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Restore random cuts in parser/ner training

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This commit is contained in:
Matthew Honnibal 2020-06-25 21:18:29 +02:00
parent 9e3695de6b
commit ae58d00327
1 changed files with 7 additions and 1 deletions
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8
spacy/syntax/nn_parser.pyx
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@ -272,7 +272,13 @@ cdef class Parser:
# Prepare the stepwise model, and get the callback for finishing the batch
model, backprop_tok2vec = self.model.begin_update(
[eg.predicted for eg in examples])
states, golds, max_steps = self._init_gold_batch(examples)
# Chop sequences into lengths of this many transitions, to make the
# batch uniform length. We randomize this to overfit less.
cut_gold = numpy.random.choice(range(20, 100))
states, golds, max_steps = self._init_gold_batch(
examples,
max_length=cut_gold
)
all_states = list(states)
states_golds = zip(states, golds)
for _ in range(max_steps):