mit-han-lab/neurips-micronet
[JMLR'20] NeurIPS 2019 MicroNet Challenge Efficient Language Modeling, Champion
This project provides an efficient approach to language modeling. It takes large text datasets, such as Wikipedia articles, and outputs a highly optimized language model capable of predicting the next word in a sequence. This is ideal for researchers and practitioners focused on natural language processing who need to deploy language models with minimal computational resources.
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Use this if you need to build or evaluate a compact and efficient language model for text generation or prediction, especially when resource constraints (like memory or processing power) are a major concern.
Not ideal if you're looking for a simple, off-the-shelf language model without needing to understand or optimize its internal architecture for efficiency.
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Jupyter Notebook
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MIT
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Last pushed
Feb 26, 2021
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