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Natural Language Processing (NLP) is still all the buzz in the field of data science and machine learning, and JAX is an exciting library that’s gaining popularity for its performance and flexibility. Developed by Google Deepmind, it is a Python library designed for high-performance numerical computing that functions similar to Numpy.
we’ll explore the use of JAX for NLP tasks by building a text classification model from scratch. We’ll cover key steps like installing JAX with GPU support, data preprocessing, and implementing neural network layers. So, let’s dive into this journey of building a custom NLP model with JAX.
Before we start building our NLP model, we need to install JAX with GPU support. First, we should check which Python and CUDA versions are installed on our machine by running the following commands:
!nvcc --version
!python --version
The output will provide information about the Python version and CUDA version installed on your system. For example:
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Sun_Jul_28_19:07:16_PDT_2019
Cuda compilation tools, release 10.1, V10.1.243
Python 3.6.6 :: Anaconda, Inc.
In this example, we have Python 3.6 and CUDA 10.1 installed. With this information, we can proceed to install JAX with GPU support.
!pip install --upgrade "https://storage.googleapis.com/jax-releases/cuda101/jaxlib-0.1.39-cp36-none-linux_x86_64.whl"
!pip install --upgrade jax
With JAX successfully installed, we can now begin working on our NLP model.
We are going to build an NLP model based on “Word Embeddings.” Word embeddings are a family of NLP techniques that map words into a geometric space, capturing their semantic meaning. This is achieved by associating a numeric vector with each word in a dictionary, allowing us to measure the semantic relationship between words using metrics like cosine distance.
For our NLP task, we’ll use pre-trained word embeddings, specifically the Global Vectors for Word Representation (GloVe) embeddings. We’ll build our…