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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 |