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How to Build a Text Classification Model using BERT and Tensorflow

December 20, 2021

Text classification is a subset of machine learning that classifies text into predefined categories. Text classification is one of the important tasks in natural language processing (NLP).

Some examples of text classification are intent detection, sentiment analysis, topic labeling and spam detection.

In this tutorial, we will build a spam detection model. The spam detection model will classify emails as spam or not spam. This will be used to filter unwanted and unsolicited emails. We will build this model using BERT and Tensorflow.

BERT will be used to generate sentence encoding for all emails. Finally, we will use Tensorflow to build the neural networks. Tensorflow will create the input and output layers of our machine learning model.

Table of contents


For a reader to understand this tutorial, they should:

Importing important packages

Let’s import the required packages as follows:

import tensorflow as tf
import tensorflow_hub as hub
import tensorflow_text as text
import pandas as pd

We have imported the following packages:

  • tensorflow: It is the machine learning package used to build the neural network. It will create the input and output layers of our machine learning model.

  • tensorflow_hub: It contains a pre-trained machine model used to build our text classification. Our pre-trained model is BERT. We will re-use the BERT model and fine-tune it to meet our needs.

  • tensorflow_text: It will allow us to work with text. In this tutorial, we are solving a text-classification problem.

  • pandas: We will use Pandas to load our dataset. We also use Pandas for data manipulation and analysis. It gives us a clear overview of how our dataset is structured.

Now, let’s load and explore the dataset we will use in this tutorial. Before we load the dataset, make sure you download this dataset from here.

We need to run this command to load the dataset.

df = pd.read_csv("spam.csv")

Let’s see the structure of five data samples in our dataset.


The output is shown below:

Dataset structure

From the image above, our dataset has two categories: ham and spam. ham represents the emails that are not spam, this are emails from a trusted source. spam represents emails from an unknown source.

The dataset also has the Message column. This column represents the email messages. Let’s see the individual value count for the spam and ham emails.


The output is shown below:

Value count

From the image above, we have 4825 ham emails and 747 spam emails. The ham email has a significantly higher number.

The ratio of the two categories is shown below:


This result implies that about 15% are spam emails and 85% of ham emails. This indicates a class imbalance. We need to balance the two classes to reduce bias during model training.

Balancing dataset

We have various techniques that are used to balance the dataset. In this tutorial, we will use the most simple approach. We will reduce 4825 of the majority class to 747. This will make the two classes balanced.

Before we balance the two classes, let’s create data frames for the individual classes.

Dataframe for spam class

To create the data frame, run this code.

df_spam = df[df['Category']=='spam']

Dataframe for ‘ham’ class

To create this data frame, run this code.

df_ham = df[df['Category']=='ham']

Now that we have created the two data frames, we will reduce the number of the ham class to be equal to the spam class.

df_ham_downsampled = df_ham.sample(df_spam.shape[0])

We will save the new class into a df_ham_downsampled variable. We need to concatenate the two balanced classes into a single data frame.

df_balanced = pd.concat([df_ham_downsampled, df_spam])

The pd.concat method will concatenate df_ham_downsampled and df_spam into a single data frame. It will save the dataset into a variable df_balanced.

Let’s now check if the classes are balanced.


The output is shown below.

spam    747
ham     747
Name: Category, dtype: int64

The output shows the dataset with equal class values of 747. Thus, our dataset is now balanced.

Adding labels

We need to label our dataset into 1 and 0. 1 will represent the data samples that belong to the spam class. 0 will represent the data samples that belong to the ham class.

To label, the dataset runs this code.

df_balanced['spam']=df_balanced['Category'].apply(lambda x: 1 if x=='spam' else 0)

From the code above, we use lambda to write our logic. The apply method will run the written logic. This will enable us to label our dataset.

To see the output of five data samples, run this code:


The output is shown below.

Labeled dataset

From the image above, we can see that the dataset is labeled into two. Some of the data samples are labeled 1 while others are labeled 0. We now need to split our labeled dataset.

Splitting labeled dataset

We split our dataset into two sets, the first set will be used for training and the second set will be used for testing.

We will split our dataset using the train_test_split, which we import as follows:

from sklearn.model_selection import train_test_split

To split this dataset, use this code:

X_train, X_test, y_train, y_test = train_test_split(df_balanced['Message'],df_balanced['spam'], stratify=df_balanced['spam'])

In the code above, we use stratify to ensure equal distribution of classes in the train and test sample. This ensures we have an equal amount of spam and ham emails after splitting. After splitting the dataset, we can start working with BERT.

Getting started with BERT

BERT stands for Bidirectional Encoder Representations from Transformers. BERT models help machines understand and interpret the meaning of the text. It uses immediately preceding text to understand the context. It also checks the relationships of words within a sentence to give the actual meaning of words.

BERT will then convert a given sentence into an embedding vector. Embedding vector is used to represent the unique words in a given document. BERT ensures words with the same meaning will have a similar representation.

Machine learning does not work with text but works well with numbers. That’s why BERT converts the input text into embedding vectors. The embedding vectors are numbers with which the model can easily work.

The BERT process undergoes two stages: Preprocessing and encoding.


Preprocessing is the first stage in BERT. This stage involves removing noise from our dataset. In this stage, BERT will clean the dataset. It also removes duplicate records from the dataset.

It will also format the dataset so that it can be easy to use during model training. This will increase the model performance.


Because machine learning does not work well with the text, we need to convert the text into real numbers. This process is known as encoding. BERT will convert a given sentence into an embedding vector.

Let’s download the BERT model.

Downloading the BERT model

BERT models are usually pre-trained. They are available in TensorFlow Hub. TensorFlow Hub contains all the pre-trained machine learning models that are downloaded.

We will download two models, one to perform preprocessing and the other one for encoding. The links for the models are shown below.

bert_preprocess = hub.KerasLayer("")
bert_encoder = hub.KerasLayer("")

After downloading the model, let’s start building our model using TensorFlow.

Building model using TensorFlow

There are two types of models that you can build in TensorFlow. Sequential model and a functional model. In a sequential model, layers are built on top of each other, layer by layer. In a sequential model, we don’t have multiple inputs and outputs.

Functional models are more robust and flexible. They do not create layers in sequential order. In the functional model, we have multiple inputs and outputs. This tutorial will use the functional approach to build our model. We will start by initializing the BERT layers.

Initializing the BERT layers

text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='text')
preprocessed_text = bert_preprocess(text_input)
outputs = bert_encoder(preprocessed_text)

In the code above, we are creating an input layer using tf.keras.layers.Input method. We will use the preprocessed_text as input for this layer.

The bert_encoder function will then convert the preprocessed text into embedding vectors. This will be the output of this layer. The outputs will then be fed into the neural network layers.

Initializing the neural network layers

l = tf.keras.layers.Dropout(0.1, name="dropout")(outputs['pooled_output'])
l = tf.keras.layers.Dense(1, activation='sigmoid', name="output")(l)

The neural network has two layers, the Dropout layer, and the Dense layer.

‘Dropout’ layer

This layer will be used to prevent model overfitting. We will use 0.1% of the neurons to handle overfitting. Overfitting happens when a model perfectly learns from training data but performs poorly in testing. We also give it the name dropout.

Since we are using the functional approach to build the model, we add the input for this layer as a function using (outputs['pooled_output']). This input was the output of the BERT layers.

‘Dense’ layer

It only has one neuron. We also initialize the activation function as sigmoid. sigmoid is used when we have output values that between 0 and 1. In our case, when making predictions, the prediction probability will lie between 0 and 1. That’s why it is best suited.

We also name the layer as output because this is our output layer. Lets now add the input and output layers to construct the final model as shown below:

model = tf.keras.Model(inputs=[text_input], outputs = [l])

The model will use the text_input as inputs and will have only one single output. We will display the model summary so that we can see all the input and output layers used.


The model summary is shown below.

Model summary

The image above shows all the input and output layers we have initialized for our model. The output also shows the total params, trainable params, and non-trainable params.

  • Total params: It represents all the parameters in our model.

  • Trainable params: It represents the parameters that we will train.

  • Non-trainable params: These parameters are from the BERT model. They are already trained.

Let’s compile our model.

Model compiling

During this stage, we will set the optimizer, the loss function, and the metrics for our model as shown below.

  • The Optimizer is used to improve the model performance and reduce errors that occur during model training. We use the adam optimizer.

  • Metrics will be used to check the model performance so that we can know how we trained our model. We set the BinaryAccuracy(name='accuracy') which will be used to calculate the accuracy score of the model.

  • The Loss function is used to calculate the model error during the training phase. We use binary_crossentropy as our loss function because our output is binary. The output can either be a 0 or 1.

We now set these parameters.



After compiling the model, we can now fit it into our dataset.

Fitting the model

In this stage, the model learns from the training data samples. The model will identify patterns in the training dataset and gain knowledge., y_train, epochs=10)

We will specify the number of epochs as 10. The model will iterate through the dataset ten times and print the accuracy score after each iteration. This process is shown below.

Model training

After ten iterations, the model accuracy score is 0.9179 . This value represents 91.79%. Let’s use the model to make predictions.

Evaluating model using the testing dataset

To evaluate the model, we will use the model to classify the data samples in the testing dataset. They should be classified into either ham or spam.

Use the following code:

y_predicted = model.predict(X_test)
y_predicted = y_predicted.flatten()

The model.predict method will give the prediction results which are in a 2D array, but we want our results in a 1D array. To convert the result from the 2D to 1D array we use the y_predicted.flatten() function.

Since we used a sigmoid activation function, the prediction probabilities will lie between 0.0 to 1.0. So, if the prediction result is > 0.5 the output should be 1, and if it is < 0.5, the output should be 0.

We will use NumPy to help us create this logic.

import numpy as np

y_predicted = np.where(y_predicted > 0.5, 1, 0)

The result is shown below.

Model prediction

The image above shows our model has classified the data samples into either 0 or 1. We can now use this model, to make a single prediction using input texts.

Making predictions

We use the following texts to make predictions:

sample_dataset = [
 'You can win a lot of money, register in the link below,
 'You have an iPhone 10, spin the image below to claim your prize and it will be delivered in your door step',
 'You have an offer, the company will give you 50% off on every item purchased.',
 'Hey Bravin, don't be late for the meeting tomorrow will start lot exactly 10:30 am,
 "See you monday, we have alot to talk about the future of this company ."

The texts above show examples of email messages. We will use our model to classify these email messages as either spam or ham.

To make run the prediction, use this code:


The prediction results are shown below.

array([[0.8734353 ],
       [0.8960864 ],
       [0.13262196]], dtype=float32)

From the output above, the first three email messages have been classified as spam. They have a prediction probability that is greater than 0.5. The last two email messages have been classified as ham. They have a prediction probability that is less than 0.5. These are the right predictions and show we have successfully built our text classification model.

To get the Python code for this tutorial, click here


In this tutorial, we learned how to build a spam detection model. The model was able to classify email messages as spam or ham. We started by using BERT to convert a given sentence into an embedding vector. This was done using the pre-trained BERT models.

We created our model using TensorFlow and initialized all the input and output layers. We followed all the stages of building the neural network and finally came up with a spam detection model. Finally, we used the model to make predictions, the model was able to give accurate predictions.

Happy coding!


Peer Review Contributions by: Willies Ogola