Initial loop
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.venv/
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.venv/
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__pycache__/
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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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#! /usr/bin/env python3
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import torch
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from torch.utils.data import Dataset, DataLoader, random_split
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from nlpaug.augmenter.char import OcrAug
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from nlpaug.augmenter.word import RandomWordAug
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from sklearn.model_selection import train_test_split
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import pandas as pd
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class TextDataset(Dataset):
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def __init__(self, path, max_length, buffer_size):
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self.data = pd.read_csv(path, delimiter="\t", header=None)
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self.max_length = max_length
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self.buffer_size = buffer_size
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# Augmentations
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self.aug_char = OcrAug(
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name="OCR_Aug",
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aug_char_min=2,
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aug_char_max=10,
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aug_char_p=0.3,
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aug_word_p=0.3,
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aug_word_min=1,
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aug_word_max=10,
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)
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self.aug_delete = RandomWordAug(
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action="delete", name="RandomWord_Aug", aug_min=0, aug_max=1, aug_p=0.1
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)
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def __len__(self):
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return len(self.data)
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def __getitem__(self, idx):
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index, sentence = self.data.iloc[idx]
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aug_sentence = self.aug_char.augment(sentence)
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aug_sentence = self.aug_delete.augment(aug_sentence)
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aug_sentence = aug_sentence[0]
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return sentence, aug_sentence
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def load_dataset(path, max_length, buffer_size, batch_size, test_ratio=0.2):
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# Create dataset
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dataset = TextDataset(path, max_length, buffer_size)
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# Calculate split sizes
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total_size = len(dataset)
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test_size = int(total_size * test_ratio)
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train_size = total_size - test_size
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# Split dataset into train and test
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train_dataset, test_dataset = random_split(dataset, [train_size, test_size])
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# Create dataloaders
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train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
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test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)
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return train_dataloader, test_dataloader
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def test():
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train_dataloader, test_dataloader = load_dataset(
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"../datasets/deu_mixed-typical_2011_1M/deu_mixed-typical_2011_1M-sentences.txt",
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100,
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100,
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32,
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test_ratio=0.2,
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)
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for batch in train_dataloader:
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for sentence, aug_sentence in zip(batch[0], batch[1]):
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print(f"sentence: {sentence} | aug_sentence: {aug_sentence}")
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if __name__ == "__main__":
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test()
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87
ft.py
87
ft.py
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#! /usr/bin/env python3
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#! /usr/bin/env python3
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from transformers import BartForConditionalGeneration, BartTokenizer, AdamW
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import torch
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from dl import load_dataset
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from tqdm import tqdm
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# Enable cudnn optimizations
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torch.backends.cudnn.benchmark = True
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# load tokenizer and model
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tokenizer = BartTokenizer.from_pretrained('facebook/bart-base')
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model = BartForConditionalGeneration.from_pretrained('facebook/bart-base')
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model.to(device)
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# set up optimizer
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optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5)
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# Initialize Amp. This should be optional and should not affect computation if not available
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try:
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from torch.cuda.amp import GradScaler, autocast
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scaler = GradScaler()
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except ImportError:
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# If Amp is not available, we'll simply define a dummy context manager
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class autocast:
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def __enter__(self):
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pass
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def __exit__(self, *args):
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pass
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scaler = None # We won't use a scaler if we don't have Amp
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def train_model(dataloader):
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model.train()
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total_loss = 0
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print("Training model...")
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for batch in tqdm(dataloader):
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optimizer.zero_grad()
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inputs = tokenizer(batch[1], return_tensors="pt", padding=True, truncation=True, max_length=512)
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inputs.to(device)
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labels = tokenizer(batch[0], return_tensors="pt", padding=True, truncation=True, max_length=512)
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labels.to(device)
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outputs = model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], labels=labels["input_ids"])
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loss = outputs.loss
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loss.backward()
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optimizer.step()
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total_loss += loss.item()
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avg_train_loss = total_loss / len(dataloader)
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return avg_train_loss
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def test_model(dataloader):
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model.eval()
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total_loss = 0
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print("Testing model...")
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for batch in tqdm(dataloader):
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with torch.no_grad():
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inputs = tokenizer(batch[1], return_tensors="pt", padding=True, truncation=True, max_length=512)
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inputs.to(device)
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labels = tokenizer(batch[0], return_tensors="pt", padding=True, truncation=True, max_length=512)
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labels.to(device)
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outputs = model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], labels=labels["input_ids"])
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loss = outputs.loss
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total_loss += loss.item()
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avg_test_loss = total_loss / len(dataloader)
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return avg_test_loss
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def train():
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train_dataloader, test_dataloader = load_dataset(
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"../datasets/deu_mixed-typical_2011_1M/deu_mixed-typical_2011_1M-sentences.txt",
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100, 100, 1, test_ratio=0.2
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)
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num_epochs = 3
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for epoch in range(num_epochs):
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avg_train_loss = train_model(train_dataloader)
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print(f"Train loss for epoch {epoch+1}: {avg_train_loss}")
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avg_test_loss = test_model(test_dataloader)
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print(f"Test loss for epoch {epoch+1}: {avg_test_loss}")
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if __name__ == "__main__":
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train()
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