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Don't reapply the model during training
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@ -47,7 +47,8 @@ class Trainer(object):
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for process in self.nlp.pipeline:
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if hasattr(process, 'update'):
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loss = process.update(doc, gold, itn=self.nr_epoch)
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process(doc)
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else:
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process(doc)
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return doc
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def evaluate(self, dev_sents, gold_preproc=False):
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