How does PyTorch read a CSV dataset?

2 years ago
Olivia Parker
2 minutes

In PyTorch, you can utilize the torchtext library to read and process CSV datasets. Here is an example of reading a CSV dataset using torchtext.

Firstly, install the torchtext library.

pip install torchtext

Next, import the necessary modules.

import torch
from torchtext.data import Field, TabularDataset, BucketIterator

Define the attributes of the dataset.

text_field = Field(sequential=True, tokenize='spacy', lower=True)
label_field = Field(sequential=False, use_vocab=False)
fields = [('text', text_field), ('label', label_field)]

Read a CSV dataset and split it into a training set and a testing set.

train_data, test_data = TabularDataset.splits(
    path='path/to/dataset', train='train.csv', test='test.csv', format='csv',
    fields=fields, skip_header=True)

Build a vocabulary (converting text into numerical indexes).

text_field.build_vocab(train_data, min_freq=1)

Create an iterator to load data in batches.

batch_size = 32
train_iterator, test_iterator = BucketIterator.splits(
    (train_data, test_data), batch_size=batch_size, sort_key=lambda x: len(x.text),
    sort_within_batch=True)

Now, you can use the train_iterator and test_iterator to iterate through the data in the training and testing sets.

Note: In the above code, ‘path/to/dataset’ should be replaced with the actual path where the dataset is located. Additionally, you can also modify the field definitions and iterator parameters according to your specific requirements.

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