# of names in new sentences.
model = dlib.train_sequence_segmenter(training_sequences, segments, params)
# Let's print out the things the model thinks are names. The output is a set
# of ranges which are predicted to contain names. If you run this example
# program you will see that it gets them all correct.
for i, s in enumerate(sentences):
print_segment(s, model(training_sequences[i]))
# Let's also try segmenting a new sentence. This will print out "Bob Bucket".
# Note that we need to remember to use the same vector representation as we used
# during training.
test_sentence = "There once was a man from Nantucket " \
"whose name rhymed with Bob Bucket"
if use_sparse_vects:
print_segment(test_sentence,
model(sentence_to_sparse_vectors(test_sentence)))
else:
print_segment(test_sentence, model(sentence_to_vectors(test_sentence)))
# We can also measure the accuracy of a model relative to some labeled data.
# This statement prints the precision, recall, and F1-score of the model
# relative to the data in training_sequences/segments.
print("Test on training data: {}".format(
dlib.test_sequence_segmenter(model, training_sequences, segments)))
# We can also do 5-fold cross-validation and print the resulting precision,
# recall, and F1-score.
print("Cross validation: {}".format(
dlib.cross_validate_sequence_segmenter(training_sequences, segments, 5,
params)))
model = dlib.train_sequence_segmenter(training_sequences, segments, params)
# Let's print out the things the model thinks are names. The output is a set
# of ranges which are predicted to contain names. If you run this example
# program you will see that it gets them all correct.
for i, s in enumerate(sentences):
print_segment(s, model(training_sequences[i]))
# Let's also try segmenting a new sentence. This will print out "Bob Bucket".
# Note that we need to remember to use the same vector representation as we used
# during training.
test_sentence = "There once was a man from Nantucket " \
"whose name rhymed with Bob Bucket"
if use_sparse_vects:
print_segment(test_sentence,
model(sentence_to_sparse_vectors(test_sentence)))
else:
print_segment(test_sentence, model(sentence_to_vectors(test_sentence)))
# We can also measure the accuracy of a model relative to some labeled data.
# This statement prints the precision, recall, and F1-score of the model
# relative to the data in training_sequences/segments.
print("Test on training data: {}".format(
dlib.test_sequence_segmenter(model, training_sequences, segments)))
# We can also do 5-fold cross-validation and print the resulting precision,
# recall, and F1-score.
print("Cross validation: {}".format(
dlib.cross_validate_sequence_segmenter(training_sequences, segments, 5,
params)))
params.C = 10
# Train a model. The model object is responsible for predicting the locations of names in
# new sentences.
model = dlib.train_sequence_segmenter(training_sequences, segments, params)
# Lets print out the things the model thinks are names. The output is a set of ranges
# which are predicted to contain names. If you run this example program you will see that
# it gets them all correct.
for i in range(len(sentences)):
print_segment(sentences[i], model(training_sequences[i]))
# Lets also try segmenting a new sentence. This will print out "Bob Bucket". Note that we
# need to remember to use the same vector representation as we used during training.
test_sentence = "There once was a man from Nantucket whose name rhymed with Bob Bucket"
if use_sparse_vects:
print_segment(test_sentence, model(sentence_to_sparse_vectors(test_sentence)))
else:
print_segment(test_sentence, model(sentence_to_vectors(test_sentence)))
# We can also measure the accuracy of a model relative to some labeled data. This
# statement prints the precision, recall, and F1-score of the model relative to the data in
# training_sequences/segments.
print "Test on training data:", dlib.test_sequence_segmenter(model, training_sequences, segments)
# We can also do 5-fold cross-validation and print the resulting precision, recall, and F1-score.
print "cross validation:", dlib.cross_validate_sequence_segmenter(training_sequences, segments, 5, params)
params.C = 10
# Train a model. The model object is responsible for predicting the locations of names in
# new sentences.
model = dlib.train_sequence_segmenter(training_sequences, segments, params)
# Lets print out the things the model thinks are names. The output is a set of ranges
# which are predicted to contain names. If you run this example program you will see that
# it gets them all correct.
for i in range(len(sentences)):
print_segment(sentences[i], model(training_sequences[i]))
# Lets also try segmenting a new sentence. This will print out "Bob Bucket". Note that we
# need to remember to use the same vector representation as we used during training.
test_sentence = "There once was a man from Nantucket whose name rhymed with Bob Bucket"
if use_sparse_vects:
print_segment(test_sentence,
model(sentence_to_sparse_vectors(test_sentence)))
else:
print_segment(test_sentence, model(sentence_to_vectors(test_sentence)))
# We can also measure the accuracy of a model relative to some labeled data. This
# statement prints the precision, recall, and F1-score of the model relative to the data in
# training_sequences/segments.
print "Test on training data:", dlib.test_sequence_segmenter(
model, training_sequences, segments)
# We can also do 5-fold cross-validation and print the resulting precision, recall, and F1-score.
print "cross validation:", dlib.cross_validate_sequence_segmenter(
training_sequences, segments, 5, params)