Upload data.py

data.py

@@ -0,0 +1,69 @@
+import torch
+from datasets import load_dataset, Dataset
+from transformers import BertTokenizerFast
+import pandas as pd
+from imblearn.under_sampling import RandomUnderSampler
+import logging
+import os
+
+
+def balance_data(dataset):
+    df = dataset.to_pandas()
+    
+    logging.info(f"Balancing {df['label'].value_counts()}")
+
+    rus = RandomUnderSampler(random_state=42, replacement=True)
+
+    X_resampled, y_resampled = rus.fit_resample(
+        df['text'].to_numpy().reshape(-1, 1), df['label'].to_numpy())
+    
+    df = pd.DataFrame(
+        {'text': X_resampled.flatten(), 'label': y_resampled})
+    
+    logging.info(f"After balancing: {df['label'].value_counts()}")
+
+    return Dataset.from_pandas(df)
+
+
+def tokenize(dataset):
+    tokenizer = BertTokenizerFast.from_pretrained("neuralmind/bert-large-portuguese-cased")
+
+    dataset = dataset.map(lambda example: tokenizer(
+        example["text"], truncation=True, padding="max_length", max_length=512))
+    
+    return dataset
+
+# This function supports the Notebook version of LID. No usage elsewhere.
+def tokenize_single_document(text):
+    tokenizer = BertTokenizerFast.from_pretrained("neuralmind/bert-large-portuguese-cased")
+
+    return tokenizer(text, truncation=True, padding="max_length", max_length=512)
+
+def load_dataloader(domain):
+
+    logging.info(f"Loading {domain} dataset...")
+    
+    if domain == 'dslcc':
+        dataset = load_dataset("arubenruben/portuguese_dslcc")
+    else:
+        dataset = load_dataset("Random-Mary-Smith/port_data_random", domain)
+    
+    DEBUG = (os.getenv('DEBUG', 'False') == 'True')
+
+    dataset['train'] = balance_data(dataset['train'])
+
+    dataset['test'] = dataset['test'].select(range(min(len(dataset['test']), 10_000)))
+    
+    for split in ['train', 'test']:    
+        if DEBUG:
+            logging.info("DEBUG MODE: Loading only 100 samples")
+            dataset[split] = dataset[split].select(range(min(len(dataset[split]), 50)))
+
+    dataset = tokenize(dataset)
+    dataset.set_format(type='torch', columns=['input_ids', 'attention_mask', 'label'])
+
+    # Create Dataloaders
+    train_dataloader = torch.utils.data.DataLoader(dataset['train'], batch_size=int(os.getenv('BATCH_SIZE')), shuffle=True)
+    test_dataloader = torch.utils.data.DataLoader(dataset['test'], batch_size=int(os.getenv('BATCH_SIZE')), shuffle=False)
+
+    return train_dataloader, test_dataloader