Release BEMB release v0.1.4 · gsbDBI/bemb

Update: the forward() method of BEMB module has been updated for easier inference. The new forward() method can return both log-probabilities and utility values.
For use cases of predicting binary labels (pred_item = False), now the returned log-probabilities are always the predicted log-probability of the actual label. To get the predicted log-probabilities of the positive class for all observations, please get the utility first and apply a log-sigmoid transformation on utility values.
We have also added a helper function predict_proba() that gives (1) if pred_item=True, predicted probability of choosing each item among items in its category, so output shape is (batch_size, num_items) (2) if pred_item=False predicted probabilities of both label = 0 and label = 1 (so output shape is (batch_size, 2).
Please refer to the Jupyter notebook for more details.

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