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Deep Learning with JAX and Flax

Preparing Text Dataset

Explore how to prepare text datasets for deep learning with JAX by loading data through TensorFlow. Learn to clean text by removing stop words, encode labels numerically, drop duplicates, and eliminate punctuation to get data ready for neural network training.

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JAX doesn’t ship with data-loading utilities. This keeps JAX focused on providing a fast tool for building and training machine learning models. Loading data in JAX is done using either TensorFlow or PyTorch. This lesson will focus on how to load datasets in JAX using TensorFlow.

Let’s dive in!

Loading text data in JAX

Let’s use the Worldcup tweets dataset to illustrate how to load text datasets with JAX. We import the standard data science packages and then view a sample of the data.

Python 3.8
import pandas as pd
# Read the csv file by giving the path to the extracted file
df = pd.read_csv("/usr/local/notebooks/worldcup-tweets.csv") 
print(df.head())

In the code above:

  • Line 1: We import the pandas library.

  • Line 4: We read the Worldcup tweets dataset using the read_csv() function from the pandas library, and store the DataFrame in df.

  • Line 5: Lastly, we call the df.head() method to print the first five rows of the dataset.

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