princeton-nlp/DataMUX
[NeurIPS 2022] DataMUX: Data Multiplexing for Neural Networks
This project helps machine learning engineers or researchers working with neural networks to efficiently train models by combining multiple data samples into a single input. It takes various text-based tasks, like sentence classification or named entity recognition, as input, and outputs trained models that perform these tasks with improved efficiency. The end-user is a machine learning practitioner looking to optimize neural network training.
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Use this if you are a machine learning engineer or researcher looking to improve the efficiency of neural network training for natural language processing tasks.
Not ideal if you are not familiar with PyTorch or training neural networks, or if your primary focus is on non-NLP machine learning domains.
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Last pushed
Nov 24, 2022
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