Hi! Thank you for developing this amazing explainer for machine learning models.
I am currently trying to use LIME's TextExplainer model to get some insights into the BERT model I developed for Japanese text classification problems. With n_sample of around 500, I was able to get the metrics score of mean_KL_divergence: 0.044, score: 1.0. However, when I increased n_sample to 1000, the explainer gives almost opposite weight explanations (texts highlighted in green are now highlighted in red) and the metrics score remains high at mean_KL_divergence: 0.0298, score: 1.0
Is it due to the nonlinearity of BERT model? Do you have any advice on how I should solve this problem?
import eli5
from eli5.lime import TextExplainer
from pipe import predict_proba
te = TextExplainer(n_samples=1000, random_state=42)
te.fit(test, predict_proba)
te.show_prediction()
Hi! Thank you for developing this amazing explainer for machine learning models.
I am currently trying to use LIME's TextExplainer model to get some insights into the BERT model I developed for Japanese text classification problems. With n_sample of around 500, I was able to get the metrics score of
mean_KL_divergence: 0.044, score: 1.0. However, when I increased n_sample to 1000, the explainer gives almost opposite weight explanations (texts highlighted in green are now highlighted in red) and the metrics score remains high atmean_KL_divergence: 0.0298, score: 1.0Is it due to the nonlinearity of BERT model? Do you have any advice on how I should solve this problem?