Report for HooshvareLab/bert-fa-base-uncased-sentiment-deepsentipers-binary

#46
by inoki-giskard - opened

Hey Team!🤗✨
We’re thrilled to share some amazing evaluation results that’ll make your day!🎉📊

We have identified 7 potential vulnerabilities in your model based on an automated scan.

This automated analysis evaluated the model on the dataset sst2 (subset default, split validation).

👉Performance issues (7)
Vulnerability Level Data slice Metric Transformation Deviation
Performance major 🔴 avg_word_length(text) >= 4.109 AND avg_word_length(text) < 4.212 Precision = 0.361 -29.10% than global
🔍✨Examples For records in the dataset where `avg_word_length(text)` >= 4.109 AND `avg_word_length(text)` < 4.212, the Precision is 29.1% lower than the global Precision.
text avg_word_length(text) label Predicted label
19 in its best moments , resembles a bad high school production of grease , without benefit of song . 4.21053 negative positive (p = 1.00)
28 it 's a cookie-cutter movie , a cut-and-paste job . 4.2 negative positive (p = 1.00)
44 the title not only describes its main characters , but the lazy people behind the camera as well . 4.21053 negative positive (p = 1.00)
Vulnerability Level Data slice Metric Transformation Deviation
Performance major 🔴 avg_whitespace(text) < 0.196 AND avg_whitespace(text) >= 0.192 Precision = 0.361 -29.10% than global
🔍✨Examples For records in the dataset where `avg_whitespace(text)` < 0.196 AND `avg_whitespace(text)` >= 0.192, the Precision is 29.1% lower than the global Precision.
text avg_whitespace(text) label Predicted label
19 in its best moments , resembles a bad high school production of grease , without benefit of song . 0.191919 negative positive (p = 1.00)
28 it 's a cookie-cutter movie , a cut-and-paste job . 0.192308 negative positive (p = 1.00)
44 the title not only describes its main characters , but the lazy people behind the camera as well . 0.191919 negative positive (p = 1.00)
Vulnerability Level Data slice Metric Transformation Deviation
Performance major 🔴 idx < 190.500 AND idx >= 129.500 Precision = 0.377 -25.87% than global
🔍✨Examples For records in the dataset where `idx` < 190.500 AND `idx` >= 129.500, the Precision is 25.87% lower than the global Precision.
idx label Predicted label
130 130 negative positive (p = 1.00)
131 131 negative positive (p = 1.00)
133 133 negative positive (p = 1.00)
Vulnerability Level Data slice Metric Transformation Deviation
Performance major 🔴 text_length(text) >= 151.500 AND text_length(text) < 165.500 Precision = 0.407 -20.03% than global
🔍✨Examples For records in the dataset where `text_length(text)` >= 151.500 AND `text_length(text)` < 165.500, the Precision is 20.03% lower than the global Precision.
text text_length(text) label Predicted label
9 in exactly 89 minutes , most of which passed as slowly as if i 'd been sitting naked on an igloo , formula 51 sank from quirky to jerky to utter turkey . 154 negative positive (p = 1.00)
11 it takes a strange kind of laziness to waste the talents of robert forster , anne meara , eugene levy , and reginald veljohnson all in the same movie . 152 negative positive (p = 1.00)
26 the action switches between past and present , but the material link is too tenuous to anchor the emotional connections that purport to span a 125-year divide . 161 negative positive (p = 1.00)
Vulnerability Level Data slice Metric Transformation Deviation
Performance major 🔴 text contains "movie" Precision = 0.421 -17.22% than global
🔍✨Examples For records in the dataset where `text` contains "movie", the Precision is 17.22% lower than the global Precision.
text label Predicted label
11 it takes a strange kind of laziness to waste the talents of robert forster , anne meara , eugene levy , and reginald veljohnson all in the same movie . negative positive (p = 1.00)
14 even horror fans will most likely not find what they 're seeking with trouble every day ; the movie lacks both thrills and humor . negative positive (p = 1.00)
18 ... the movie is just a plain old monster . negative positive (p = 1.00)
Vulnerability Level Data slice Metric Transformation Deviation
Performance major 🔴 avg_word_length(text) >= 4.317 AND avg_word_length(text) < 4.613 Precision = 0.445 -12.46% than global
🔍✨Examples For records in the dataset where `avg_word_length(text)` >= 4.317 AND `avg_word_length(text)` < 4.613, the Precision is 12.46% lower than the global Precision.
text avg_word_length(text) label Predicted label
6 a sometimes tedious film . 4.4 negative positive (p = 1.00)
20 pumpkin takes an admirable look at the hypocrisy of political correctness , but it does so with such an uneven tone that you never know when humor ends and tragedy begins . 4.40625 negative positive (p = 1.00)
22 holden caulfield did it better . 4.5 negative positive (p = 1.00)
Vulnerability Level Data slice Metric Transformation Deviation
Performance major 🔴 avg_whitespace(text) < 0.188 AND avg_whitespace(text) >= 0.178 Precision = 0.445 -12.46% than global
🔍✨Examples For records in the dataset where `avg_whitespace(text)` < 0.188 AND `avg_whitespace(text)` >= 0.178, the Precision is 12.46% lower than the global Precision.
text avg_whitespace(text) label Predicted label
6 a sometimes tedious film . 0.185185 negative positive (p = 1.00)
20 pumpkin takes an admirable look at the hypocrisy of political correctness , but it does so with such an uneven tone that you never know when humor ends and tragedy begins . 0.184971 negative positive (p = 1.00)
22 holden caulfield did it better . 0.181818 negative positive (p = 1.00)

Disclaimer: it's important to note that automated scans may produce false positives or miss certain vulnerabilities. We encourage you to review the findings and assess the impact accordingly.

💡 What's Next?

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🙌 Big Thanks!

We're grateful to have you on this adventure with us. 🚀🌟 Here's to more breakthroughs, laughter, and code magic! 🥂✨ Keep hugging that code and spreading the love! 💻 #Giskard #Huggingface #AISafety 🌈👏 Your enthusiasm, feedback, and contributions are what seek. 🌟 Keep being awesome!

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