction after perturbation + + + + 22 samples affected
+ (0.4% of dataset) + + + 		+ Show details + +
+ + + Feature `text` + + + + + Switch countries from high- to low-income and vice versa + + Fail rate = 0.148 + + + + 12/81 tested samples (14.81%) changed prediction after perturbation + + + + 81 samples affected
+ (1.5% of dataset) + + + 		+ Show details + +
+ + + Feature `text` + + + + + Switch Gender + + Fail rate = 0.095 + + + + 78/818 tested samples (9.54%) changed prediction after perturbation + + + + 818 samples affected
+ (15.1% of dataset) + + + 		+ Show details + +
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+

Debug your issues in the Giskard hub

+ +

+ Install the Giskard hub app to: +

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  • Debug and diagnose your scan issues
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  • Save your scan result as a re-executable test suite to benchmark your model
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  • Extend your test suite with our catalog of ready-to-use tests
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+

+ You can find installation instructions here. +

+
+ 
from giskard import GiskardClient
+
+# Create a test suite from your scan results
+test_suite = results.generate_test_suite("My first test suite")
+
+# Upload your test suite to your Giskard hub instance
+client = GiskardClient("http://localhost:19000", "GISKARD_API_KEY")
+client.create_project("my_project_id", "my_project_name")
+test_suite.upload(client, "my_project_id")
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+
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+ +
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+ + + + + + + \ No newline at end of file