wtf is state-of-the-art-result-for-machine-learning-problems?

This repository is a reference table tracking the best-known results for a wide range of machine learning research problems. The idea is that when a researcher or developer wants to know how well the top methods perform on a particular task, they can look here instead of hunting through academic papers one by one. The tables cover tasks across several broad categories: supervised learning (which includes speech recognition, computer vision, and natural language processing), semi-supervised learning, unsupervised learning, transfer learning, and reinforcement learning. Within each category, the tables list the research paper that achieved a given result, the dataset it was tested on, the metric used to measure performance (such as accuracy, error rate, or BLEU score for translation), a link to source code if available, and the year the result was published. For example, the natural language processing section covers language modeling, machine translation, question answering, sentiment analysis, text classification, and natural language inference, among other tasks. Each of these has its own table showing which papers hold the top spots on which datasets and what scores they achieved. The repository does not contain any code to run. It is a curated document. The maintainer has noted that the last update was in February 2019, so the entries reflect the state of the field as of that date and have not been kept current since. Results in machine learning research move quickly, and benchmarks that were leading in 2019 have typically been surpassed many times over. The project was started as a community effort, with the maintainer encouraging others to submit new results via GitHub issues or a linked Google Form. Anyone new to GitHub could use the form. The README also notes the maintainer was looking for collaborators, particularly in the areas of natural language processing, computer vision, and reinforcement learning.