Categories: AI/ML Research

Fine-Tuning DistilBERT for Question Answering

This post is divided into three parts; they are: • Fine-tuning DistilBERT for Custom Q&A • Dataset and Preprocessing • Running the Training The simplest way to use a model in the transformers library is to create a pipeline, which hides many details about how to interact with it.

Advanced Q&A Features with DistilBERT

This post is divided into three parts; they are: • Using DistilBERT Model for Question Answering • Evaluating the Answer • Other Techniques for Improving the Q&A Capability BERT (Bidirectional Encoder Representations from Transformers) was trained to be a general-purpose language model that can understand text.

April 1, 2025

In "AI/ML Research"

How to Do Named Entity Recognition (NER) with a BERT Model

This post is in six parts; they are: • The Complexity of NER Systems • The Evolution of NER Technology • BERT's Revolutionary Approach to NER • Using DistilBERT with Hugging Face's Pipeline • Using DistilBERT Explicitly with AutoModelForTokenClassification • Best Practices for NER Implementation The challenge of Named Entity…

February 20, 2025

In "AI/ML Research"

A Gentle Introduction to Language Model Fine-tuning

This article is divided into four parts; they are: • The Reason for Fine-tuning a Model • Dataset for Fine-tuning • Fine-tuning Procedure • Other Fine-Tuning Techniques Once you train your decoder-only transformer model, you have a text generator.

January 7, 2026

In "AI/ML Research"

AI Generated Robotic Content