Fine-Tuning a BERT Model

Fine-Tuning a BERT Model

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This article is divided into two parts; they are: • Fine-tuning a BERT Model for GLUE Tasks • Fine-tuning a BERT Model for SQuAD Tasks GLUE is a benchmark for evaluating natural language understanding (NLU) tasks.

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The Machine Learning “Advent Calendar” Day 7: Decision Tree Classifier

The Machine Learning “Advent Calendar” Day 7: Decision Tree Classifier

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In Day 6, we saw how a Decision Tree Regressor finds its optimal split by minimizing the Mean Squared Error. Today, for Day 7 of the Machine Learning “Advent Calendar”, we switch to classification. With just one numerical feature and two classes, we explore how a Decision Tree Classifier decides where to cut the data, using impurity measures like Gini and Entropy. Even without doing the math, we can visually guess possible split points. But which one is […]

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Language Models: A 75-Year Journey That Didn’t Start With Transformers

Language Models: A 75-Year Journey That Didn’t Start With Transformers

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Introduction Language models have existed for decades — long before today’s so-called “LLMs.” In the 1990s, IBM’s alignment models and smoothed n-gram systems trained on hundreds of millions of words set performance records. By the 2000s, the internet’s growth enabled “web as corpus” datasets, pushing statistical models to dominate natural language processing (NLP). Yet, many believe language modelling began in 2017 with Google’s Transformer architecture and BERT. In reality, Transformers revolutionized scalability but were just one step in a much […]

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