Push

.gitignore

@@ -0,0 +1 @@
+.venv/

dataloader.py

@@ -0,0 +1,78 @@
+import tensorflow as tf
+import nlpaug.augmenter.word as naw
+
+
+class DataLoader:
+    def __init__(self, path, buffer_size, batch_size, max_length, test_ratio=0.2):
+        self.path = path
+        self.buffer_size = buffer_size
+        self.batch_size = batch_size
+        self.max_length = max_length
+        self.test_ratio = test_ratio
+        self.aug = naw.SynonymAug(aug_src="wordnet")
+
+    def _split_input_target(self, sequence):
+        parts = tf.strings.split(sequence, "\t")
+        index = int(parts[0])
+        sentence = tf.strings.reduce_join(parts[1:], separator=" ")
+        return sentence, index
+
+    def augment_data(self, sentence, index):
+        aug_sentence = self.aug.augment(sentence.numpy().decode())
+        return sentence, aug_sentence, index
+
+    def tf_augment_data(self, sentence, index):
+        sentence, aug_sentence, index = tf.py_function(
+            self.augment_data, [sentence, index], [tf.string, tf.string, tf.int32]
+        )
+        return sentence, aug_sentence, index
+
+    def load_dataset(self):
+        lines_dataset = tf.data.TextLineDataset(self.path)
+        dataset = lines_dataset.map(self._split_input_target)
+        dataset = dataset.map(self.tf_augment_data)
+
+        # Split dataset into train and test
+        dataset_size = tf.data.experimental.cardinality(dataset).numpy()
+        test_size = int(dataset_size * self.test_ratio)
+        train_size = dataset_size - test_size
+        train_dataset = dataset.take(train_size)
+        test_dataset = dataset.skip(train_size)
+
+        # Shuffle and batch
+        train_dataset = train_dataset.shuffle(self.buffer_size).batch(self.batch_size)
+        test_dataset = test_dataset.shuffle(self.buffer_size).batch(self.batch_size)
+
+        return train_dataset, test_dataset
+
+
+def test():
+    # Hyperparameters
+    buffer_size = 10000
+    batch_size = 64
+    max_length = 100  # Or any other value depending on your data
+
+    # Create DataLoader
+    data_loader = DataLoader(
+        "../datasets/deu_mixed-typical_2011_1M/deu_mixed-typical_2011_1M-sentences.txt",
+        buffer_size,
+        batch_size,
+        max_length,
+    )
+
+    # Load the datasets
+    train_dataset, test_dataset = data_loader.load_dataset()
+
+    # Test the data loader on the training dataset
+    print("First 5 batches from the training dataset:")
+    for sent, aug, indxs in train_dataset.take(1):
+        print(f"Indices: {indxs}, Sentences: {sent}, Augmented: {aug}")
+
+    # Test the data loader on the test dataset
+    # print("\nFirst 5 batches from the test dataset:")
+    # for sentences, indices in test_dataset.take(5):
+    #    print(f"Indices: {indices}, Sentences: {sentences}")
+
+
+if __name__ == "__main__":
+    test()

ft.py

@@ -0,0 +1 @@
+#! /usr/bin/env python3