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fine-tuning-bert

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collection of resources for learning about Artificial Intelligence (AI) and Large Language Models (LLMs). It includes fundamental topics, recommended notebooks, online courses, tools, datasets, and advanced techniques for developing and fine-tuning AI models.

  • Updated Mar 7, 2025

Successfully developed a fine-tuned BERT transformer model which can effectively perform emotion classification on any given piece of texts to identify a suitable human emotion based on semantic meaning of the text.

  • Updated Dec 13, 2022
  • Jupyter Notebook

A comprehensive guide for beginners looking to start fine-tuning BERT models for sentiment analysis on Arabic text. This project walks through the complete process of data preprocessing, model training, and evaluation, providing a beginner-friendly tutorial on how to fine-tune and deploy machine learning models for real-world applications.

  • Updated Dec 6, 2024
  • Jupyter Notebook

Successfully developed a resume classification model which can accurately classify the resume of any person into its corresponding job with a tremendously high accuracy of more than 99%.

  • Updated Dec 14, 2024
  • Jupyter Notebook

Successfully developed a text classification model to predict whether a given news text is fake or not by fine-tuning a pretrained BERT transformed model imported from Hugging Face.

  • Updated Dec 10, 2024
  • Jupyter Notebook

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