Dive into this detailed tutorial where we demonstrate the process of fine-tuning both the 125 million and 2.7 billion parameter versions of the GPT-Neo model for text classification. Using a large dataset of student questions, the tutorial provides step-by-step instructions on preparing the dataset, setting up the model, and evaluating it. Discover the effectiveness of these AI models in dealing with real-world text classification tasks and understand the potential implications of overfitting. Perfect for anyone interested in machine learning and AI model optimization.
This video references a related video on transfer learning at 6:55. Much of this tutorial is identical, including the preprocessing of the dataset and use of a test set. This transfer video timestamp showing the creation of the test set is here: • Transfer Learning with...
Download the Python utility we used in this video: github.com/Clarifai/examples/...
Link to Dataset on Kaggle: www.kaggle.com/datasets/mruty...
Link to shortened 5000 sample dataset used in video: s3.amazonaws.com/samples.clar...
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#MachineLearning #NLP #TextClassification #CohereModel #TransferLearning #Tutorial
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