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@ -158,9 +158,13 @@ model = dlib.train_sequence_segmenter(training_sequences, segments, params)
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for i in range(len(sentences)):
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print_segment(sentences[i], model.segment_sequence(training_sequences[i]))
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# Lets also try segmenting a new sentence. This will print out "Bob Bucket"
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# Lets also try segmenting a new sentence. This will print out "Bob Bucket". Note that we
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# need to remember to use the same vector representation as we used during training.
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test_sentence = "There once was a man from Nantucket whose name rhymed with Bob Bucket"
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print_segment(test_sentence, model.segment_sequence(sentence_to_vectors(test_sentence)))
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if use_sparse_vects:
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print_segment(test_sentence, model.segment_sequence(sentence_to_sparse_vectors(test_sentence)))
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else:
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print_segment(test_sentence, model.segment_sequence(sentence_to_vectors(test_sentence)))
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# We can also measure the accuracy of a model relative to some labeled data. This
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# statement prints the precision, recall, and F1-score of the model relative to the data in
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