How to Build an Effective Comment Toxicity Ranking Model with Hugging Face’s Transformer Models | Written by Jacky Kaub | August 2023

How to Build an Effective Comment Toxicity Ranking Model with Hugging Face’s Transformer Models | Written by Jacky Kaub | August 2023

Introduction:

Welcome to the first part of “Catching up on NLP and LLM.” As a Data Scientist, I understand the importance of staying updated with the latest advancements in Natural Language Processing. That’s why I decided to embark on a journey to explore the new boom of Large Language Models (LLM) and dive deep into NLP through mini-projects.

During this journey, I realized that finding comprehensive content that guides readers through the process of understanding and implementing new NLP models was challenging. That’s why I decided to create this series of articles to bridge that gap.

In this first article, we will delve into the construction of a comment toxicity ranker using HuggingFace’s Transformer Models. Inspired by a Kaggle competition, our objective is to build a model that can determine the toxicity of comments.

Through this article, we will not only train our first NLP Classifier using Pytorch and HuggingFace transformers but also provide practical details and implementations that will be useful in future articles of this series.

Stay tuned for more exciting content that will enhance your NLP skills and take your data science game to the next level.

Full Article: How to Build an Effective Comment Toxicity Ranking Model with Hugging Face’s Transformer Models | Written by Jacky Kaub | August 2023

Catching up on NLP and LLM (Part I)
As a Data Scientist, I have never had the opportunity to properly explore the latest progress in Natural Language Processing. With the summer and the new boom of Large Language Models since the beginning of the year, I decided it was time to dive deep into the field and embark on some mini-projects. After all, there is never a better way to learn than by practicing.

Building a Comment Toxicity Ranker Using HuggingFace’s Transformer Models
In this first article, we are going to take a deep dive into building a comment toxicity ranker. This project is inspired by the “Jigsaw Rate Severity of Toxic Comments” competition which took place on Kaggle last year.

The objective of the competition was to build a model with the capacity to determine which comment (out of two comments given as input) is the most toxic.

To do so, the model will attribute to every comment passed as input a score, which determines its relative toxicity.

What this article will cover
In this article, we are going to train our first NLP Classifier using Pytorch and Hugging Face transformers. I will not go into the details of how transformers work, but more into practical details and implementations and initiate some concepts that will be useful for the next articles of the series.

In particular, we will see:
How to download a model from Hugging Face Hub
How to customize and use an Encoder
Build and train a Pytorch ranker from one of the Hugging Face models

This article is directly addressed to data scientists that would like to step up their game in NLP from a practical point of view. I will not do much hand-holding or explain every single line of code, but rather provide a roadmap for building a comment toxicity ranker using HuggingFace’s transformer models.

Getting Started: Downloading a Model from Hugging Face Hub
The first step in our project is to download a model from the Hugging Face Hub. The Hugging Face Hub is a repository of pre-trained transformer models that can be easily downloaded and used for various NLP tasks. We will utilize this resource to kickstart our comment toxicity ranker.

Customizing and Using an Encoder
Once we have downloaded our model, we will dive into customizing and using an Encoder. The Encoder is a crucial component in our comment toxicity ranker, as it takes in the input comments and converts them into vector representations that can be used for classification.

Building and Training a Pytorch Ranker
Finally, we will focus on building and training a Pytorch ranker using one of the Hugging Face models. This step will bring everything together and enable us to effectively rank the toxicity of comments.

Conclusion
In this first part of the series on catching up with NLP and LLM, we have explored the project of building a comment toxicity ranker. We have discussed the objective of the project and the steps we will take to achieve it. In the next articles, we will delve deeper into the technical implementation and explore more advanced concepts in NLP.

Stay tuned for the next article in this series, where we will dive into the code and start building our comment toxicity ranker using Hugging Face’s transformer models.

Summary: How to Build an Effective Comment Toxicity Ranking Model with Hugging Face’s Transformer Models | Written by Jacky Kaub | August 2023

In this article, the author discusses their journey to explore the latest progress in Natural Language Processing (NLP) and Large Language Models (LLM). They decide to start a series of articles that will take readers on a step-by-step journey towards a deep comprehension of new NLP models with concrete projects. The first article focuses on building a comment toxicity ranker using HuggingFace’s Transformer Models. The author provides practical details and implementations of training an NLP Classifier using PyTorch and Hugging Face transformers. This article is perfect for data scientists looking to enhance their NLP skills from a practical perspective.

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