In the world of natural language processing, one of the most important tasks is binary text classification. Binary text classification is the process of classifying text into two distinct classes. For example, a binary classifier could classify an email into either spam or not.
A new deep learning tool called BERT (Bidirectional Encoder Representations from Transformers) has emerged as a powerful tool for binary text classification. BERT is a transformer-based language model that has achieved state-of-the-art performance on many natural language processing tasks. It is based on the Transformer architecture and uses a bidirectional approach to better capture the context of words in a sentence.
Using BERT for binary text classification has many advantages. For one, it allows for the accurate and efficient classification of texts. BERT can learn to classify a text in a short amount of time with high accuracy. Additionally, it can be used to classify texts of different lengths, making it a useful tool for natural language processing tasks such as sentiment analysis, question answering, and machine translation.
In the following GitHub repository, you can find the Python code for building a binary BERT classifier. You can only change the file name to make it work for your own dataset. There are 3 general steps to follow:
STEP 1: Loading and Preprocessing the Dataset
STEP 2: Creating the BERT Model and Wrapping in Learner Object
STEP 3: Training the BERT Model
The GitHub repository for building the binary BERT classifier.
In case you are looking for a 3 classes classification using BERT, you may refer to our previous blog post, the use of BERT in solving classification problems.