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Fixed vocabulary in the entity linker training example (#5676)
* entity linker training example: model loading changed according to issue 5668 (https://github.com/explosion/spaCy/issues/5668) + vocab_path is a required argument * contributor agreement
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@ -60,12 +60,12 @@ TRAIN_DATA = sample_train_data()
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output_dir=("Optional output directory", "option", "o", Path),
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output_dir=("Optional output directory", "option", "o", Path),
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n_iter=("Number of training iterations", "option", "n", int),
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n_iter=("Number of training iterations", "option", "n", int),
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)
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)
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def main(kb_path, vocab_path=None, output_dir=None, n_iter=50):
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def main(kb_path, vocab_path, output_dir=None, n_iter=50):
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"""Create a blank model with the specified vocab, set up the pipeline and train the entity linker.
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"""Create a blank model with the specified vocab, set up the pipeline and train the entity linker.
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The `vocab` should be the one used during creation of the KB."""
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The `vocab` should be the one used during creation of the KB."""
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vocab = Vocab().from_disk(vocab_path)
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# create blank English model with correct vocab
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# create blank English model with correct vocab
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nlp = spacy.blank("en", vocab=vocab)
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nlp = spacy.blank("en")
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nlp.vocab.from_disk(vocab_path)
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nlp.vocab.vectors.name = "spacy_pretrained_vectors"
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nlp.vocab.vectors.name = "spacy_pretrained_vectors"
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print("Created blank 'en' model with vocab from '%s'" % vocab_path)
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print("Created blank 'en' model with vocab from '%s'" % vocab_path)
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