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This project involves analyzing and classifying the BoolQ dataset from the SuperGLUE benchmark. We implemented various classifiers and techniques, including rules-based logic, BERT, RNN, and GPT-3/4 data augmentation, achieving performance improvements.
Corpus2GPT: A project enabling users to train their own GPT models on diverse datasets, including local languages and various corpus types, using Keras and compatible with TensorFlow, PyTorch, or JAX backends for subsequent storage or sharing.