How to iterate through the dataset when training a model To activate this function you simply add the parameter collate_fn=Your_Function_name when initialising the DataLoader object. The function will now return processed text data ready for training. The word tensors are then concatenated and the list of class tensors, in this case 1, are combined into a single tensor. The batch is then unpacked and then we add the word and label tensors to lists. In practice, these could be word vectors passed in through another function. def collate_batch(batch): word_tensor = torch.tensor(,, ]) label_tensor = torch.tensor(]) text_list, classes =, for (_text, _class) in batch: text_list.append(word_tensor) classes.append(label_tensor) text = torch.cat(text_list) classes = torch.tensor(classes) return text, classes DL_DS = DataLoader(TD, batch_size=2, collate_fn=collate_batch)Īs an example, two tensors are created to represent the word and class. This parameter allows you to create separate data processing functions and will apply the processing within that function to the data before it is output. DataLoader has a handy parameter called collate_fn. In machine learning or deep learning text needs to be cleaned and turned in to vectors prior to training. Create a custom Dataset class class CustomTextDataset(Dataset): def _init_(self, txt, labels): self.labels = labels self.text = text def _len_(self): return len(self.labels) def _getitem_(self, idx): label = self.labels text = self.text sample = ] How to pre-process your data using ‘ collate_fn’ imports the required functions we need to create and use Dataset and DataLoader. However, it’s a powerful tool for managing data so i’m going to use it. Pandas is not essential to create a Dataset object. Import libraries import pandas as pd import torch from import Dataset, DataLoader a Dataset stores all your data, and Dataloader is can be used to iterate through the data, manage batches, transform the data, and much more. Creating a PyTorch Dataset and managing it with Dataloader keeps your data manageable and helps to simplify your machine learning pipeline.
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