RobertCsordas/transformer_generalization
The official repository for our paper "The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers". We significantly improve the systematic generalization of transformer models on a variety of datasets using simple tricks and careful considerations.
This project helps machine learning researchers improve how well transformer models can apply learned rules to new, unseen examples. It takes in datasets designed to test systematic generalization (like mathematical problems or natural language queries) and outputs trained transformer models with enhanced generalization capabilities, along with performance plots and tables. Machine learning researchers, especially those working on natural language processing or logical reasoning, would find this useful for advancing model robustness.
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Use this if you are a machine learning researcher focused on improving the systematic generalization of transformer models, and you need to benchmark novel approaches against established datasets.
Not ideal if you are looking for a plug-and-play solution for applying pre-trained transformers to standard tasks like sentiment analysis or machine translation, as this is a research toolkit for model development.
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Language
Python
License
MIT
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Last pushed
Dec 16, 2022
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