Comments (1)
Hot fix has been implemented by adding distinct values for label_classes
in the config:
vs
and then changing the dataset label column and label_names:
I'll close this for now, but the right way to go would probably be fixing the label order when loading the dataset before running the explanation job.
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Related Issues (13)
- Selection of experiment_in unsafe HOT 1
- Missing assertion that num_labels in dataset corresponds to classification head shape of model
- Improve heatmap visualization options (more color schemes, better readability)
- Prediction Values in Dataset HOT 3
- ExplainerCaptum.get_inputs_and_additional_args and .get_forward_function need to be extensible HOT 1
- LIME : "normal_kernel_cuda" not implemented for 'Long' HOT 4
- [KernelShap] ZeroDivisionError: Weights sum to zero, can't be normalized HOT 1
- [InputXGradient] RuntimeError: One of the differentiated Tensors does not require grad HOT 2
- Find out if the cast to long for batch components in forward functions is necessary HOT 1
- batch dimension ignored HOT 4
- AttributeError: 'XLNetModel' object has no attribute 'embeddings' HOT 6
- LIME token similarity kernel for input.shape[0] > 1 HOT 3
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