# create work dir
work_dir = os.path.join(os.path.expanduser('~'), 'brocas_models')
lm_file = os.path.join(work_dir, 'test_model.bin')
if not os.path.exists(work_dir):
os.makedirs(work_dir)
# get text corpus
nltk.download('brown')
sents = nltk.corpus.brown.sents()[:100]
# preprocessing
normalizer = Normalization(sents, min_count=15)
training_data = NormalizationIter(normalizer, sents)
lm = LanguageModel(tokenized_sentences=training_data,
input_layer_size=64,
hidden_layer_size=128)
print()
# train model
lm.train(training_data, epochs=5, backup_directory=work_dir, log_interval=20)
print()
# test trained model
normalized_sentence = normalizer.normalize(sents[0])
print('normalized sentence:')
print(' '.join(normalized_sentence))
print('probability: ', lm.sentence_log_probability(normalized_sentence))
print()
start_tag = normalized_sentence[0]
end_tag = normalized_sentence[-1]
from brocas_lm.model import LanguageModel
# create work dir
work_dir = os.path.join(os.path.expanduser('~'), 'brocas_models')
lm_file = os.path.join(work_dir, 'test_model.bin')
if not os.path.exists(work_dir):
os.makedirs(work_dir)
# get text corpus
nltk.download('brown')
sents = nltk.corpus.brown.sents()[:100]
# preprocessing
normalizer = Normalization(sents, min_count=15)
training_data = NormalizationIter(normalizer, sents)
lm = LanguageModel(tokenized_sentences=training_data, input_layer_size=64, hidden_layer_size=128)
print()
# train model
lm.train(training_data, epochs=5, backup_directory=work_dir, log_interval=20)
print()
# test trained model
normalized_sentence = normalizer.normalize(sents[0])
print('normalized sentence:')
print(' '.join(normalized_sentence))
print('probability: ', lm.sentence_log_probability(normalized_sentence))
print()
start_tag = normalized_sentence[0]
end_tag = normalized_sentence[-1]
print('sample:')
min_count=20,
start_tag='S',
end_tag='E',
unknown_tag='U',
digit_tag='D')
all_sents_normalized = NormalizationIter(normalizer, all_sents)
cs1 = ['than', 'then']
cs2 = ['except', 'accept']
cs3 = ['well', 'good']
acs = AdvancedCorpusSplitter(all_sents_normalized, cs1 + cs2 + cs3)
if os.path.isfile(lm_file):
lm = LanguageModel(lm_file=lm_file)
else:
lm = LanguageModel(verbose=True,
tokenized_sentences=acs,
input_layer_size=128,
hidden_layer_size=512)
cost_log = lm.train(acs,
epochs=10,
backup_directory=work_dir,
return_cost=True,
log_interval=1000)
lm.save(lm_file)
# sampling
