def testBuildDatasetFromSameFile(self):
files = [
utils.get_data_file('iwslt15.tst2013.100.envi'),
utils.get_data_file('iwslt15.tst2013.100.envi'),
]
x_tokenizer = SpaceTokenizer()
x_tokenizer.build_from_corpus(
[utils.get_data_file('iwslt15.tst2013.100.en')])
y_tokenizer = SpaceTokenizer()
y_tokenizer.build_from_corpus(
[utils.get_data_file('iwslt15.tst2013.100.vi')])
config = {
'train_batch_size': 2,
'predict_batch_size': 2,
'eval_batch_size': 2,
'buffer_size': 100
}
dataset = Seq2SeqDataset(x_tokenizer, y_tokenizer, config)
train_dataset = dataset.build_train_dataset(files)
print(next(iter(train_dataset)))
print('=' * 120)
eval_dataset = dataset.build_eval_dataset(files)
print(next(iter(eval_dataset)))
print('=' * 120)
predict_files = [utils.get_data_file('iwslt15.tst2013.100.envi')]
predict_dataset = dataset.build_predict_dataset(predict_files)
print(next(iter(predict_dataset)))
print('=' * 120)
def testBuildDatasetFromSameFile(self):
files = [utils.get_data_file('classify.seq.label.txt')]
x_tokenizer = SpaceTokenizer()
x_tokenizer.build_from_corpus(
[utils.get_data_file('classify.seq.txt')])
config = {
'train_batch_size': 2,
'eval_batch_size': 2,
'predict_batch_size': 2,
'buffer_size': 100
}
dataset = SeqClassifyDataset(x_tokenizer, config)
train_dataset = dataset.build_train_dataset(files)
print(next(iter(train_dataset)))
print('=' * 120)
eval_dataset = dataset.build_eval_dataset(files)
print(next(iter(eval_dataset)))
print('=' * 120)
predict_files = [utils.get_data_file('classify.seq.txt')]
predict_dataset = dataset.build_predict_dataset(predict_files)
print(next(iter(predict_dataset)))
print('=' * 120)
def testBuildDatasetFromSameFile(self):
files = [
utils.get_data_file('dssm.query.doc.label.txt'),
utils.get_data_file('dssm.query.doc.label.txt'),
]
x_tokenizer = SpaceTokenizer()
x_tokenizer.build_from_vocab(utils.get_data_file('dssm.vocab.txt'))
y_tokenizer = SpaceTokenizer()
y_tokenizer.build_from_vocab(utils.get_data_file('dssm.vocab.txt'))
config = {
'train_batch_size': 2,
'eval_batch_size': 2,
'predict_batch_size': 2,
'buffer_size': 100,
}
dataset = SeqMatchDataset(x_tokenizer, y_tokenizer, config)
train_dataset = dataset.build_train_dataset(files)
print(next(iter(train_dataset)))
print('=' * 120)
eval_dataset = dataset.build_eval_dataset(files)
print(next(iter(eval_dataset)))
print('=' * 120)
predict_files = [utils.get_data_file('dssm.query.doc.label.txt')]
predict_dataset = dataset.build_predict_dataset(predict_files)
print(next(iter(predict_dataset)))
print('=' * 120)
def testBuildFromVocab(self):
print('============start build from vocab=============')
tokenizer = SpaceTokenizer()
tokenizer.build_from_vocab(data_dir_utils.get_data_file('vocab.test.txt'))
print('token2id dict: ', tokenizer.token2id_dict)
print('id2token dict: ', tokenizer.id2token_dict)
words = tf.constant(['I', 'am', 'a', 'developer'])
v0 = tokenizer.encode(words)
print(v0)
ids = tf.constant([1, 0, 2, 3, 4], dtype=tf.dtypes.int64)
v1 = tokenizer.decode(ids)
print(v1)
print('============end build from vocab=============')
def testSaveVocabFile(self):
tokenizer = self.buildTokenizer()
tokenizer.save_to_vocab(data_dir_utils.get_data_file('vocab.test.txt'))
print(tokenizer.token2id_dict)
print(tokenizer.id2token_dict)
def buildTokenizer(self):
tokenizer = SpaceTokenizer()
corpus = ['iwslt15.tst2013.100.en']
corpus = [data_dir_utils.get_data_file(f) for f in corpus]
tokenizer.build_from_corpus(corpus, token_filters=[EmptyTokenFilter()])
return tokenizer