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Fix train loop for train_textcat example
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@ -73,10 +73,12 @@ def main(model=None, output_dir=None, n_iter=20, n_texts=2000, init_tok2vec=None
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textcat.model.tok2vec.from_bytes(file_.read())
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print("Training the model...")
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print("{:^5}\t{:^5}\t{:^5}\t{:^5}".format("LOSS", "P", "R", "F"))
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batch_sizes = compounding(4.0, 32.0, 1.001)
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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(4.0, 32.0, 1.001))
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random.shuffle(train_data)
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batches = minibatch(train_data, size=batch_sizes)
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for batch in batches:
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texts, annotations = zip(*batch)
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nlp.update(texts, annotations, sgd=optimizer, drop=0.2, losses=losses)
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