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39 lines
1.3 KiB
39 lines
1.3 KiB
import torch
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from datasets import load_dataset
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from torch.utils.data import Dataset
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class NetflixDataset(Dataset):
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def __init__(self, tokenizer):
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super().__init__()
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self.tokenizer = tokenizer
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self.input_ids = []
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self.attn_masks = []
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self.labels = []
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self.txt_list = netflix_descriptions = load_dataset("hugginglearners/netflix-shows", split="train")[
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"description"
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]
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self.max_length = max([len(self.tokenizer.encode(description)) for description in netflix_descriptions])
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for txt in self.txt_list:
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encodings_dict = self.tokenizer(
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"</s>" + txt + "</s>", truncation=True, max_length=self.max_length, padding="max_length"
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)
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self.input_ids.append(torch.tensor(encodings_dict["input_ids"]))
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self.attn_masks.append(torch.tensor(encodings_dict["attention_mask"]))
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def __len__(self):
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return len(self.input_ids)
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def __getitem__(self, idx):
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return self.input_ids[idx], self.attn_masks[idx]
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def netflix_collator(data):
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return {
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"input_ids": torch.stack([x[0] for x in data]),
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"attention_mask": torch.stack([x[1] for x in data]),
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"labels": torch.stack([x[0] for x in data]),
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}
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