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Use averaged params for evaluation
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@ -53,7 +53,8 @@ def train(lang_id, output_dir, train_data, dev_data, n_iter, n_sents,
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golds = list(golds)
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golds = list(golds)
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nlp.update(docs, golds, drop=dropout, sgd=optimizer)
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nlp.update(docs, golds, drop=dropout, sgd=optimizer)
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pbar.update(len(docs))
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pbar.update(len(docs))
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scorer = nlp.evaluate(corpus.dev_docs(nlp))
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with nlp.use_params(optimizer.averages):
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scorer = nlp.evaluate(corpus.dev_docs(nlp))
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print_progress(i, {}, scorer.scores)
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print_progress(i, {}, scorer.scores)
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with (output_path / 'model.bin').open('wb') as file_:
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with (output_path / 'model.bin').open('wb') as file_:
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dill.dump(nlp, file_, -1)
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dill.dump(nlp, file_, -1)
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