datasets
A dataset utils repository based on tf.data. For tensorflow>=2.0 only!
Requirements
- python 3.6
- tensorflow>=2.0
Installation
pip install nlp-datasetsUsage
seq2seq models
These models has an source sequence x and an target sequence y.
from nlp_datasets import Seq2SeqDataset
from nlp_datasets import SpaceTokenizer
from nlp_datasets.utils import data_dir_utils as utils
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)sequence match models
These models has two sequences as input, x and y, and has an label z.
from nlp_datasets import SeqMatchDataset
from nlp_datasets import SpaceTokenizer
from nlp_datasets.utils import data_dir_utils as utils
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)sequence classify model
These models has a input sequence x, and a output label y.
from nlp_datasets import SeqClassifyDataset
from nlp_datasets import SpaceTokenizer
from nlp_datasets.utils import data_dir_utils as utils
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)
