Is everything comparable? How much slower in your benchmark?
not everything, but the definition of slow are: 1) how fast the loss going down (in terms of number of steps to reach), the overall loss after ~50k steps still 1-ish something while
t2t are 0.5-ish something. 2) how fast to stepping (per 100 steps), This one might 5x slower.
These measurement is oversimplified, however i just can feel it.
Actually there are still some odd things going on... Still investigating and might need to refine the behavior in a patch version. It's unclear what TensorFlow is doing under the hood.
scoreis usually used to score an existing prediction. You could define a custom model that extends the base
SequenceClassifier, something like:
class MyClassifier(onmt.models.SequenceClassifier): def __init__(self): super(MyClassifier, self).__init__(...) def call(self, *args, **kwargs): logits, _ = super(MyClassifier, self).super(*args, **kwargs) predictions = dict(probs=tf.nn.softmax(logits)) return logits, predictions def print_prediction(self, prediction, params=None, stream=None): print(prediction["probs"], file=stream)