Category: AI/ML Research

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In machine learning, probability distributions play a fundamental role for various reasons: modeling uncertainty of information and data, applying optimization processes with stochastic settings, and performing inference processes, to name a few.

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 Recognition extends far beyond simple …

Read more “How to Do Named Entity Recognition (NER) with a BERT Model”

Be sure to check out the previous articles in this series: •

Combining the power of

You know it as well as I do: people are relying more and more on generative AI and large language models (LLM) for quick and easy information acquisition.

Large language models (LLMs) have evolved and permeated our lives so much and so quickly that many we have become dependent on them in all sorts of scenarios.

Before we start, let’s ensure you are in the right place.

Creating custom layers and loss functions in

Machine learning (ML) is considered the largest subarea of artificial intelligence (AI) , studying the development of software systems that learn from data by themselves to perform a task, without being explicitly programmed with the instructions to address it.