Loading Text Dataset

Linux Ascend GPU CPU Data Preparation Beginner Intermediate Expert

Overview

The mindspore.dataset module provided by MindSpore enables users to customize their data fetching strategy from disk. At the same time, data processing and tokenization operators are applied to the data. Pipelined data processing produces a continuous flow of data to the training network, improving overall performance.

In addition, MindSpore supports data loading in distributed scenarios. Users can define the number of shards while loading. For more details, see Loading the Dataset in Data Parallel Mode.

This tutorial briefly demonstrates how to load and process text data using MindSpore.

Preparations

1. Prepare the following text data.

   Welcome to Beijing!
   北京欢迎您！
   我喜欢English!
   

2. Create the tokenizer.txt file, copy the text data to the file, and save the file under ./test directory. The directory structure is as follow.

   └─test
       └─tokenizer.txt
   

3. Import the mindspore.dataset and mindspore.dataset.text modules.

   import mindspore.dataset as ds
   import mindspore.dataset.text as text
   

Loading Dataset

MindSpore supports loading common datasets in the field of text processing that come in a variety of on-disk formats. Users can also implement custom dataset class to load customized data.

The following tutorial demonstrates loading datasets using the TextFileDataset in the mindspore.dataset module.

1. Configure the dataset directory as follows and create a dataset object.

   DATA_FILE = "./test/tokenizer.txt"
   dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
   

2. Create an iterator then obtain data through the iterator.

   for data in dataset.create_dict_iterator(output_numpy=True):
       print(text.to_str(data['text']))
   

   The output without tokenization:

   Welcome to Beijing!
   北京欢迎您！
   我喜欢English!
   

Processing Data

The following tutorial demonstrates how to perform data processing such as SlidingWindow and shuffle after a dataset is created.

• SlidingWindow

  The following tutorial demonstrates how to use the SlidingWindow to slice text data.

  1. Load the text dataset.

     inputs = [["大", "家", "早", "上", "好"]]
     dataset = ds.NumpySlicesDataset(inputs, column_names=["text"], shuffle=False)
     

  2. Print the results without any data processing.

     for data in dataset.create_dict_iterator(output_numpy=True):
             print(text.to_str(data['text']).tolist())
     

     The output is as follows:

     ['大', '家', '早', '上', '好']
     

  3. Perform the data processing operation.

     dataset = dataset.map(operations=text.SlidingWindow(2, 0), input_columns=["text"])
     

  4. Print the results after data processing.

     for data in dataset.create_dict_iterator(output_numpy=True):
             print(text.to_str(data['text']).tolist())
     

     The output is as follows:

     [['大', '家'],
      ['家', '早'],
      ['早', '上'],
      ['上', '好']]
     

• shuffle

  The following tutorial demonstrates how to shuffle text data while loading a dataset.

  1. Load and shuffle the text dataset.

     inputs = ["a", "b", "c", "d"]
     dataset = ds.NumpySlicesDataset(inputs, column_names=["text"], shuffle=True)
     

  2. Print the results after performing shuffle.

     for data in dataset.create_dict_iterator(output_numpy=True):
             print(text.to_str(data['text']).tolist())
     

     The output is as follows:

     c
     a
     d
     b
     

Tokenization

The following tutorial demonstrates how to use the WhitespaceTokenizer to tokenize words with space.

1. Create a tokenizer.

   tokenizer = text.WhitespaceTokenizer()
   

2. Apply the tokenizer.

   dataset = dataset.map(operations=tokenizer)
   

3. Create an iterator and obtain data through the iterator.

   for i in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
           token = text.to_str(i['text']).tolist()
           print(token)
   

   The output after tokenization is as follows:

   ['Welcome', 'to', 'Beijing!']
   ['北京欢迎您！']
   ['我喜欢English!']