marcomoldovan/hierarchical-language-modeling
We address the task of learning contextualized word, sentence and document representations with a hierarchical language model by stacking Transformer-based encoders on a sentence level and subsequently on a document level and performing masked token prediction.
This project provides a clean template for deep learning practitioners or researchers to quickly set up and manage their machine learning experiments. It helps streamline the process of training models by handling common setup tasks, allowing users to focus on model development rather than boilerplate code. You input your prepared datasets and model architecture, and it outputs trained models and detailed logs of your experiments.
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Use this if you are a machine learning engineer or researcher looking for a structured way to kickstart new deep learning projects, manage model training workflows, and track experiments efficiently.
Not ideal if your primary focus is on building complex data engineering pipelines that involve interdependent data processing steps rather than model prototyping.
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Jupyter Notebook
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MIT
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Last pushed
Jul 25, 2023
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