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0b416c6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from transformers import BertTokenizerFast
import random
# --- DATASET ---
class TextDataset(Dataset):
def __init__(self, texts, tokenizer, max_len=32):
'''
Args:
texts: List of stories
tokenizer: Tokenizer
max_len: Maximum length of the story # THIS IS JUST AN EXAMPLE
'''
self.encodings = tokenizer(
texts,
padding="max_length",
truncation=True,
max_length=max_len,
return_tensors="pt"
)
def __len__(self):
return len(self.encodings["input_ids"])
def __getitem__(self, idx):
return {
"input_ids": self.encodings["input_ids"][idx],
"token_type_ids": self.encodings["token_type_ids"][idx],
"attention_mask": self.encodings["attention_mask"][idx].unsqueeze(0).unsqueeze(0)
}
# --- MAIN EXECUTION ---
if __name__ == "__main__":
# Load tokenizer
tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased") # or your trained tokenizer path
# Example dataset
texts = [
"The quick brown fox jumps over the lazy dog.",
"Transformers are powerful models for NLP tasks.",
"Masked language modeling trains BERT to understand context.",
"Pretraining is followed by task-specific fine-tuning."
]
dataset = TextDataset(texts, tokenizer, max_len=32)
dataloader = DataLoader(dataset, batch_size=2, shuffle=True)
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