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minor cleanup
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@ -6,7 +6,7 @@ To achieve that, it duplicates some of spaCy's internal functionality.
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Specifically, in this example, we don't use spaCy's built-in Language class to
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wire together the Vocab, Tokenizer and EntityRecognizer. Instead, we write
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our own simle Pipeline class, so that it's easier to see how the pieces
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our own simple Pipeline class, so that it's easier to see how the pieces
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interact.
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Input data:
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@ -149,7 +149,8 @@ def report_scores(i, loss, scores):
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precision = '%.2f' % scores['ents_p']
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recall = '%.2f' % scores['ents_r']
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f_measure = '%.2f' % scores['ents_f']
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print('Epoch %d: %d %s %s %s' % (i, int(loss), precision, recall, f_measure))
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print('Epoch %d: %d %s %s %s' % (
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i, int(loss), precision, recall, f_measure))
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def read_examples(path):
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