feat: add classification accuracy semantic robustness eval algo #47
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polaschwoebel
previously approved these changes
Oct 18, 2023
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Looks good! Please implement the two 2 quick fixes below.
We can discuss whether we want to address the point in the bottom at some later point, not important for mvp.
Quick fixes:
- ClassificationAccuracySemanticRobustness missing from
eval_algo_mapping.py, so it cannot be imported like the others (for example notebooks). - In example notebooks, first cell:
# from amazon_fmeval.eval_algo_mapping import get_eval_algorithmneeds to be
from amazon_fmeval import get_eval_algorithm
More involved, for later:
- Only accuracy is reported for robustness (not "balanced_accuracy_score", "precision_score", and "recall_score"). Adding those will probably require some refactoring because they cannot be computed on a per-sample-basis but need the whole dataset at once.
| num_perturbations: int = 5 | ||
| seed: int = 5 | ||
| # BUTTER FINGER PERTURBATION | ||
| butter_finger_perturbation_prob: Optional[float] = 0.1 |
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These defaults should be the same between all robustness evals. Consider turning them into constants.
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Later: abstract away base_task (for QA, summarization, classification) + robustness to avoid duplications.
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malhotra18
approved these changes
Oct 18, 2023
pinaraws
approved these changes
Oct 19, 2023
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Description of changes:
add classification accuracy semantic robustness eval algo
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