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.venv/
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import tensorflow as tf
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import nlpaug.augmenter.word as naw
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class DataLoader:
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def __init__(self, path, buffer_size, batch_size, max_length, test_ratio=0.2):
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self.path = path
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self.buffer_size = buffer_size
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self.batch_size = batch_size
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self.max_length = max_length
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self.test_ratio = test_ratio
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self.aug = naw.SynonymAug(aug_src="wordnet")
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def _split_input_target(self, sequence):
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parts = tf.strings.split(sequence, "\t")
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index = int(parts[0])
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sentence = tf.strings.reduce_join(parts[1:], separator=" ")
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return sentence, index
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def augment_data(self, sentence, index):
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aug_sentence = self.aug.augment(sentence.numpy().decode())
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return sentence, aug_sentence, index
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def tf_augment_data(self, sentence, index):
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sentence, aug_sentence, index = tf.py_function(
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self.augment_data, [sentence, index], [tf.string, tf.string, tf.int32]
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)
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return sentence, aug_sentence, index
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def load_dataset(self):
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lines_dataset = tf.data.TextLineDataset(self.path)
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dataset = lines_dataset.map(self._split_input_target)
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dataset = dataset.map(self.tf_augment_data)
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# Split dataset into train and test
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dataset_size = tf.data.experimental.cardinality(dataset).numpy()
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test_size = int(dataset_size * self.test_ratio)
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train_size = dataset_size - test_size
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train_dataset = dataset.take(train_size)
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test_dataset = dataset.skip(train_size)
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# Shuffle and batch
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train_dataset = train_dataset.shuffle(self.buffer_size).batch(self.batch_size)
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test_dataset = test_dataset.shuffle(self.buffer_size).batch(self.batch_size)
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return train_dataset, test_dataset
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def test():
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# Hyperparameters
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buffer_size = 10000
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batch_size = 64
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max_length = 100 # Or any other value depending on your data
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# Create DataLoader
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data_loader = DataLoader(
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"../datasets/deu_mixed-typical_2011_1M/deu_mixed-typical_2011_1M-sentences.txt",
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buffer_size,
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batch_size,
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max_length,
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)
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# Load the datasets
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train_dataset, test_dataset = data_loader.load_dataset()
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# Test the data loader on the training dataset
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print("First 5 batches from the training dataset:")
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for sent, aug, indxs in train_dataset.take(1):
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print(f"Indices: {indxs}, Sentences: {sent}, Augmented: {aug}")
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# Test the data loader on the test dataset
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# print("\nFirst 5 batches from the test dataset:")
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# for sentences, indices in test_dataset.take(5):
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# print(f"Indices: {indices}, Sentences: {sentences}")
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if __name__ == "__main__":
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test()
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