NLP-Text Classification

Explore different methods

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Text classification is one of the important NLP tasks. I’ll not go into the details of what text classification is.

We’ll discuss how to create multiclass text classification models using different methods.

I’ve not shared much details about every method here because then it would be very exhaustive article. I’ve shared notebook links to refer.

Requirement

Create a text classification api which accepts a news article or a sentence from news article and classifies it into POSITIVE, NEGATIVE or NEUTRAL sentiment.

Dataset

Data is collected using my project news_api and annotated using doccano.

Model Building

Input vectors used : TF-IDF, word embeddings from distilbert, word embeddings from sentence transformer

Model techniques used : Custom ML using sklearn, Custom Neural Network using keras, Custom Neural Network using pytorch, Custom Neural Network using hugging face transformers trainer api

Using combinations of above I’ve created 7 different models:

Image by Author

notebooks:
1.Text Classification using TF-IDF and Pycaret
2.Text Classification using TF-IDF and custom Machine Learning
3.Text Classification using TF-IDF and custom Neural network using Keras
4.Text Classification using distilbert embeddings and custom Machine Learning
5.Text Classification using distilbert embeddings and Neural network using Pytorch
6.Text Classification using distilbert embeddings and Neural Network using huggging face trainer api
7.Text Classification using sentence transformer embeddings and custom neural network using Pytorch

Image by Author

Image by Author

I’ve created a complete end to end project for text classification model building to deployment. The project is production ready. You can refer it here.

The main challenges I’ve solved in this project:

1. Create model building code for different methods. Anyone can just pick any method to start with.
2. Create production ready code for text classification

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