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data.py
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data.py
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import random
import pytorch_lightning as pl
from torch.utils.data.dataloader import DataLoader
from torchtext.data import Field, BucketIterator
from torchtext.data.field import LabelField
from torchtext.vocab import FastText, GloVe
from torchtext.datasets import IMDB
class IMDBDataModule(pl.LightningDataModule):
def __init__(self, batch_size):
super().__init__()
self.batch_size=batch_size
def prepare_data(self):
self.text_field = Field(sequential=True, fix_length=200, include_lengths=True)
self.label_field = LabelField()
train_val, test = IMDB.splits(self.text_field, self.label_field)
random.seed(42)
train, val = train_val.split(random_state=random.getstate())
self.text_field.build_vocab(train, vectors=GloVe())#vectors=FastText('simple'))
self.label_field.build_vocab(train)
self.train_iter, self.test_iter, self.val_iter = BucketIterator.splits(
(train, test, val),
batch_size=self.batch_size
)
self.train_iter.sort_within_batch = True
self.val_iter.sort_within_batch = True
def train_dataloader(self) -> DataLoader:
return self.train_iter
def test_dataloader(self) -> DataLoader:
return self.test_iter
def val_dataloader(self) -> DataLoader:
return self.val_iter