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

#2
by giskard-bot - opened

Hi Team,

This is a report from Giskard Bot Scan 🐢.

We have identified 2 potential vulnerabilities in your model based on an automated scan.

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

You can find a full version of scan report here.

👉Performance issues (2)

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.

Level Data slice Metric Deviation
major 🔴 text_length(text) >= 151.500 AND text_length(text) < 165.500 Precision = 0.407 -20.03% than global

Taxonomy

avid-effect:performance:P0204
🔍✨Examples
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)

For records in the dataset where text contains "movie", the Precision is 17.22% lower than the global Precision.

Level Data slice Metric Deviation
major 🔴 text contains "movie" Precision = 0.421 -17.22% than global

Taxonomy

avid-effect:performance:P0204
🔍✨Examples
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)

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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.

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