fannn1217/Results-of-Deep-Learning-in-NLP
SOTA results for machine learning problems in NLP .
This project helps machine learning practitioners in Natural Language Processing (NLP) compare the effectiveness of different deep learning models for common tasks like text classification and summarization. It compiles a list of state-of-the-art results, providing details on the academic paper, datasets used, performance metrics, and links to source code. Data scientists and machine learning engineers working on NLP problems would use this to inform their model selection.
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Use this if you need to quickly find the best-performing deep learning models for specific NLP tasks and want to see their benchmark results and associated research.
Not ideal if you are looking for a plug-and-play solution or an interactive tool to run experiments, as this is primarily a curated list of research findings.
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Nov 03, 2020
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