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Report for distilbert-base-uncased-finetuned-sst-2-english
Hey Team!🤗✨
We’re thrilled to share some amazing evaluation results that’ll make your day!🎉📊
We have identified 1 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 (1)
| Vulnerability | Level | Data slice | Metric | Transformation | Deviation |
|---|---|---|---|---|---|
| Performance | major 🔴 | text contains "film" |
Accuracy = 0.402 | — | -18.16% than global |
🔍✨Examples
For records in the dataset where `text` contains "film", the Accuracy is 18.16% lower than the global Accuracy.| text | label | Predicted label |
|
|---|---|---|---|
| 5 | although laced with humor and a few fanciful touches , the film is a refreshingly serious look at young women . | POSITIVE | NEGATIVE (p = 1.00) |
| 8 | you do n't have to know about music to appreciate the film 's easygoing blend of comedy and romance . | POSITIVE | NEGATIVE (p = 0.99) |
| 10 | the mesmerizing performances of the leads keep the film grounded and keep the audience riveted . | POSITIVE | NEGATIVE (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.
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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!