<jupyter_start><jupyter_text>Dataset + Dataloader<jupyter_code>import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
import pandas as pd
import sampler
import datasets
from torch.autograd  import  Function
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
train_dataset_file_path = '../yufeng_emotions/dataset/IEMOCAP/0/train.csv'
train_loader = datasets.get_dataloader(train_dataset_file_path, 'train')
test_dataset_file_path = '../yufeng_emotions/dataset/IEMOCAP/0/test.csv'
test_loader = datasets.get_dataloader(test_dataset_file_path, 'test')
train_loader.dataset[0]<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.08801213960546282, 'fraction_numerical': 0.004552352048558422, 'mean_word_length': 7.25, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '602597', 'license': 'MIT', 'n_tokens_mistral': 228, 'n_tokens_neox': 226, 'n_words': 46, 'path': 'nbs/dataloader.ipynb', 'repo_name': 'MartinLiu2/MuSE_Emotion_Analysis', 'size': '659'}