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Merge pull request #1415 from IamJeffG/fix-alpha-example-train-ner-standalone

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Bugfix example script train_ner_standalone.py, fails after training
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Matthew Honnibal 2017-10-12 21:44:28 +02:00 committed by GitHub
commit cb0e727c54
1 changed files with 4 additions and 5 deletions
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9
examples/training/train_ner_standalone.py
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@ -6,7 +6,7 @@ To achieve that, it duplicates some of spaCy's internal functionality.
Specifically, in this example, we don't use spaCy's built-in Language class to
wire together the Vocab, Tokenizer and EntityRecognizer. Instead, we write
our own simle Pipeline class, so that it's easier to see how the pieces
our own simple Pipeline class, so that it's easier to see how the pieces
interact.
Input data:
@ -142,16 +142,15 @@ def train(nlp, train_examples, dev_examples, nr_epoch=5):
inputs, annots = zip(*batch)
nlp.update(list(inputs), list(annots), sgd, losses=losses)
scores = nlp.evaluate(dev_examples)
report_scores(i, losses['ner'], scores)
scores = nlp.evaluate(dev_examples)
report_scores(channels, i+1, loss, scores)
report_scores(i+1, losses['ner'], scores)
def report_scores(i, loss, scores):
precision = '%.2f' % scores['ents_p']
recall = '%.2f' % scores['ents_r']
f_measure = '%.2f' % scores['ents_f']
print('%d %s %s %s' % (int(loss), precision, recall, f_measure))
print('Epoch %d: %d %s %s %s' % (
i, int(loss), precision, recall, f_measure))
def read_examples(path):