| 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 |
|
|
| |
| 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']) |
|
|
| |
| 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 |
