Interpretable Neural Predictions with Differentiable Binary Variables
Table 6: SNLI hyperparameters.
['Optimizer', 'Adam']
Our hyperparameters are taken from Parikh et al. Word embeddings are projected to 200 dimensions with a trained linear layer. Unknown words are mapped to 100 unknown word classes based on the MD5 hash function, just as in Parikh et al. We train for 100 epochs, evaluate every 1000 updates, and select the best model based on validation loss.