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<title>BERT from R</title>
<meta property="description" itemprop="description" content="A deep learning model - BERT from Google AI Research has given state-of-the-art results in a wide variety of NLP tasks. In this tutorial, we will show how to load and train the BERT model with Keras within R."/>
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<div class="d-title">
<h1>BERT from R</h1>
<p><p>A deep learning model - BERT from Google AI Research has given state-of-the-art results in a wide variety of NLP tasks. In this tutorial, we will show how to load and train the BERT model with Keras within R.</p></p>
</div>
<div class="d-byline">
Turgut Abdullayev <a href="https://github.com/henry090" class="uri">https://github.com/henry090</a> (AccessBank Azerbaijan)<a href="https://www.accessbank.az/en/" class="uri">https://www.accessbank.az/en/</a>
<br/>2019-09-30
</div>
<div class="d-article">
<p><a href="https://blogs.rstudio.com/tensorflow/posts/2019-08-29-using-tf-from-r/">In the previous post, Sigrid Keydana</a> explained the logic behind the <a href="https://rstudio.github.io/reticulate/">reticulate package</a> and its efficiency for interoperability between Python and R. So, this time we will build a classification model with <a href="https://github.com/google-research/bert">BERT</a> taking into account one of the powerful capabilities of the reticulate package – calling Python from R via importing Python modules.</p>
<p>Before we start, make sure that Python version is 3 because Python 2 can bring lots of difficulties while working with BERT model such as Unicode issues related to the text.</p>
<div class="layout-chunk" data-layout="l-body">
<pre class="r"><code>
# to use a preferred version of python, we nee python 3
reticulate::use_python('C:/Users/turgut.abdullayev/AppData/Local/Continuum/anaconda3/python.exe',
required=T)
# to see python version
reticulate::py_discover_config()</code></pre>
</div>
<div class="layout-chunk" data-layout="l-body">
<pre class="r"><code>
python: C:/Users/turgut.abdullayev/AppData/Local/Continuum/anaconda3/python.exe
libpython: C:/Users/turgut.abdullayev/AppData/Local/Continuum/anaconda3/python37.dll
pythonhome: C:\Users\TURGUT~1.ABD\AppData\Local\CONTIN~1\ANACON~1
version: 3.7.3 (default, Mar 27 2019, 17:13:21) [MSC v.1915 64 bit (AMD64)]
Architecture: 64bit
numpy: C:\Users\TURGUT~1.ABD\AppData\Local\CONTIN~1\ANACON~1\lib\site-packages\numpy
numpy_version: 1.16.4
NOTE: Python version was forced by use_python function</code></pre>
</div>
<p>However, a convenient way of importing BERT model with Keras was done by Zhao HG which is called <a href="https://github.com/CyberZHG/keras-bert">Keras-bert</a>. This means that importing the same python library with reticulate can allow us to build a popular state-of-the-art model within R.</p>
<p>There are several methods to install keras-bert in Python.</p>
<ul>
<li>in Jupyter Notebook, run:</li>
</ul>
<div class="layout-chunk" data-layout="l-body">
<pre class="r"><code>
!pip install keras-bert</code></pre>
</div>
<ul>
<li>in Terminal (Linux, Mac OS), run:</li>
</ul>