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Return examples from init_gold_batch
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@ -272,7 +272,7 @@ cdef class Parser:
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# Prepare the stepwise model, and get the callback for finishing the batch
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model, backprop_tok2vec = self.model.begin_update(
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[eg.predicted for eg in examples])
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states, golds, max_steps = self.moves.init_gold_batch(examples)
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states, golds, examples, max_steps = self.moves.init_gold_batch(examples)
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all_states = list(states)
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states_golds = zip(states, golds)
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for _ in range(max_steps):
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@ -285,6 +285,7 @@ cdef class Parser:
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# Follow the predicted action
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self.transition_states(states, scores)
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states_golds = [(s, g) for (s, g) in zip(states, golds) if not s.is_final()]
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backprop_tok2vec(golds)
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if sgd not in (None, False):
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self.model.finish_update(sgd)
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