mirror of https://github.com/explosion/spaCy.git
Fix print statements in text classifier example
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@ -26,8 +26,9 @@ from spacy.pipeline import TextCategorizer
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@plac.annotations(
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model=("Model name. Defaults to blank 'en' model.", "option", "m", str),
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output_dir=("Optional output directory", "option", "o", Path),
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n_examples=("Number of texts to train from", "option", "N", int),
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n_iter=("Number of training iterations", "option", "n", int))
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def main(model=None, output_dir=None, n_iter=20):
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def main(model=None, output_dir=None, n_iter=20, n_texts=2000):
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if model is not None:
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nlp = spacy.load(model) # load existing spaCy model
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print("Loaded model '%s'" % model)
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@ -50,7 +51,8 @@ def main(model=None, output_dir=None, n_iter=20):
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# load the IMBD dataset
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print("Loading IMDB data...")
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(train_texts, train_cats), (dev_texts, dev_cats) = load_data(limit=2000)
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print("Using %d training examples" % n_texts)
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(train_texts, train_cats), (dev_texts, dev_cats) = load_data(limit=n_texts)
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train_docs = [nlp.tokenizer(text) for text in train_texts]
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train_gold = [GoldParse(doc, cats=cats) for doc, cats in
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zip(train_docs, train_cats)]
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@ -65,14 +67,14 @@ def main(model=None, output_dir=None, n_iter=20):
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for i in range(n_iter):
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losses = {}
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# batch up the examples using spaCy's minibatch
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batches = minibatch(train_data, size=compounding(4., 128., 1.001))
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batches = minibatch(train_data, size=compounding(4., 32., 1.001))
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for batch in batches:
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docs, golds = zip(*batch)
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nlp.update(docs, golds, sgd=optimizer, drop=0.2, losses=losses)
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with textcat.model.use_params(optimizer.averages):
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# evaluate on the dev data split off in load_data()
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scores = evaluate(nlp.tokenizer, textcat, dev_texts, dev_cats)
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print('{0:.3f}\t{0:.3f}\t{0:.3f}\t{0:.3f}' # print a simple table
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print('{0:.3f}\t{1:.3f}\t{2:.3f}\t{3:.3f}' # print a simple table
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.format(losses['textcat'], scores['textcat_p'],
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scores['textcat_r'], scores['textcat_f']))
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